From 93e876a3bed0cff640ba922f36786957ed68ef6e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 9 May 2024 04:39:46 -0400
Subject: [PATCH 001/315] Remove warnings that confuse people.

---
 comfy/sd.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index ceb080b3d..9671e4aee 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -486,7 +486,11 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
                 clip = CLIP(clip_target, embedding_directory=embedding_directory)
                 m, u = clip.load_sd(clip_sd, full_model=True)
                 if len(m) > 0:
-                    logging.warning("clip missing: {}".format(m))
+                    m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
+                    if len(m_filter) > 0:
+                        logging.warning("clip missing: {}".format(m))
+                    else:
+                        logging.debug("clip missing: {}".format(m))
 
                 if len(u) > 0:
                     logging.debug("clip unexpected {}:".format(u))

From 0fecfd2b1a2794b77277c7e256c84de54a63d860 Mon Sep 17 00:00:00 2001
From: shawnington <88048838+shawnington@users.noreply.github.com>
Date: Thu, 9 May 2024 02:38:00 -0700
Subject: [PATCH 002/315] Added generic wrapper function node_helpers.pillow to
 fix PIL issues #4472 and #2445 (#3422)

* Update node_helpers.py to use generic pillow wrapper to resolve multiple meta-data related issues.

replaced open_image function with a generic pillow function that takes Pil functions as a dependency injection and applies the ImageFile.LOAD_TRUNCATED_IMAGES try except fix to them.

This provides an extensible function to handle related errors that can wrap offending functions when discovered without the need to repeat code.

* Update a few Pil functions to use node_helpers.pillow wrapper

Update a Pil function calls in a few locations to use the generic node_helpers.pillow wrapper that takes the function as a dependency injection and uses the try except method with ImageFIle.LOAD_TRUNCATED_IMAGES solution

* Corrected comment in issue #s fixed.

* Update node_helpers.py to remove import of Image from PIL

import of Image is no longer required as functions are Injected
---
 node_helpers.py | 13 ++++++-------
 nodes.py        | 17 ++++-------------
 2 files changed, 10 insertions(+), 20 deletions(-)

diff --git a/node_helpers.py b/node_helpers.py
index 60f8fa415..43b9e829f 100644
--- a/node_helpers.py
+++ b/node_helpers.py
@@ -1,4 +1,4 @@
-from PIL import Image, ImageFile, UnidentifiedImageError
+from PIL import ImageFile, UnidentifiedImageError
 
 def conditioning_set_values(conditioning, values={}):
     c = []
@@ -10,16 +10,15 @@ def conditioning_set_values(conditioning, values={}):
 
     return c
 
-def open_image(path):
+def pillow(fn, arg):
     prev_value = None
-
     try:
-        img = Image.open(path)
-    except (UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445
+        x = fn(arg)
+    except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416
         prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
         ImageFile.LOAD_TRUNCATED_IMAGES = True
-        img = Image.open(path)
+        x = fn(arg)
     finally:
         if prev_value is not None:
             ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
-        return img
+        return x
diff --git a/nodes.py b/nodes.py
index 4d3171b8a..488afd577 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1457,21 +1457,12 @@ class LoadImage:
     def load_image(self, image):
         image_path = folder_paths.get_annotated_filepath(image)
         
-        img = node_helpers.open_image(image_path)
+        img = node_helpers.pillow(Image.open, image_path)
         
         output_images = []
         output_masks = []
         for i in ImageSequence.Iterator(img):
-            prev_value = None
-            try:
-                i = ImageOps.exif_transpose(i)
-            except OSError:
-                prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
-                ImageFile.LOAD_TRUNCATED_IMAGES = True
-                i = ImageOps.exif_transpose(i)
-            finally:
-                if prev_value is not None:
-                    ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
             if i.mode == 'I':
                 i = i.point(lambda i: i * (1 / 255))
@@ -1527,8 +1518,8 @@ class LoadImageMask:
     FUNCTION = "load_image"
     def load_image(self, image, channel):
         image_path = folder_paths.get_annotated_filepath(image)
-        i = Image.open(image_path)
-        i = ImageOps.exif_transpose(i)
+        i = node_helpers.pillow(Image.open, image_path)
+        i = node_helpers.pillow(ImageOps.exif_transpose, i)
         if i.getbands() != ("R", "G", "B", "A"):
             if i.mode == 'I':
                 i = i.point(lambda i: i * (1 / 255))

From f374ea714d8c32c2f214e818146b06754c963028 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Fri, 10 May 2024 22:07:46 +0100
Subject: [PATCH 003/315] Setting for saving and restoring canvas position and
 zoom level (#3437)

---
 web/scripts/app.js | 35 +++++++++++++++++++++++++++++++++++
 1 file changed, 35 insertions(+)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index a3105c275..a0fe8b290 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -262,6 +262,36 @@ export class ComfyApp {
 			})
 		);
 	}
+	
+	#addRestoreWorkflowView() {
+		const serialize = LGraph.prototype.serialize;
+		const self = this;
+		LGraph.prototype.serialize = function() {
+			const workflow = serialize.apply(this, arguments);
+
+			// Store the drag & scale info in the serialized workflow if the setting is enabled
+			if (self.enableWorkflowViewRestore.value) {
+				if (!workflow.extra) {
+					workflow.extra = {};
+				}
+				workflow.extra.ds = {
+					scale: self.canvas.ds.scale,
+					offset: self.canvas.ds.offset,
+				};
+			} else if (workflow.extra?.ds) {
+				// Clear any old view data
+				delete workflow.extra.ds;
+			}
+
+			return workflow;
+		}
+		this.enableWorkflowViewRestore = this.ui.settings.addSetting({
+			id: "Comfy.EnableWorkflowViewRestore",
+			name: "Save and restore canvas position and zoom level in workflows",
+			type: "boolean",
+			defaultValue: true
+		});
+	}
 
 	/**
 	 * Adds special context menu handling for nodes
@@ -1505,6 +1535,7 @@ export class ComfyApp {
 		this.#addProcessKeyHandler();
 		this.#addConfigureHandler();
 		this.#addApiUpdateHandlers();
+		this.#addRestoreWorkflowView();
 
 		this.graph = new LGraph();
 
@@ -1805,6 +1836,10 @@ export class ComfyApp {
 
 		try {
 			this.graph.configure(graphData);
+			if (this.enableWorkflowViewRestore.value && graphData.extra?.ds) {
+				this.canvas.ds.offset = graphData.extra.ds.offset;
+				this.canvas.ds.scale = graphData.extra.ds.scale;
+			}
 		} catch (error) {
 			let errorHint = [];
 			// Try extracting filename to see if it was caused by an extension script

From 4f63ee99f185db316a017520570310d094efffba Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 10 May 2024 17:30:52 -0400
Subject: [PATCH 004/315] Add a button to reset the view.

---
 web/scripts/app.js | 6 ++++++
 web/scripts/ui.js  | 7 +++++++
 2 files changed, 13 insertions(+)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index a0fe8b290..7ed262cb2 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -2313,6 +2313,12 @@ export class ComfyApp {
 		await this.#invokeExtensionsAsync("refreshComboInNodes", defs);
 	}
 
+	resetView() {
+		app.canvas.ds.scale = 1;
+		app.canvas.ds.offset = [0, 0]
+		app.graph.setDirtyCanvas(true, true);
+	}
+
 	/**
 	 * Clean current state
 	 */
diff --git a/web/scripts/ui.js b/web/scripts/ui.js
index d0fa46efb..36fed3238 100644
--- a/web/scripts/ui.js
+++ b/web/scripts/ui.js
@@ -597,16 +597,23 @@ export class ComfyUI {
 					if (!confirmClear.value || confirm("Clear workflow?")) {
 						app.clean();
 						app.graph.clear();
+						app.resetView();
 					}
 				}
 			}),
 			$el("button", {
 				id: "comfy-load-default-button", textContent: "Load Default", onclick: async () => {
 					if (!confirmClear.value || confirm("Load default workflow?")) {
+						app.resetView();
 						await app.loadGraphData()
 					}
 				}
 			}),
+			$el("button", {
+				id: "comfy-reset-view-button", textContent: "Reset View", onclick: async () => {
+					app.resetView();
+				}
+			}),
 		]);
 
 		const devMode = this.settings.addSetting({

From e1489ad2576651a1384e9b82fd1991ac2f8764e0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 11 May 2024 21:46:05 -0400
Subject: [PATCH 005/315] Fix issue with lowvram mode breaking model saving.

---
 comfy/model_management.py           |  8 ++++----
 comfy/model_patcher.py              | 12 +++++++++---
 comfy/sd.py                         |  2 +-
 comfy_extras/nodes_model_merging.py |  2 +-
 4 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 913b6844f..15dd73a6b 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -285,7 +285,7 @@ class LoadedModel:
         else:
             return self.model_memory()
 
-    def model_load(self, lowvram_model_memory=0):
+    def model_load(self, lowvram_model_memory=0, force_patch_weights=False):
         patch_model_to = self.device
 
         self.model.model_patches_to(self.device)
@@ -295,7 +295,7 @@ class LoadedModel:
 
         try:
             if lowvram_model_memory > 0 and load_weights:
-                self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory)
+                self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights)
             else:
                 self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights)
         except Exception as e:
@@ -379,7 +379,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
             if mem_free_torch > mem_free_total * 0.25:
                 soft_empty_cache()
 
-def load_models_gpu(models, memory_required=0):
+def load_models_gpu(models, memory_required=0, force_patch_weights=False):
     global vram_state
 
     inference_memory = minimum_inference_memory()
@@ -444,7 +444,7 @@ def load_models_gpu(models, memory_required=0):
         if vram_set_state == VRAMState.NO_VRAM:
             lowvram_model_memory = 64 * 1024 * 1024
 
-        cur_loaded_model = loaded_model.model_load(lowvram_model_memory)
+        cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
         current_loaded_models.insert(0, loaded_model)
     return
 
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index cf51c4ad8..48e5be31e 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -272,7 +272,7 @@ class ModelPatcher:
 
         return self.model
 
-    def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0):
+    def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False):
         self.patch_model(device_to, patch_weights=False)
 
         logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024)))
@@ -296,9 +296,15 @@ class ModelPatcher:
 
             if lowvram_weight:
                 if weight_key in self.patches:
-                    m.weight_function = LowVramPatch(weight_key, self)
+                    if force_patch_weights:
+                        self.patch_weight_to_device(weight_key)
+                    else:
+                        m.weight_function = LowVramPatch(weight_key, self)
                 if bias_key in self.patches:
-                    m.bias_function = LowVramPatch(bias_key, self)
+                    if force_patch_weights:
+                        self.patch_weight_to_device(bias_key)
+                    else:
+                        m.bias_function = LowVramPatch(bias_key, self)
 
                 m.prev_comfy_cast_weights = m.comfy_cast_weights
                 m.comfy_cast_weights = True
diff --git a/comfy/sd.py b/comfy/sd.py
index 9671e4aee..8044c184f 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -562,7 +562,7 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m
         load_models.append(clip.load_model())
         clip_sd = clip.get_sd()
 
-    model_management.load_models_gpu(load_models)
+    model_management.load_models_gpu(load_models, force_patch_weights=True)
     clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
     sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd)
     for k in extra_keys:
diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py
index 2a431f65d..8c5dc9859 100644
--- a/comfy_extras/nodes_model_merging.py
+++ b/comfy_extras/nodes_model_merging.py
@@ -262,7 +262,7 @@ class CLIPSave:
                 for x in extra_pnginfo:
                     metadata[x] = json.dumps(extra_pnginfo[x])
 
-        comfy.model_management.load_models_gpu([clip.load_model()])
+        comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True)
         clip_sd = clip.get_sd()
 
         for prefix in ["clip_l.", "clip_g.", ""]:

From 49c20cdc70149b2f18586d7de5c99b1013044ab5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 12 May 2024 05:34:43 -0400
Subject: [PATCH 006/315] No longer necessary.

---
 comfy/model_management.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 15dd73a6b..5a66a3836 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -393,8 +393,6 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
         loaded_model = LoadedModel(x)
 
         if loaded_model in current_loaded_models:
-            index = current_loaded_models.index(loaded_model)
-            current_loaded_models.insert(0, current_loaded_models.pop(index))
             models_already_loaded.append(loaded_model)
         else:
             if hasattr(x, "model"):

From fa6dd7e5bbee031defa640534b0924313757676f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 12 May 2024 06:13:45 -0400
Subject: [PATCH 007/315] Fix lowvram issue with saving checkpoints.

The previous fix didn't cover the case where the model was loaded in
lowvram mode right before.
---
 comfy/model_management.py | 23 ++++++++++++++++++++---
 comfy/model_patcher.py    |  6 ++++++
 2 files changed, 26 insertions(+), 3 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 5a66a3836..3d01e8a23 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -309,6 +309,11 @@ class LoadedModel:
         self.weights_loaded = True
         return self.real_model
 
+    def should_reload_model(self, force_patch_weights=False):
+        if force_patch_weights and self.model.lowvram_patch_counter > 0:
+            return True
+        return False
+
     def model_unload(self, unpatch_weights=True):
         self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights)
         self.model.model_patches_to(self.model.offload_device)
@@ -391,10 +396,22 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
     models_already_loaded = []
     for x in models:
         loaded_model = LoadedModel(x)
+        loaded = None
 
-        if loaded_model in current_loaded_models:
-            models_already_loaded.append(loaded_model)
-        else:
+        try:
+            loaded_model_index = current_loaded_models.index(loaded_model)
+        except:
+            loaded_model_index = None
+
+        if loaded_model_index is not None:
+            loaded = current_loaded_models[loaded_model_index]
+            if loaded.should_reload_model(force_patch_weights=force_patch_weights): #TODO: cleanup this model reload logic
+                current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True)
+                loaded = None
+            else:
+                models_already_loaded.append(loaded)
+
+        if loaded is None:
             if hasattr(x, "model"):
                 logging.info(f"Requested to load {x.model.__class__.__name__}")
             models_to_load.append(loaded_model)
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 48e5be31e..c38b2f79b 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -58,6 +58,7 @@ class ModelPatcher:
 
         self.weight_inplace_update = weight_inplace_update
         self.model_lowvram = False
+        self.lowvram_patch_counter = 0
         self.patches_uuid = uuid.uuid4()
 
     def model_size(self):
@@ -284,6 +285,7 @@ class ModelPatcher:
                 return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key)
 
         mem_counter = 0
+        patch_counter = 0
         for n, m in self.model.named_modules():
             lowvram_weight = False
             if hasattr(m, "comfy_cast_weights"):
@@ -300,11 +302,13 @@ class ModelPatcher:
                         self.patch_weight_to_device(weight_key)
                     else:
                         m.weight_function = LowVramPatch(weight_key, self)
+                        patch_counter += 1
                 if bias_key in self.patches:
                     if force_patch_weights:
                         self.patch_weight_to_device(bias_key)
                     else:
                         m.bias_function = LowVramPatch(bias_key, self)
+                        patch_counter += 1
 
                 m.prev_comfy_cast_weights = m.comfy_cast_weights
                 m.comfy_cast_weights = True
@@ -317,6 +321,7 @@ class ModelPatcher:
                     logging.debug("lowvram: loaded module regularly {}".format(m))
 
         self.model_lowvram = True
+        self.lowvram_patch_counter = patch_counter
         return self.model
 
     def calculate_weight(self, patches, weight, key):
@@ -468,6 +473,7 @@ class ModelPatcher:
                     m.bias_function = None
 
                 self.model_lowvram = False
+                self.lowvram_patch_counter = 0
 
             keys = list(self.backup.keys())
 

From f509c6fe21179db585372ec4443c80179fa6c659 Mon Sep 17 00:00:00 2001
From: Simon Lui <502929+simonlui@users.noreply.github.com>
Date: Sun, 12 May 2024 03:36:30 -0700
Subject: [PATCH 008/315] Fix Intel GPU memory allocation accuracy and
 documentation update. (#3459)

* Change calculation of memory total to be more accurate, allocated is actually smaller than reserved.

* Update README.md install documentation for Intel GPUs.
---
 README.md                 | 11 ++++++++++-
 comfy/model_management.py |  6 +++---
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 2636ce140..312468a98 100644
--- a/README.md
+++ b/README.md
@@ -136,7 +136,16 @@ After this you should have everything installed and can proceed to running Comfy
 
 ### Others:
 
-#### [Intel Arc](https://github.com/comfyanonymous/ComfyUI/discussions/476)
+#### Intel GPUs
+
+Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) page. Choose your platform and method of install and follow the instructions. The steps are as follows:
+
+1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed.
+1. Follow the instructions to install [Intel's oneAPI Basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for your platform.
+1. Install the packages for IPEX using the instructions provided in the Installation page for your platform.
+1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed.
+
+Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
 
 #### Apple Mac silicon
 
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 3d01e8a23..7b54b2567 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -102,8 +102,8 @@ def get_total_memory(dev=None, torch_total_too=False):
         elif is_intel_xpu():
             stats = torch.xpu.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
-            mem_total = torch.xpu.get_device_properties(dev).total_memory
             mem_total_torch = mem_reserved
+            mem_total = torch.xpu.get_device_properties(dev).total_memory
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
@@ -701,10 +701,10 @@ def get_free_memory(dev=None, torch_free_too=False):
         elif is_intel_xpu():
             stats = torch.xpu.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
-            mem_allocated = stats['allocated_bytes.all.current']
             mem_reserved = stats['reserved_bytes.all.current']
             mem_free_torch = mem_reserved - mem_active
-            mem_free_total = torch.xpu.get_device_properties(dev).total_memory - mem_allocated
+            mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
+            mem_free_total = mem_free_xpu + mem_free_torch
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']

From 22edd3add541cdb956b50a3c6412ce2bb36c1090 Mon Sep 17 00:00:00 2001
From: shawnington <88048838+shawnington@users.noreply.github.com>
Date: Sun, 12 May 2024 04:07:38 -0700
Subject: [PATCH 009/315] =?UTF-8?q?Fix=20to=20LoadImage=20Node=20for=20#34?=
 =?UTF-8?q?16=20HDR=20images=20loading=20additional=20smaller=E2=80=A6=20(?=
 =?UTF-8?q?#3454)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* Fix to LoadImage Node for #3416 HDR images loading additional smaller images.

Added a blocking if statement  in the ImageSequence.Iterator that checks if subsequent images after the first match dimensionally, and prevent them from being appended to output_images if they do not match.

This does not fix or change current behavior for PIL 10.2.0 where the images are loaded at the same size, but it does for 10.3.0 where they are loaded at their correct smaller sizes.

* added list of excluded formats that should return 1 image

added an explicit check for the image format so that additional formats can be added to the list that have problematic behavior.
---
 nodes.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/nodes.py b/nodes.py
index 488afd577..37e7d7345 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1461,12 +1461,24 @@ class LoadImage:
         
         output_images = []
         output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+        
         for i in ImageSequence.Iterator(img):
             i = node_helpers.pillow(ImageOps.exif_transpose, i)
 
             if i.mode == 'I':
                 i = i.point(lambda i: i * (1 / 255))
             image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+            
+            if image.size[0] != w or image.size[1] != h:
+                continue
+            
             image = np.array(image).astype(np.float32) / 255.0
             image = torch.from_numpy(image)[None,]
             if 'A' in i.getbands():
@@ -1477,7 +1489,7 @@ class LoadImage:
             output_images.append(image)
             output_masks.append(mask.unsqueeze(0))
 
-        if len(output_images) > 1:
+        if len(output_images) > 1 and img.format not in excluded_formats:
             output_image = torch.cat(output_images, dim=0)
             output_mask = torch.cat(output_masks, dim=0)
         else:

From 794a357f7a4bd40c6e2dafab9d93142be9933a56 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 12 May 2024 07:24:12 -0400
Subject: [PATCH 010/315] Update the nightly workflow.

---
 .github/workflows/windows_release_nightly_pytorch.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml
index 672a7f220..1434b0a23 100644
--- a/.github/workflows/windows_release_nightly_pytorch.yml
+++ b/.github/workflows/windows_release_nightly_pytorch.yml
@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "121"
+        default: "124"
 
       python_minor:
         description: 'python minor version'
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "2"
+        default: "3"
 #  push:
 #    branches:
 #      - master

From ece5acb8e8025d8ca26aa880f604d971d245475d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 12 May 2024 16:05:10 -0400
Subject: [PATCH 011/315] Fix nightly package workflow.

---
 .github/workflows/windows_release_nightly_pytorch.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml
index 1434b0a23..fa24a985c 100644
--- a/.github/workflows/windows_release_nightly_pytorch.yml
+++ b/.github/workflows/windows_release_nightly_pytorch.yml
@@ -49,7 +49,7 @@ jobs:
             echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
             curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
             ./python.exe get-pip.py
-            python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
+            python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 numpy==1.26.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
             ls ../temp_wheel_dir
             ./python.exe -s -m pip install --pre ../temp_wheel_dir/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth

From cf6e1efb69a17cdaad509e665ad53b0e031b4807 Mon Sep 17 00:00:00 2001
From: freakabcd <freakabcd@yahoo.com>
Date: Tue, 14 May 2024 05:22:22 +1000
Subject: [PATCH 012/315] Show message on error when loading wf from file
 (works on drag and drop) (#3466)

---
 web/scripts/app.js | 48 +++++++++++++++++++++++++++++-----------------
 1 file changed, 30 insertions(+), 18 deletions(-)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index 7ed262cb2..a516be704 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -2157,6 +2157,14 @@ export class ComfyApp {
 		api.dispatchEvent(new CustomEvent("promptQueued", { detail: { number, batchCount } }));
 	}
 
+	showErrorOnFileLoad(file) {
+		this.ui.dialog.show(
+			$el("div", [
+				$el("p", {textContent: `Unable to find workflow in ${file.name}`})
+			]).outerHTML
+		);
+	}
+
 	/**
 	 * Loads workflow data from the specified file
 	 * @param {File} file
@@ -2164,27 +2172,27 @@ export class ComfyApp {
 	async handleFile(file) {
 		if (file.type === "image/png") {
 			const pngInfo = await getPngMetadata(file);
-			if (pngInfo) {
-				if (pngInfo.workflow) {
-					await this.loadGraphData(JSON.parse(pngInfo.workflow));
-				} else if (pngInfo.prompt) {
-					this.loadApiJson(JSON.parse(pngInfo.prompt));
-				} else if (pngInfo.parameters) {
-					importA1111(this.graph, pngInfo.parameters);
-				}
+			if (pngInfo?.workflow) {
+				await this.loadGraphData(JSON.parse(pngInfo.workflow));
+			} else if (pngInfo?.prompt) {
+				this.loadApiJson(JSON.parse(pngInfo.prompt));
+			} else if (pngInfo?.parameters) {
+				importA1111(this.graph, pngInfo.parameters);
+			} else {
+				this.showErrorOnFileLoad(file);
 			}
 		} else if (file.type === "image/webp") {
 			const pngInfo = await getWebpMetadata(file);
-			if (pngInfo) {
-				if (pngInfo.workflow) {
-					this.loadGraphData(JSON.parse(pngInfo.workflow));
-				} else if (pngInfo.Workflow) {
-					this.loadGraphData(JSON.parse(pngInfo.Workflow)); // Support loading workflows from that webp custom node.
-				} else if (pngInfo.prompt) {
-					this.loadApiJson(JSON.parse(pngInfo.prompt));
-				} else if (pngInfo.Prompt) {
-					this.loadApiJson(JSON.parse(pngInfo.Prompt)); // Support loading prompts from that webp custom node.
-				}
+			// Support loading workflows from that webp custom node.
+			const workflow = pngInfo?.workflow || pngInfo?.Workflow;
+			const prompt = pngInfo?.prompt || pngInfo?.Prompt;
+
+			if (workflow) {
+				this.loadGraphData(JSON.parse(workflow));
+			} else if (prompt) {
+				this.loadApiJson(JSON.parse(prompt));
+			} else {
+				this.showErrorOnFileLoad(file);
 			}
 		} else if (file.type === "application/json" || file.name?.endsWith(".json")) {
 			const reader = new FileReader();
@@ -2205,7 +2213,11 @@ export class ComfyApp {
 				await this.loadGraphData(JSON.parse(info.workflow));
 			} else if (info.prompt) {
 				this.loadApiJson(JSON.parse(info.prompt));
+			} else {
+				this.showErrorOnFileLoad(file);
 			}
+		} else {
+			this.showErrorOnFileLoad(file);
 		}
 	}
 

From 2de3b69b3074028fd0f850f801f4ca023489c692 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 13 May 2024 21:54:11 -0400
Subject: [PATCH 013/315] Support saving some more modelspec types.

---
 comfy_extras/nodes_model_merging.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py
index 8c5dc9859..bb15112f4 100644
--- a/comfy_extras/nodes_model_merging.py
+++ b/comfy_extras/nodes_model_merging.py
@@ -175,9 +175,14 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
 
     enable_modelspec = True
     if isinstance(model.model, comfy.model_base.SDXL):
-        metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
+        if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix):
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit"
+        else:
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
     elif isinstance(model.model, comfy.model_base.SDXLRefiner):
         metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
+    elif isinstance(model.model, comfy.model_base.SVD_img2vid):
+        metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1"
     else:
         enable_modelspec = False
 

From b0ab31d06c5df98b094d8f38db5cda4e5aec47eb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 14 May 2024 12:47:31 -0400
Subject: [PATCH 014/315] Refactor attention upcasting code part 1.

---
 comfy/ldm/modules/attention.py | 28 +++++++++++++++-------------
 1 file changed, 15 insertions(+), 13 deletions(-)

diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index d51a2fae1..de66db4f8 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -22,9 +22,9 @@ ops = comfy.ops.disable_weight_init
 # CrossAttn precision handling
 if args.dont_upcast_attention:
     logging.info("disabling upcasting of attention")
-    _ATTN_PRECISION = "fp16"
+    _ATTN_PRECISION = None
 else:
-    _ATTN_PRECISION = "fp32"
+    _ATTN_PRECISION = torch.float32
 
 
 def exists(val):
@@ -85,7 +85,7 @@ class FeedForward(nn.Module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
-def attention_basic(q, k, v, heads, mask=None):
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None):
     b, _, dim_head = q.shape
     dim_head //= heads
     scale = dim_head ** -0.5
@@ -101,7 +101,7 @@ def attention_basic(q, k, v, heads, mask=None):
     )
 
     # force cast to fp32 to avoid overflowing
-    if _ATTN_PRECISION =="fp32":
+    if attn_precision == torch.float32:
         sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
     else:
         sim = einsum('b i d, b j d -> b i j', q, k) * scale
@@ -135,7 +135,7 @@ def attention_basic(q, k, v, heads, mask=None):
     return out
 
 
-def attention_sub_quad(query, key, value, heads, mask=None):
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None):
     b, _, dim_head = query.shape
     dim_head //= heads
 
@@ -146,7 +146,7 @@ def attention_sub_quad(query, key, value, heads, mask=None):
     key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
 
     dtype = query.dtype
-    upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
+    upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32
     if upcast_attention:
         bytes_per_token = torch.finfo(torch.float32).bits//8
     else:
@@ -195,7 +195,7 @@ def attention_sub_quad(query, key, value, heads, mask=None):
     hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
     return hidden_states
 
-def attention_split(q, k, v, heads, mask=None):
+def attention_split(q, k, v, heads, mask=None, attn_precision=None):
     b, _, dim_head = q.shape
     dim_head //= heads
     scale = dim_head ** -0.5
@@ -214,10 +214,12 @@ def attention_split(q, k, v, heads, mask=None):
 
     mem_free_total = model_management.get_free_memory(q.device)
 
-    if _ATTN_PRECISION =="fp32":
+    if attn_precision == torch.float32:
         element_size = 4
+        upcast = True
     else:
         element_size = q.element_size()
+        upcast = False
 
     gb = 1024 ** 3
     tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
@@ -251,7 +253,7 @@ def attention_split(q, k, v, heads, mask=None):
             slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
             for i in range(0, q.shape[1], slice_size):
                 end = i + slice_size
-                if _ATTN_PRECISION =="fp32":
+                if upcast:
                     with torch.autocast(enabled=False, device_type = 'cuda'):
                         s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
                 else:
@@ -302,7 +304,7 @@ try:
 except:
     pass
 
-def attention_xformers(q, k, v, heads, mask=None):
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
     b, _, dim_head = q.shape
     dim_head //= heads
     if BROKEN_XFORMERS:
@@ -334,7 +336,7 @@ def attention_xformers(q, k, v, heads, mask=None):
     )
     return out
 
-def attention_pytorch(q, k, v, heads, mask=None):
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None):
     b, _, dim_head = q.shape
     dim_head //= heads
     q, k, v = map(
@@ -409,9 +411,9 @@ class CrossAttention(nn.Module):
             v = self.to_v(context)
 
         if mask is None:
-            out = optimized_attention(q, k, v, self.heads)
+            out = optimized_attention(q, k, v, self.heads, attn_precision=_ATTN_PRECISION)
         else:
-            out = optimized_attention_masked(q, k, v, self.heads, mask)
+            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=_ATTN_PRECISION)
         return self.to_out(out)
 
 

From bb4940d837f0cfd338ff64776b084303be066c67 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 14 May 2024 15:18:00 -0400
Subject: [PATCH 015/315] Only enable attention upcasting on models that
 actually need it.

---
 README.md                                     |  6 ----
 comfy/cli_args.py                             |  1 -
 comfy/ldm/modules/attention.py                | 28 ++++++++-----------
 .../modules/diffusionmodules/openaimodel.py   |  4 ++-
 comfy/supported_models.py                     | 12 ++++++++
 5 files changed, 27 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index 312468a98..80de21bcd 100644
--- a/README.md
+++ b/README.md
@@ -207,12 +207,6 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the
 ```embedding:embedding_filename.pt```
 
 
-## How to increase generation speed?
-
-On non Nvidia hardware you can set this command line setting to disable the upcasting to fp32 in some cross attention operations which will increase your speed. Note that this will very likely give you black images on SD2.x models. If you use xformers or pytorch attention this option does not do anything.
-
-```--dont-upcast-attention```
-
 ## How to show high-quality previews?
 
 Use ```--preview-method auto``` to enable previews.
diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 569c79380..2759f4e94 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -51,7 +51,6 @@ cm_group = parser.add_mutually_exclusive_group()
 cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
 cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
 
-parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")
 
 fp_group = parser.add_mutually_exclusive_group()
 fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index de66db4f8..2515bac5e 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -19,14 +19,6 @@ from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
-# CrossAttn precision handling
-if args.dont_upcast_attention:
-    logging.info("disabling upcasting of attention")
-    _ATTN_PRECISION = None
-else:
-    _ATTN_PRECISION = torch.float32
-
-
 def exists(val):
     return val is not None
 
@@ -386,10 +378,11 @@ def optimized_attention_for_device(device, mask=False, small_input=False):
 
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
+        self.attn_precision = attn_precision
 
         self.heads = heads
         self.dim_head = dim_head
@@ -411,15 +404,15 @@ class CrossAttention(nn.Module):
             v = self.to_v(context)
 
         if mask is None:
-            out = optimized_attention(q, k, v, self.heads, attn_precision=_ATTN_PRECISION)
+            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
         else:
-            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=_ATTN_PRECISION)
+            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
         return self.to_out(out)
 
 
 class BasicTransformerBlock(nn.Module):
     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None,
-                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops):
+                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops):
         super().__init__()
 
         self.ff_in = ff_in or inner_dim is not None
@@ -434,7 +427,7 @@ class BasicTransformerBlock(nn.Module):
 
         self.disable_self_attn = disable_self_attn
         self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
+                              context_dim=context_dim if self.disable_self_attn else None, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
         self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
 
         if disable_temporal_crossattention:
@@ -448,7 +441,7 @@ class BasicTransformerBlock(nn.Module):
                 context_dim_attn2 = context_dim
 
             self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
-                                heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
+                                heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
             self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
 
         self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
@@ -588,7 +581,7 @@ class SpatialTransformer(nn.Module):
     def __init__(self, in_channels, n_heads, d_head,
                  depth=1, dropout=0., context_dim=None,
                  disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True, dtype=None, device=None, operations=ops):
+                 use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops):
         super().__init__()
         if exists(context_dim) and not isinstance(context_dim, list):
             context_dim = [context_dim] * depth
@@ -606,7 +599,7 @@ class SpatialTransformer(nn.Module):
 
         self.transformer_blocks = nn.ModuleList(
             [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
-                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations)
+                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
                 for d in range(depth)]
         )
         if not use_linear:
@@ -662,6 +655,7 @@ class SpatialVideoTransformer(SpatialTransformer):
         disable_self_attn=False,
         disable_temporal_crossattention=False,
         max_time_embed_period: int = 10000,
+        attn_precision=None,
         dtype=None, device=None, operations=ops
     ):
         super().__init__(
@@ -674,6 +668,7 @@ class SpatialVideoTransformer(SpatialTransformer):
             context_dim=context_dim,
             use_linear=use_linear,
             disable_self_attn=disable_self_attn,
+            attn_precision=attn_precision,
             dtype=dtype, device=device, operations=operations
         )
         self.time_depth = time_depth
@@ -703,6 +698,7 @@ class SpatialVideoTransformer(SpatialTransformer):
                     inner_dim=time_mix_inner_dim,
                     disable_self_attn=disable_self_attn,
                     disable_temporal_crossattention=disable_temporal_crossattention,
+                    attn_precision=attn_precision,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(self.depth)
diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py
index d782eff31..1f5a4ded2 100644
--- a/comfy/ldm/modules/diffusionmodules/openaimodel.py
+++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py
@@ -431,6 +431,7 @@ class UNetModel(nn.Module):
         video_kernel_size=None,
         disable_temporal_crossattention=False,
         max_ddpm_temb_period=10000,
+        attn_precision=None,
         device=None,
         operations=ops,
     ):
@@ -550,13 +551,14 @@ class UNetModel(nn.Module):
                     disable_self_attn=disable_self_attn,
                     disable_temporal_crossattention=disable_temporal_crossattention,
                     max_time_embed_period=max_ddpm_temb_period,
+                    attn_precision=attn_precision,
                     dtype=self.dtype, device=device, operations=operations
                 )
             else:
                 return SpatialTransformer(
                                 ch, num_heads, dim_head, depth=depth, context_dim=context_dim,
                                 disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
+                                use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
                             )
 
         def get_resblock(
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index b3b69e05b..6ca32e8ee 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -65,6 +65,12 @@ class SD20(supported_models_base.BASE):
         "use_temporal_attention": False,
     }
 
+    unet_extra_config = {
+        "num_heads": -1,
+        "num_head_channels": 64,
+        "attn_precision": torch.float32,
+    }
+
     latent_format = latent_formats.SD15
 
     def model_type(self, state_dict, prefix=""):
@@ -276,6 +282,12 @@ class SVD_img2vid(supported_models_base.BASE):
         "use_temporal_resblock": True
     }
 
+    unet_extra_config = {
+        "num_heads": -1,
+        "num_head_channels": 64,
+        "attn_precision": torch.float32,
+    }
+
     clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual."
 
     latent_format = latent_formats.SD15

From ec6f16adb607fa8d14b26670106e1a09d8401e20 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 14 May 2024 18:02:27 -0400
Subject: [PATCH 016/315] Fix SAG.

---
 comfy/ldm/modules/attention.py |  6 ++++--
 comfy_extras/nodes_sag.py      | 10 +++++-----
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 2515bac5e..1d5cf0da5 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -420,6 +420,7 @@ class BasicTransformerBlock(nn.Module):
             inner_dim = dim
 
         self.is_res = inner_dim == dim
+        self.attn_precision = attn_precision
 
         if self.ff_in:
             self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device)
@@ -427,7 +428,7 @@ class BasicTransformerBlock(nn.Module):
 
         self.disable_self_attn = disable_self_attn
         self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
+                              context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
         self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
 
         if disable_temporal_crossattention:
@@ -441,7 +442,7 @@ class BasicTransformerBlock(nn.Module):
                 context_dim_attn2 = context_dim
 
             self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
-                                heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
+                                heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
             self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
 
         self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
@@ -471,6 +472,7 @@ class BasicTransformerBlock(nn.Module):
 
         extra_options["n_heads"] = self.n_heads
         extra_options["dim_head"] = self.d_head
+        extra_options["attn_precision"] = self.attn_precision
 
         if self.ff_in:
             x_skip = x
diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py
index 69084e91d..8d786db57 100644
--- a/comfy_extras/nodes_sag.py
+++ b/comfy_extras/nodes_sag.py
@@ -5,12 +5,12 @@ import math
 
 from einops import rearrange, repeat
 import os
-from comfy.ldm.modules.attention import optimized_attention, _ATTN_PRECISION
+from comfy.ldm.modules.attention import optimized_attention
 import comfy.samplers
 
 # from comfy/ldm/modules/attention.py
 # but modified to return attention scores as well as output
-def attention_basic_with_sim(q, k, v, heads, mask=None):
+def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None):
     b, _, dim_head = q.shape
     dim_head //= heads
     scale = dim_head ** -0.5
@@ -26,7 +26,7 @@ def attention_basic_with_sim(q, k, v, heads, mask=None):
     )
 
     # force cast to fp32 to avoid overflowing
-    if _ATTN_PRECISION =="fp32":
+    if attn_precision == torch.float32:
         sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
     else:
         sim = einsum('b i d, b j d -> b i j', q, k) * scale
@@ -121,13 +121,13 @@ class SelfAttentionGuidance:
             if 1 in cond_or_uncond:
                 uncond_index = cond_or_uncond.index(1)
                 # do the entire attention operation, but save the attention scores to attn_scores
-                (out, sim) = attention_basic_with_sim(q, k, v, heads=heads)
+                (out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"])
                 # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn]
                 n_slices = heads * b
                 attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)]
                 return out
             else:
-                return optimized_attention(q, k, v, heads=heads)
+                return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"])
 
         def post_cfg_function(args):
             nonlocal attn_scores

From 2d4164271634476627aae31fbec251ca748a0ae0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 15 May 2024 02:40:06 -0400
Subject: [PATCH 017/315] Fix lowvram dora issue.

---
 comfy/model_patcher.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index c38b2f79b..35ede5eef 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -16,7 +16,7 @@ def apply_weight_decompose(dora_scale, weight):
         .transpose(0, 1)
     )
 
-    return weight * (dora_scale / weight_norm)
+    return weight * (dora_scale / weight_norm).type(weight.dtype)
 
 def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
     to = model_options["transformer_options"].copy()

From 58f8388020ba6ab5a913beb742a6312914d640b2 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 16 May 2024 00:11:01 -0400
Subject: [PATCH 018/315] More proper fix for #3484.

---
 folder_paths.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/folder_paths.py b/folder_paths.py
index 489795002..234b73409 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -258,7 +258,7 @@ def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height
         raise Exception(err)
 
     try:
-        counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
+        counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
     except ValueError:
         counter = 1
     except FileNotFoundError:

From 46daf0a9a7c02e7652e60f3a7af898e2f6594c2e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 16 May 2024 04:09:41 -0400
Subject: [PATCH 019/315] Add debug options to force on and off attention
 upcasting.

---
 comfy/cli_args.py              |  5 +++++
 comfy/ldm/modules/attention.py | 14 ++++++++++++++
 2 files changed, 19 insertions(+)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 2759f4e94..b8ac9bc69 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -95,6 +95,11 @@ attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", he
 
 parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
 
+upcast = parser.add_mutually_exclusive_group()
+upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.")
+upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
+
+
 vram_group = parser.add_mutually_exclusive_group()
 vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
 vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 1d5cf0da5..426530867 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -19,6 +19,14 @@ from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
+
+def get_attn_precision(attn_precision):
+    if args.dont_upcast_attention:
+        return None
+    if attn_precision is None and args.force_upcast_attention:
+        return torch.float32
+    return attn_precision
+
 def exists(val):
     return val is not None
 
@@ -78,6 +86,8 @@ def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
 def attention_basic(q, k, v, heads, mask=None, attn_precision=None):
+    attn_precision = get_attn_precision(attn_precision)
+
     b, _, dim_head = q.shape
     dim_head //= heads
     scale = dim_head ** -0.5
@@ -128,6 +138,8 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None):
 
 
 def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None):
+    attn_precision = get_attn_precision(attn_precision)
+
     b, _, dim_head = query.shape
     dim_head //= heads
 
@@ -188,6 +200,8 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None)
     return hidden_states
 
 def attention_split(q, k, v, heads, mask=None, attn_precision=None):
+    attn_precision = get_attn_precision(attn_precision)
+
     b, _, dim_head = q.shape
     dim_head //= heads
     scale = dim_head ** -0.5

From 19300655ddaeb1287a2ecf427cf64c0766e8a999 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 17 May 2024 00:31:32 -0400
Subject: [PATCH 020/315] Don't automatically switch to lowvram mode on GPUs
 with low memory.

---
 comfy/model_management.py | 8 +-------
 1 file changed, 1 insertion(+), 7 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 7b54b2567..21ae8d291 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -119,10 +119,6 @@ def get_total_memory(dev=None, torch_total_too=False):
 total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
 total_ram = psutil.virtual_memory().total / (1024 * 1024)
 logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
-if not args.normalvram and not args.cpu:
-    if lowvram_available and total_vram <= 4096:
-        logging.warning("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
-        set_vram_to = VRAMState.LOW_VRAM
 
 try:
     OOM_EXCEPTION = torch.cuda.OutOfMemoryError
@@ -451,9 +447,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
             lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
-            if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary
-                vram_set_state = VRAMState.LOW_VRAM
-            else:
+            if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 
         if vram_set_state == VRAMState.NO_VRAM:

From 91590adf04e644304267a7a296710b4f7ae80bb2 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Fri, 17 May 2024 18:16:08 +0100
Subject: [PATCH 021/315] Add webcam node (#3497)

* Add webcam node

* unused import
---
 comfy_extras/nodes_webcam.py         |  33 ++++++++
 nodes.py                             |   1 +
 web/extensions/core/webcamCapture.js | 120 +++++++++++++++++++++++++++
 3 files changed, 154 insertions(+)
 create mode 100644 comfy_extras/nodes_webcam.py
 create mode 100644 web/extensions/core/webcamCapture.js

diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py
new file mode 100644
index 000000000..32a0ba2f6
--- /dev/null
+++ b/comfy_extras/nodes_webcam.py
@@ -0,0 +1,33 @@
+import nodes
+import folder_paths
+
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+
+class WebcamCapture(nodes.LoadImage):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("WEBCAM", {}),
+                "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "capture_on_queue": ("BOOLEAN", {"default": True}),
+            }
+        }
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_capture"
+
+    CATEGORY = "image"
+
+    def load_capture(s, image, **kwargs):
+        return super().load_image(folder_paths.get_annotated_filepath(image))
+
+
+NODE_CLASS_MAPPINGS = {
+    "WebcamCapture": WebcamCapture,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "WebcamCapture": "Webcam Capture",
+}
\ No newline at end of file
diff --git a/nodes.py b/nodes.py
index 37e7d7345..34821ca3f 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1961,6 +1961,7 @@ def init_custom_nodes():
         "nodes_align_your_steps.py",
         "nodes_attention_multiply.py",
         "nodes_advanced_samplers.py",
+        "nodes_webcam.py",
     ]
 
     import_failed = []
diff --git a/web/extensions/core/webcamCapture.js b/web/extensions/core/webcamCapture.js
new file mode 100644
index 000000000..ea1f597e2
--- /dev/null
+++ b/web/extensions/core/webcamCapture.js
@@ -0,0 +1,120 @@
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+
+const WEBCAM_READY = Symbol();
+
+app.registerExtension({
+	name: "Comfy.WebcamCapture",
+	getCustomWidgets(app) {
+		return {
+			WEBCAM(node, inputName) {
+				let res;
+				node[WEBCAM_READY] = new Promise((resolve) => (res = resolve));
+
+				const container = document.createElement("div");
+				container.style.background = "rgba(0,0,0,0.25)";
+				container.style.textAlign = "center";
+
+				const video = document.createElement("video");
+				video.style.height = video.style.width = "100%";
+
+				const loadVideo = async () => {
+					try {
+						const stream = await navigator.mediaDevices.getUserMedia({ video: true, audio: false });
+						container.replaceChildren(video);
+
+						setTimeout(() => res(video), 500); // Fallback as loadedmetadata doesnt fire sometimes?
+						video.addEventListener("loadedmetadata", () => res(video), false);
+						video.srcObject = stream;
+						video.play();
+					} catch (error) {
+						const label = document.createElement("div");
+						label.style.color = "red";
+						label.style.overflow = "auto";
+						label.style.maxHeight = "100%";
+						label.style.whiteSpace = "pre-wrap";
+						label.textContent = "Unable to load webcam, please ensure access is granted:\n" + error.message;
+						container.replaceChildren(label);
+					}
+				};
+
+				loadVideo();
+
+				return { widget: node.addDOMWidget(inputName, "WEBCAM", container) };
+			},
+		};
+	},
+	nodeCreated(node) {
+		if ((node.type, node.constructor.comfyClass !== "WebcamCapture")) return;
+
+		let video;
+		const camera = node.widgets.find((w) => w.name === "image");
+		const w = node.widgets.find((w) => w.name === "width");
+		const h = node.widgets.find((w) => w.name === "height");
+		const captureOnQueue = node.widgets.find((w) => w.name === "capture_on_queue");
+
+		const canvas = document.createElement("canvas");
+
+		const capture = () => {
+			canvas.width = w.value;
+			canvas.height = h.value;
+			const ctx = canvas.getContext("2d");
+			ctx.drawImage(video, 0, 0, w.value, h.value);
+			const data = canvas.toDataURL("image/png");
+
+			const img = new Image();
+			img.onload = () => {
+				node.imgs = [img];
+				app.graph.setDirtyCanvas(true);
+				requestAnimationFrame(() => {
+					node.setSizeForImage?.();
+				});
+			};
+			img.src = data;
+		};
+
+		const btn = node.addWidget("button", "waiting for camera...", "capture", capture);
+		btn.disabled = true;
+		btn.serializeValue = () => undefined;
+
+		camera.serializeValue = async () => {
+			if (captureOnQueue.value) {
+				capture();
+			} else if (!node.imgs?.length) {
+				const err = `No webcam image captured`;
+				alert(err);
+				throw new Error(err);
+			}
+
+			// Upload image to temp storage
+			const blob = await new Promise((r) => canvas.toBlob(r));
+			const name = `${+new Date()}.png`;
+			const file = new File([blob], name);
+			const body = new FormData();
+			body.append("image", file);
+			body.append("subfolder", "webcam");
+			body.append("type", "temp");
+			const resp = await api.fetchApi("/upload/image", {
+				method: "POST",
+				body,
+			});
+			if (resp.status !== 200) {
+				const err = `Error uploading camera image: ${resp.status} - ${resp.statusText}`;
+				alert(err);
+				throw new Error(err);
+			}
+			return `webcam/${name} [temp]`;
+		};
+
+		node[WEBCAM_READY].then((v) => {
+			video = v;
+			// If width isnt specified then use video output resolution
+			if (!w.value) {
+				w.value = video.videoWidth || 640;
+				h.value = video.videoHeight || 480; 
+			}
+			btn.disabled = false;
+			btn.label = "capture";
+		});
+	},
+});

From 1c4af5918a5ffb022606e56ab2d31e510dcccec2 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 17 May 2024 14:02:09 -0400
Subject: [PATCH 022/315] Better error message if the webcam node doesn't work.

---
 web/extensions/core/webcamCapture.js | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/web/extensions/core/webcamCapture.js b/web/extensions/core/webcamCapture.js
index ea1f597e2..dd5725bd4 100644
--- a/web/extensions/core/webcamCapture.js
+++ b/web/extensions/core/webcamCapture.js
@@ -33,7 +33,13 @@ app.registerExtension({
 						label.style.overflow = "auto";
 						label.style.maxHeight = "100%";
 						label.style.whiteSpace = "pre-wrap";
-						label.textContent = "Unable to load webcam, please ensure access is granted:\n" + error.message;
+
+						if (window.isSecureContext) {
+							label.textContent = "Unable to load webcam, please ensure access is granted:\n" + error.message;
+						} else {
+							label.textContent = "Unable to load webcam. A secure context is required, if you are not accessing ComfyUI on localhost (127.0.0.1) you will have to enable TLS (https)\n\n" + error.message;
+						}
+
 						container.replaceChildren(label);
 					}
 				};

From 98f828fad98643d4f8f31e355cc79a1fd42e1bb8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 18 May 2024 09:36:26 -0400
Subject: [PATCH 023/315] Remove unnecessary code.

---
 comfy/ldm/modules/attention.py              | 11 ++---------
 comfy/ldm/modules/diffusionmodules/model.py |  1 -
 2 files changed, 2 insertions(+), 10 deletions(-)

diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 426530867..88ee2f32d 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -318,11 +318,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
             return attention_pytorch(q, k, v, heads, mask)
 
     q, k, v = map(
-        lambda t: t.unsqueeze(3)
-        .reshape(b, -1, heads, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b * heads, -1, dim_head)
-        .contiguous(),
+        lambda t: t.reshape(b, -1, heads, dim_head),
         (q, k, v),
     )
 
@@ -335,10 +331,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
 
     out = (
-        out.unsqueeze(0)
-        .reshape(b, heads, -1, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b, -1, heads * dim_head)
+        out.reshape(b, -1, heads * dim_head)
     )
     return out
 
diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
index fabc5c5e5..04eb83b21 100644
--- a/comfy/ldm/modules/diffusionmodules/model.py
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -3,7 +3,6 @@ import math
 import torch
 import torch.nn as nn
 import numpy as np
-from einops import rearrange
 from typing import Optional, Any
 import logging
 

From 0bdc2b15c75426af75a326d5966ad47aab5b76d3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 18 May 2024 10:11:44 -0400
Subject: [PATCH 024/315] Cleanup.

---
 comfy/ldm/modules/attention.py                    | 10 +++-------
 comfy/ldm/modules/diffusionmodules/openaimodel.py |  2 +-
 2 files changed, 4 insertions(+), 8 deletions(-)

diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 88ee2f32d..aa74b6323 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -6,7 +6,7 @@ from einops import rearrange, repeat
 from typing import Optional, Any
 import logging
 
-from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding
+from .diffusionmodules.util import AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
 
 from comfy import model_management
@@ -454,15 +454,11 @@ class BasicTransformerBlock(nn.Module):
 
         self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
         self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
-        self.checkpoint = checkpoint
         self.n_heads = n_heads
         self.d_head = d_head
         self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
 
     def forward(self, x, context=None, transformer_options={}):
-        return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None, transformer_options={}):
         extra_options = {}
         block = transformer_options.get("block", None)
         block_index = transformer_options.get("block_index", 0)
@@ -629,7 +625,7 @@ class SpatialTransformer(nn.Module):
         x = self.norm(x)
         if not self.use_linear:
             x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+        x = x.movedim(1, -1).flatten(1, 2).contiguous()
         if self.use_linear:
             x = self.proj_in(x)
         for i, block in enumerate(self.transformer_blocks):
@@ -637,7 +633,7 @@ class SpatialTransformer(nn.Module):
             x = block(x, context=context[i], transformer_options=transformer_options)
         if self.use_linear:
             x = self.proj_out(x)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+        x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(-1, 1).contiguous()
         if not self.use_linear:
             x = self.proj_out(x)
         return x + x_in
diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py
index 1f5a4ded2..ba8fc2c4a 100644
--- a/comfy/ldm/modules/diffusionmodules/openaimodel.py
+++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py
@@ -258,7 +258,7 @@ class ResBlock(TimestepBlock):
         else:
             if emb_out is not None:
                 if self.exchange_temb_dims:
-                    emb_out = rearrange(emb_out, "b t c ... -> b c t ...")
+                    emb_out = emb_out.movedim(1, 2)
                 h = h + emb_out
             h = self.out_layers(h)
         return self.skip_connection(x) + h

From f37a47110bef97186b8392830d5c49148a06ecef Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 19 May 2024 11:45:36 -0400
Subject: [PATCH 025/315] Make --preview-method auto default to the fast
 latent2rgb previews.

---
 latent_preview.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/latent_preview.py b/latent_preview.py
index 4dbcbf455..12927647a 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -63,8 +63,6 @@ def get_previewer(device, latent_format):
 
         if method == LatentPreviewMethod.Auto:
             method = LatentPreviewMethod.Latent2RGB
-            if taesd_decoder_path:
-                method = LatentPreviewMethod.TAESD
 
         if method == LatentPreviewMethod.TAESD:
             if taesd_decoder_path:

From 4ae1515f14d338a347fdf8f647e062f5bc17d196 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 19 May 2024 17:42:35 -0400
Subject: [PATCH 026/315] Slightly faster latent2rgb previews.

---
 latent_preview.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/latent_preview.py b/latent_preview.py
index 12927647a..05d750e2c 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -37,12 +37,13 @@ class Latent2RGBPreviewer(LatentPreviewer):
         self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
 
     def decode_latent_to_preview(self, x0):
-        latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors
+        self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
+        latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
 
         latents_ubyte = (((latent_image + 1) / 2)
                             .clamp(0, 1)  # change scale from -1..1 to 0..1
                             .mul(0xFF)  # to 0..255
-                            .byte()).cpu()
+                            ).to(device="cpu", dtype=torch.uint8, non_blocking=True)
 
         return Image.fromarray(latents_ubyte.numpy())
 

From 11a2ad5110a96ac5895e889d455c227d1b80dd30 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 19 May 2024 17:58:03 -0400
Subject: [PATCH 027/315] Fix controlnet not upcasting on models that have it
 enabled.

---
 comfy/cldm/cldm.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index 5eee5a51c..28076dd92 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -52,6 +52,7 @@ class ControlNet(nn.Module):
         adm_in_channels=None,
         transformer_depth_middle=None,
         transformer_depth_output=None,
+        attn_precision=None,
         device=None,
         operations=comfy.ops.disable_weight_init,
         **kwargs,
@@ -202,7 +203,7 @@ class ControlNet(nn.Module):
                             SpatialTransformer(
                                 ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
                                 disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
+                                use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
                             )
                         )
                 self.input_blocks.append(TimestepEmbedSequential(*layers))
@@ -262,7 +263,7 @@ class ControlNet(nn.Module):
             mid_block += [SpatialTransformer(  # always uses a self-attn
                             ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
                             disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
+                            use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
                         ),
             ResBlock(
                 ch,

From 09e069ae6ca700cabb885662a83a05227b4f79a5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 20 May 2024 06:22:29 -0400
Subject: [PATCH 028/315] Log the pytorch version.

---
 comfy/model_management.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 21ae8d291..b21928628 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -120,6 +120,11 @@ total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
 total_ram = psutil.virtual_memory().total / (1024 * 1024)
 logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
 
+try:
+    logging.info("pytorch version: {}".format(torch.version.__version__))
+except:
+    pass
+
 try:
     OOM_EXCEPTION = torch.cuda.OutOfMemoryError
 except:

From 4bc1884478a14dea2d64a34f9beb1c52a2d16b09 Mon Sep 17 00:00:00 2001
From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com>
Date: Mon, 20 May 2024 19:58:46 +0900
Subject: [PATCH 029/315] Provide a better error message when attempting to
 execute the workflow with a missing node. (#3517)

---
 execution.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/execution.py b/execution.py
index 47d58b9d7..86bde1c46 100644
--- a/execution.py
+++ b/execution.py
@@ -622,8 +622,17 @@ def full_type_name(klass):
 def validate_prompt(prompt):
     outputs = set()
     for x in prompt:
+        if 'class_type' not in prompt[x]:
+            error = {
+                "type": "invalid_prompt",
+                "message": f"Cannot execute due to a missing node",
+                "details": f"Node ID '#{x}'",
+                "extra_info": {}
+            }
+            return (False, error, [], [])
+
         class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
-        if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True:
+        if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
             outputs.add(x)
 
     if len(outputs) == 0:

From 276f8fce9f5a80b500947fb5745a4dde9e84622d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 20 May 2024 07:03:06 -0400
Subject: [PATCH 030/315] Print error when node is missing.

---
 execution.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/execution.py b/execution.py
index 86bde1c46..76225a962 100644
--- a/execution.py
+++ b/execution.py
@@ -625,13 +625,23 @@ def validate_prompt(prompt):
         if 'class_type' not in prompt[x]:
             error = {
                 "type": "invalid_prompt",
-                "message": f"Cannot execute due to a missing node",
+                "message": f"Cannot execute because a node is missing the class_type property.",
+                "details": f"Node ID '#{x}'",
+                "extra_info": {}
+            }
+            return (False, error, [], [])
+
+        class_type = prompt[x]['class_type']
+        class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None)
+        if class_ is None:
+            error = {
+                "type": "invalid_prompt",
+                "message": f"Cannot execute because node {class_type} does not exist.",
                 "details": f"Node ID '#{x}'",
                 "extra_info": {}
             }
             return (False, error, [], [])
 
-        class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
         if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
             outputs.add(x)
 

From 1900e5119f70d6db0677fe91194050be3c4476c4 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 20 May 2024 08:19:54 -0400
Subject: [PATCH 031/315] Fix potential issue.

---
 comfy/ldm/modules/attention.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index aa74b6323..74a2fd99a 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -625,7 +625,7 @@ class SpatialTransformer(nn.Module):
         x = self.norm(x)
         if not self.use_linear:
             x = self.proj_in(x)
-        x = x.movedim(1, -1).flatten(1, 2).contiguous()
+        x = x.movedim(1, 3).flatten(1, 2).contiguous()
         if self.use_linear:
             x = self.proj_in(x)
         for i, block in enumerate(self.transformer_blocks):
@@ -633,7 +633,7 @@ class SpatialTransformer(nn.Module):
             x = block(x, context=context[i], transformer_options=transformer_options)
         if self.use_linear:
             x = self.proj_out(x)
-        x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(-1, 1).contiguous()
+        x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous()
         if not self.use_linear:
             x = self.proj_out(x)
         return x + x_in

From 83d969e3975d340ef980db59b07954a67d08ce6f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 21 May 2024 13:55:49 -0400
Subject: [PATCH 032/315] Disable xformers when tracing model.

---
 comfy/ldm/modules/attention.py | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 74a2fd99a..2ce99d460 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -313,9 +313,19 @@ except:
 def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
     b, _, dim_head = q.shape
     dim_head //= heads
+
+    disabled_xformers = False
+
     if BROKEN_XFORMERS:
         if b * heads > 65535:
-            return attention_pytorch(q, k, v, heads, mask)
+            disabled_xformers = True
+
+    if not disabled_xformers:
+        if torch.jit.is_tracing() or torch.jit.is_scripting():
+            disabled_xformers = True
+
+    if disabled_xformers:
+        return attention_pytorch(q, k, v, heads, mask)
 
     q, k, v = map(
         lambda t: t.reshape(b, -1, heads, dim_head),

From 8508df25691b0c9213049ab0d723610d3d8f9136 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 21 May 2024 16:56:33 -0400
Subject: [PATCH 033/315] Work around black image bug on Mac 14.5 by forcing
 attention upcasting.

---
 comfy/ldm/modules/attention.py |  5 +++--
 comfy/model_management.py      | 13 +++++++++++++
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 2ce99d460..93c944589 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -19,12 +19,13 @@ from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
+FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype()
 
 def get_attn_precision(attn_precision):
     if args.dont_upcast_attention:
         return None
-    if attn_precision is None and args.force_upcast_attention:
-        return torch.float32
+    if FORCE_UPCAST_ATTENTION_DTYPE is not None:
+        return FORCE_UPCAST_ATTENTION_DTYPE
     return attn_precision
 
 def exists(val):
diff --git a/comfy/model_management.py b/comfy/model_management.py
index b21928628..fbfbb7e4c 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -5,6 +5,7 @@ from comfy.cli_args import args
 import comfy.utils
 import torch
 import sys
+import platform
 
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
@@ -685,6 +686,18 @@ def pytorch_attention_flash_attention():
             return True
     return False
 
+def force_upcast_attention_dtype():
+    upcast = args.force_upcast_attention
+    try:
+        if platform.mac_ver()[0] in ['14.5']: #black image bug on OSX Sonoma 14.5
+            upcast = True
+    except:
+        pass
+    if upcast:
+        return torch.float32
+    else:
+        return None
+
 def get_free_memory(dev=None, torch_free_too=False):
     global directml_enabled
     if dev is None:

From 7718ada4eddf101d088b69e159011e4108286b5b Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 22 May 2024 02:07:27 -0400
Subject: [PATCH 034/315] Add type annotation UnetWrapperFunction (#3531)

* Add type annotation UnetWrapperFunction

* nit

* Add types.py
---
 comfy/model_patcher.py |  4 +++-
 comfy/types.py         | 32 ++++++++++++++++++++++++++++++++
 2 files changed, 35 insertions(+), 1 deletion(-)
 create mode 100644 comfy/types.py

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 35ede5eef..c397ee51c 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -6,6 +6,8 @@ import uuid
 
 import comfy.utils
 import comfy.model_management
+from comfy.types import UnetWrapperFunction
+
 
 def apply_weight_decompose(dora_scale, weight):
     weight_norm = (
@@ -117,7 +119,7 @@ class ModelPatcher:
         if disable_cfg1_optimization:
             self.model_options["disable_cfg1_optimization"] = True
 
-    def set_model_unet_function_wrapper(self, unet_wrapper_function):
+    def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
         self.model_options["model_function_wrapper"] = unet_wrapper_function
 
     def set_model_denoise_mask_function(self, denoise_mask_function):
diff --git a/comfy/types.py b/comfy/types.py
new file mode 100644
index 000000000..a8a3d29fd
--- /dev/null
+++ b/comfy/types.py
@@ -0,0 +1,32 @@
+import torch
+from typing import Callable, Protocol, TypedDict, Optional, List
+
+
+class UnetApplyFunction(Protocol):
+    """Function signature protocol on comfy.model_base.BaseModel.apply_model"""
+
+    def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor:
+        pass
+
+
+class UnetApplyConds(TypedDict):
+    """Optional conditions for unet apply function."""
+
+    c_concat: Optional[torch.Tensor]
+    c_crossattn: Optional[torch.Tensor]
+    control: Optional[torch.Tensor]
+    transformer_options: Optional[dict]
+
+
+class UnetParams(TypedDict):
+    # Tensor of shape [B, C, H, W]
+    input: torch.Tensor
+    # Tensor of shape [B]
+    timestep: torch.Tensor
+    c: UnetApplyConds
+    # List of [0, 1], [0], [1], ...
+    # 0 means unconditional, 1 means conditional
+    cond_or_uncond: List[int]
+
+
+UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor]

From 6c23854f54edb51643026b7b10d1ae9e34cfd3d6 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 22 May 2024 13:56:28 -0400
Subject: [PATCH 035/315] Fix OSX latent2rgb previews.

---
 comfy/model_management.py | 10 ++++++++--
 comfy/ops.py              |  2 +-
 latent_preview.py         |  3 ++-
 3 files changed, 11 insertions(+), 4 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index fbfbb7e4c..ef36a2c48 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -630,8 +630,14 @@ def supports_dtype(device, dtype): #TODO
 def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
+    return True
+
+def device_should_use_non_blocking(device):
+    if not device_supports_non_blocking(device):
+        return False
     return False
-    # return True #TODO: figure out why this causes issues
+    # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others
+
 
 def cast_to_device(tensor, device, dtype, copy=False):
     device_supports_cast = False
@@ -643,7 +649,7 @@ def cast_to_device(tensor, device, dtype, copy=False):
         elif is_intel_xpu():
             device_supports_cast = True
 
-    non_blocking = device_supports_non_blocking(device)
+    non_blocking = device_should_use_non_blocking(device)
 
     if device_supports_cast:
         if copy:
diff --git a/comfy/ops.py b/comfy/ops.py
index eb6507682..7ebb3dd2f 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -21,7 +21,7 @@ import comfy.model_management
 
 def cast_bias_weight(s, input):
     bias = None
-    non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
+    non_blocking = comfy.model_management.device_should_use_non_blocking(input.device)
     if s.bias is not None:
         bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
         if s.bias_function is not None:
diff --git a/latent_preview.py b/latent_preview.py
index 05d750e2c..dae9beb60 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -4,6 +4,7 @@ import struct
 import numpy as np
 from comfy.cli_args import args, LatentPreviewMethod
 from comfy.taesd.taesd import TAESD
+import comfy.model_management
 import folder_paths
 import comfy.utils
 import logging
@@ -43,7 +44,7 @@ class Latent2RGBPreviewer(LatentPreviewer):
         latents_ubyte = (((latent_image + 1) / 2)
                             .clamp(0, 1)  # change scale from -1..1 to 0..1
                             .mul(0xFF)  # to 0..255
-                            ).to(device="cpu", dtype=torch.uint8, non_blocking=True)
+                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
 
         return Image.fromarray(latents_ubyte.numpy())
 

From 6507a9c71691102fcba3d3a6adcf68303cb33895 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 23 May 2024 01:29:22 -0400
Subject: [PATCH 036/315] Remove the CTRL-Delete keybind.

On some keyboards it's apparently too easy to accidentally do CTRL-Delete
when pressing CTRL-Enter repeatedly.

CTRL-Backspace can still be used to clear the workflow.
---
 README.md                       | 2 +-
 web/extensions/core/keybinds.js | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 80de21bcd..cf32014b2 100644
--- a/README.md
+++ b/README.md
@@ -53,7 +53,7 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 | Ctrl + M                           | Mute/unmute selected nodes                                                                                         |
 | Ctrl + B                           | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
 | Delete/Backspace                   | Delete selected nodes                                                                                              |
-| Ctrl + Delete/Backspace            | Delete the current graph                                                                                           |
+| Ctrl + Backspace                   | Delete the current graph                                                                                           |
 | Space                              | Move the canvas around when held and moving the cursor                                                             |
 | Ctrl/Shift + Click                 | Add clicked node to selection                                                                                      |
 | Ctrl + C/Ctrl + V                  | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
diff --git a/web/extensions/core/keybinds.js b/web/extensions/core/keybinds.js
index cf698ea5a..ac367c116 100644
--- a/web/extensions/core/keybinds.js
+++ b/web/extensions/core/keybinds.js
@@ -21,7 +21,6 @@ app.registerExtension({
 				s: "#comfy-save-button",
 				o: "#comfy-file-input",
 				Backspace: "#comfy-clear-button",
-				Delete: "#comfy-clear-button",
 				d: "#comfy-load-default-button",
 			};
 

From b02bcced05dcc2d09a0358eff6393c5641485878 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 23 May 2024 11:47:43 -0400
Subject: [PATCH 037/315] Fix FreeU not working when shape is tensor.

---
 comfy_extras/nodes_freelunch.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py
index 6f1d87bf3..c5ebcf26f 100644
--- a/comfy_extras/nodes_freelunch.py
+++ b/comfy_extras/nodes_freelunch.py
@@ -42,7 +42,7 @@ class FreeU:
         on_cpu_devices = {}
 
         def output_block_patch(h, hsp, transformer_options):
-            scale = scale_dict.get(h.shape[1], None)
+            scale = scale_dict.get(int(h.shape[1]), None)
             if scale is not None:
                 h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]
                 if hsp.device not in on_cpu_devices:
@@ -81,7 +81,7 @@ class FreeU_V2:
         on_cpu_devices = {}
 
         def output_block_patch(h, hsp, transformer_options):
-            scale = scale_dict.get(h.shape[1], None)
+            scale = scale_dict.get(int(h.shape[1]), None)
             if scale is not None:
                 hidden_mean = h.mean(1).unsqueeze(1)
                 B = hidden_mean.shape[0]

From 58c9838274f53c6aa8912992db9f73e9a0721227 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 24 May 2024 02:37:57 -0400
Subject: [PATCH 038/315] Speed up TAESD preview.

---
 latent_preview.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/latent_preview.py b/latent_preview.py
index dae9beb60..b258fcf20 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -25,9 +25,8 @@ class TAESDPreviewerImpl(LatentPreviewer):
 
     def decode_latent_to_preview(self, x0):
         x_sample = self.taesd.decode(x0[:1])[0].detach()
-        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
+        x_sample = 255. * torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
+        x_sample = np.moveaxis(x_sample.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(x_sample.device)).numpy(), 0, 2)
 
         preview_image = Image.fromarray(x_sample)
         return preview_image

From efa5a711b20c4544fe7377cc85d9f40d7d180b37 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 24 May 2024 23:36:48 -0400
Subject: [PATCH 039/315] Reduce memory usage when applying DORA: #3557

---
 comfy/model_patcher.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index c397ee51c..78982d795 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -9,7 +9,7 @@ import comfy.model_management
 from comfy.types import UnetWrapperFunction
 
 
-def apply_weight_decompose(dora_scale, weight):
+def weight_decompose_scale(dora_scale, weight):
     weight_norm = (
         weight.transpose(0, 1)
         .reshape(weight.shape[1], -1)
@@ -18,7 +18,7 @@ def apply_weight_decompose(dora_scale, weight):
         .transpose(0, 1)
     )
 
-    return weight * (dora_scale / weight_norm).type(weight.dtype)
+    return (dora_scale / weight_norm).type(weight.dtype)
 
 def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
     to = model_options["transformer_options"].copy()
@@ -365,7 +365,7 @@ class ModelPatcher:
                 try:
                     weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
                     if dora_scale is not None:
-                        weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
+                        weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "lokr":
@@ -407,7 +407,7 @@ class ModelPatcher:
                 try:
                     weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
                     if dora_scale is not None:
-                        weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
+                        weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "loha":
@@ -439,7 +439,7 @@ class ModelPatcher:
                 try:
                     weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
                     if dora_scale is not None:
-                        weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
+                        weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "glora":
@@ -456,7 +456,7 @@ class ModelPatcher:
                 try:
                     weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
                     if dora_scale is not None:
-                        weight = apply_weight_decompose(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
+                        weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             else:

From 5b8736947490ea94e219a24743ce5460816696ee Mon Sep 17 00:00:00 2001
From: DLohn <41272253+DLohn@users.noreply.github.com>
Date: Fri, 24 May 2024 20:53:15 -0700
Subject: [PATCH 040/315] Load titles from API format JSON (#3563)

---
 web/scripts/app.js | 1 +
 1 file changed, 1 insertion(+)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index a516be704..4dc011b9f 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -2238,6 +2238,7 @@ export class ComfyApp {
 			const data = apiData[id];
 			const node = LiteGraph.createNode(data.class_type);
 			node.id = isNaN(+id) ? id : +id;
+			node.title = data._meta?.title ?? node.title
 			graph.add(node);
 		}
 

From ffc4b7c30e35eb2773ace52a0b00e0ca5c1f4362 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 25 May 2024 02:31:23 -0400
Subject: [PATCH 041/315] Fix DORA strength.

This is a different version of #3298 with more correct behavior.
---
 comfy/model_patcher.py | 68 +++++++++++++++++++++++++++++-------------
 1 file changed, 48 insertions(+), 20 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 78982d795..2e746d8a9 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -9,16 +9,26 @@ import comfy.model_management
 from comfy.types import UnetWrapperFunction
 
 
-def weight_decompose_scale(dora_scale, weight):
+def weight_decompose(dora_scale, weight, lora_diff, alpha, strength):
+    dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32)
+    lora_diff *= alpha
+    weight_calc = weight + lora_diff.type(weight.dtype)
     weight_norm = (
-        weight.transpose(0, 1)
-        .reshape(weight.shape[1], -1)
+        weight_calc.transpose(0, 1)
+        .reshape(weight_calc.shape[1], -1)
         .norm(dim=1, keepdim=True)
-        .reshape(weight.shape[1], *[1] * (weight.dim() - 1))
+        .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
         .transpose(0, 1)
     )
 
-    return (dora_scale / weight_norm).type(weight.dtype)
+    weight_calc *= (dora_scale / weight_norm).type(weight.dtype)
+    if strength != 1.0:
+        weight_calc -= weight
+        weight += strength * (weight_calc)
+    else:
+        weight[:] = weight_calc
+    return weight
+
 
 def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
     to = model_options["transformer_options"].copy()
@@ -328,7 +338,7 @@ class ModelPatcher:
 
     def calculate_weight(self, patches, weight, key):
         for p in patches:
-            alpha = p[0]
+            strength = p[0]
             v = p[1]
             strength_model = p[2]
 
@@ -346,26 +356,31 @@ class ModelPatcher:
 
             if patch_type == "diff":
                 w1 = v[0]
-                if alpha != 0.0:
+                if strength != 0.0:
                     if w1.shape != weight.shape:
                         logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
                     else:
-                        weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
+                        weight += strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
             elif patch_type == "lora": #lora/locon
                 mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
                 mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
                 dora_scale = v[4]
                 if v[2] is not None:
-                    alpha *= v[2] / mat2.shape[0]
+                    alpha = v[2] / mat2.shape[0]
+                else:
+                    alpha = 1.0
+
                 if v[3] is not None:
                     #locon mid weights, hopefully the math is fine because I didn't properly test it
                     mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32)
                     final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
                     mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
                 try:
-                    weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
+                    lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
+                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                    else:
+                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "lokr":
@@ -402,19 +417,26 @@ class ModelPatcher:
                 if len(w2.shape) == 4:
                     w1 = w1.unsqueeze(2).unsqueeze(2)
                 if v[2] is not None and dim is not None:
-                    alpha *= v[2] / dim
+                    alpha = v[2] / dim
+                else:
+                    alpha = 1.0
 
                 try:
-                    weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
+                    lora_diff = torch.kron(w1, w2).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
+                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                    else:
+                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "loha":
                 w1a = v[0]
                 w1b = v[1]
                 if v[2] is not None:
-                    alpha *= v[2] / w1b.shape[0]
+                    alpha = v[2] / w1b.shape[0]
+                else:
+                    alpha = 1.0
+
                 w2a = v[3]
                 w2b = v[4]
                 dora_scale = v[7]
@@ -437,14 +459,18 @@ class ModelPatcher:
                                   comfy.model_management.cast_to_device(w2b, weight.device, torch.float32))
 
                 try:
-                    weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
+                    lora_diff = (m1 * m2).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
+                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                    else:
+                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "glora":
                 if v[4] is not None:
-                    alpha *= v[4] / v[0].shape[0]
+                    alpha = v[4] / v[0].shape[0]
+                else:
+                    alpha = 1.0
 
                 dora_scale = v[5]
 
@@ -454,9 +480,11 @@ class ModelPatcher:
                 b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
 
                 try:
-                    weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
+                    lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight *= weight_decompose_scale(comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32), weight)
+                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                    else:
+                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             else:

From 8cfd677cc0add9048ca9c085a8b79afbbc70bd91 Mon Sep 17 00:00:00 2001
From: Joey Ballentine <34788790+joeyballentine@users.noreply.github.com>
Date: Sun, 26 May 2024 13:44:17 -0400
Subject: [PATCH 042/315] Replace chainner_models with Spandrel package (#2146)

* Replace chainner_models with Spandrel

* Update to latest spandrel

* Use spandrel_foss instead

* update spandrel to new FOSS-compliant version
---
 comfy_extras/chainner_models/__init__.py      |    0
 .../chainner_models/architecture/DAT.py       | 1182 --------------
 .../chainner_models/architecture/HAT.py       | 1277 ---------------
 .../chainner_models/architecture/LICENSE-DAT  |  201 ---
 .../architecture/LICENSE-ESRGAN               |  201 ---
 .../chainner_models/architecture/LICENSE-HAT  |   21 -
 .../architecture/LICENSE-RealESRGAN           |   29 -
 .../architecture/LICENSE-SCUNet               |  201 ---
 .../chainner_models/architecture/LICENSE-SPSR |  201 ---
 .../architecture/LICENSE-SwiftSRGAN           |  121 --
 .../architecture/LICENSE-Swin2SR              |  201 ---
 .../architecture/LICENSE-SwinIR               |  201 ---
 .../chainner_models/architecture/LICENSE-lama |  201 ---
 .../chainner_models/architecture/LaMa.py      |  694 ---------
 .../architecture/OmniSR/ChannelAttention.py   |  110 --
 .../architecture/OmniSR/LICENSE               |  201 ---
 .../architecture/OmniSR/OSA.py                |  577 -------
 .../architecture/OmniSR/OSAG.py               |   60 -
 .../architecture/OmniSR/OmniSR.py             |  143 --
 .../architecture/OmniSR/esa.py                |  294 ----
 .../architecture/OmniSR/layernorm.py          |   70 -
 .../architecture/OmniSR/pixelshuffle.py       |   31 -
 .../chainner_models/architecture/RRDB.py      |  296 ----
 .../chainner_models/architecture/SCUNet.py    |  455 ------
 .../chainner_models/architecture/SPSR.py      |  383 -----
 .../chainner_models/architecture/SRVGG.py     |  114 --
 .../architecture/SwiftSRGAN.py                |  161 --
 .../chainner_models/architecture/Swin2SR.py   | 1377 -----------------
 .../chainner_models/architecture/SwinIR.py    | 1224 ---------------
 .../chainner_models/architecture/__init__.py  |    0
 .../chainner_models/architecture/block.py     |  546 -------
 .../architecture/face/LICENSE-GFPGAN          |  351 -----
 .../architecture/face/LICENSE-RestoreFormer   |  351 -----
 .../architecture/face/LICENSE-codeformer      |   35 -
 .../architecture/face/arcface_arch.py         |  265 ----
 .../architecture/face/codeformer.py           |  790 ----------
 .../architecture/face/fused_act.py            |   81 -
 .../architecture/face/gfpgan_bilinear_arch.py |  389 -----
 .../architecture/face/gfpganv1_arch.py        |  566 -------
 .../architecture/face/gfpganv1_clean_arch.py  |  370 -----
 .../architecture/face/restoreformer_arch.py   |  776 ----------
 .../architecture/face/stylegan2_arch.py       |  865 -----------
 .../face/stylegan2_bilinear_arch.py           |  709 ---------
 .../architecture/face/stylegan2_clean_arch.py |  453 ------
 .../architecture/face/upfirdn2d.py            |  194 ---
 .../chainner_models/architecture/timm/LICENSE |  201 ---
 .../chainner_models/architecture/timm/drop.py |  223 ---
 .../architecture/timm/helpers.py              |   31 -
 .../architecture/timm/weight_init.py          |  128 --
 comfy_extras/chainner_models/model_loading.py |   99 --
 comfy_extras/chainner_models/types.py         |   69 -
 comfy_extras/nodes_upscale_model.py           |   10 +-
 requirements.txt                              |    1 +
 53 files changed, 8 insertions(+), 17722 deletions(-)
 delete mode 100644 comfy_extras/chainner_models/__init__.py
 delete mode 100644 comfy_extras/chainner_models/architecture/DAT.py
 delete mode 100644 comfy_extras/chainner_models/architecture/HAT.py
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-DAT
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-ESRGAN
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-HAT
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SCUNet
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SPSR
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-Swin2SR
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-SwinIR
 delete mode 100644 comfy_extras/chainner_models/architecture/LICENSE-lama
 delete mode 100644 comfy_extras/chainner_models/architecture/LaMa.py
 delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py
 delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/LICENSE
 delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/OSA.py
 delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/OSAG.py
 delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py
 delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/esa.py
 delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/layernorm.py
 delete mode 100644 comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py
 delete mode 100644 comfy_extras/chainner_models/architecture/RRDB.py
 delete mode 100644 comfy_extras/chainner_models/architecture/SCUNet.py
 delete mode 100644 comfy_extras/chainner_models/architecture/SPSR.py
 delete mode 100644 comfy_extras/chainner_models/architecture/SRVGG.py
 delete mode 100644 comfy_extras/chainner_models/architecture/SwiftSRGAN.py
 delete mode 100644 comfy_extras/chainner_models/architecture/Swin2SR.py
 delete mode 100644 comfy_extras/chainner_models/architecture/SwinIR.py
 delete mode 100644 comfy_extras/chainner_models/architecture/__init__.py
 delete mode 100644 comfy_extras/chainner_models/architecture/block.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN
 delete mode 100644 comfy_extras/chainner_models/architecture/face/LICENSE-RestoreFormer
 delete mode 100644 comfy_extras/chainner_models/architecture/face/LICENSE-codeformer
 delete mode 100644 comfy_extras/chainner_models/architecture/face/arcface_arch.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/codeformer.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/fused_act.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/restoreformer_arch.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/stylegan2_arch.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py
 delete mode 100644 comfy_extras/chainner_models/architecture/face/upfirdn2d.py
 delete mode 100644 comfy_extras/chainner_models/architecture/timm/LICENSE
 delete mode 100644 comfy_extras/chainner_models/architecture/timm/drop.py
 delete mode 100644 comfy_extras/chainner_models/architecture/timm/helpers.py
 delete mode 100644 comfy_extras/chainner_models/architecture/timm/weight_init.py
 delete mode 100644 comfy_extras/chainner_models/model_loading.py
 delete mode 100644 comfy_extras/chainner_models/types.py

diff --git a/comfy_extras/chainner_models/__init__.py b/comfy_extras/chainner_models/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/comfy_extras/chainner_models/architecture/DAT.py b/comfy_extras/chainner_models/architecture/DAT.py
deleted file mode 100644
index 0bcc26ef4..000000000
--- a/comfy_extras/chainner_models/architecture/DAT.py
+++ /dev/null
@@ -1,1182 +0,0 @@
-# pylint: skip-file
-import math
-import re
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint as checkpoint
-from einops import rearrange
-from einops.layers.torch import Rearrange
-from torch import Tensor
-from torch.nn import functional as F
-
-from .timm.drop import DropPath
-from .timm.weight_init import trunc_normal_
-
-
-def img2windows(img, H_sp, W_sp):
-    """
-    Input: Image (B, C, H, W)
-    Output: Window Partition (B', N, C)
-    """
-    B, C, H, W = img.shape
-    img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp)
-    img_perm = (
-        img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C)
-    )
-    return img_perm
-
-
-def windows2img(img_splits_hw, H_sp, W_sp, H, W):
-    """
-    Input: Window Partition (B', N, C)
-    Output: Image (B, H, W, C)
-    """
-    B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp))
-
-    img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1)
-    img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return img
-
-
-class SpatialGate(nn.Module):
-    """Spatial-Gate.
-    Args:
-        dim (int): Half of input channels.
-    """
-
-    def __init__(self, dim):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim)
-        self.conv = nn.Conv2d(
-            dim, dim, kernel_size=3, stride=1, padding=1, groups=dim
-        )  # DW Conv
-
-    def forward(self, x, H, W):
-        # Split
-        x1, x2 = x.chunk(2, dim=-1)
-        B, N, C = x.shape
-        x2 = (
-            self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W))
-            .flatten(2)
-            .transpose(-1, -2)
-            .contiguous()
-        )
-
-        return x1 * x2
-
-
-class SGFN(nn.Module):
-    """Spatial-Gate Feed-Forward Network.
-    Args:
-        in_features (int): Number of input channels.
-        hidden_features (int | None): Number of hidden channels. Default: None
-        out_features (int | None): Number of output channels. Default: None
-        act_layer (nn.Module): Activation layer. Default: nn.GELU
-        drop (float): Dropout rate. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.sg = SpatialGate(hidden_features // 2)
-        self.fc2 = nn.Linear(hidden_features // 2, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x, H, W):
-        """
-        Input: x: (B, H*W, C), H, W
-        Output: x: (B, H*W, C)
-        """
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-
-        x = self.sg(x, H, W)
-        x = self.drop(x)
-
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class DynamicPosBias(nn.Module):
-    # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py
-    """Dynamic Relative Position Bias.
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        residual (bool):  If True, use residual strage to connect conv.
-    """
-
-    def __init__(self, dim, num_heads, residual):
-        super().__init__()
-        self.residual = residual
-        self.num_heads = num_heads
-        self.pos_dim = dim // 4
-        self.pos_proj = nn.Linear(2, self.pos_dim)
-        self.pos1 = nn.Sequential(
-            nn.LayerNorm(self.pos_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.pos_dim, self.pos_dim),
-        )
-        self.pos2 = nn.Sequential(
-            nn.LayerNorm(self.pos_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.pos_dim, self.pos_dim),
-        )
-        self.pos3 = nn.Sequential(
-            nn.LayerNorm(self.pos_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.pos_dim, self.num_heads),
-        )
-
-    def forward(self, biases):
-        if self.residual:
-            pos = self.pos_proj(biases)  # 2Gh-1 * 2Gw-1, heads
-            pos = pos + self.pos1(pos)
-            pos = pos + self.pos2(pos)
-            pos = self.pos3(pos)
-        else:
-            pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases))))
-        return pos
-
-
-class Spatial_Attention(nn.Module):
-    """Spatial Window Self-Attention.
-    It supports rectangle window (containing square window).
-    Args:
-        dim (int): Number of input channels.
-        idx (int): The indentix of window. (0/1)
-        split_size (tuple(int)): Height and Width of spatial window.
-        dim_out (int | None): The dimension of the attention output. Default: None
-        num_heads (int): Number of attention heads. Default: 6
-        attn_drop (float): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float): Dropout ratio of output. Default: 0.0
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set
-        position_bias (bool): The dynamic relative position bias. Default: True
-    """
-
-    def __init__(
-        self,
-        dim,
-        idx,
-        split_size=[8, 8],
-        dim_out=None,
-        num_heads=6,
-        attn_drop=0.0,
-        proj_drop=0.0,
-        qk_scale=None,
-        position_bias=True,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.dim_out = dim_out or dim
-        self.split_size = split_size
-        self.num_heads = num_heads
-        self.idx = idx
-        self.position_bias = position_bias
-
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        if idx == 0:
-            H_sp, W_sp = self.split_size[0], self.split_size[1]
-        elif idx == 1:
-            W_sp, H_sp = self.split_size[0], self.split_size[1]
-        else:
-            print("ERROR MODE", idx)
-            exit(0)
-        self.H_sp = H_sp
-        self.W_sp = W_sp
-
-        if self.position_bias:
-            self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
-            # generate mother-set
-            position_bias_h = torch.arange(1 - self.H_sp, self.H_sp)
-            position_bias_w = torch.arange(1 - self.W_sp, self.W_sp)
-            biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))
-            biases = biases.flatten(1).transpose(0, 1).contiguous().float()
-            self.register_buffer("rpe_biases", biases)
-
-            # get pair-wise relative position index for each token inside the window
-            coords_h = torch.arange(self.H_sp)
-            coords_w = torch.arange(self.W_sp)
-            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
-            coords_flatten = torch.flatten(coords, 1)
-            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
-            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
-            relative_coords[:, :, 0] += self.H_sp - 1
-            relative_coords[:, :, 1] += self.W_sp - 1
-            relative_coords[:, :, 0] *= 2 * self.W_sp - 1
-            relative_position_index = relative_coords.sum(-1)
-            self.register_buffer("relative_position_index", relative_position_index)
-
-        self.attn_drop = nn.Dropout(attn_drop)
-
-    def im2win(self, x, H, W):
-        B, N, C = x.shape
-        x = x.transpose(-2, -1).contiguous().view(B, C, H, W)
-        x = img2windows(x, self.H_sp, self.W_sp)
-        x = (
-            x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads)
-            .permute(0, 2, 1, 3)
-            .contiguous()
-        )
-        return x
-
-    def forward(self, qkv, H, W, mask=None):
-        """
-        Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size
-        Output: x (B, H, W, C)
-        """
-        q, k, v = qkv[0], qkv[1], qkv[2]
-
-        B, L, C = q.shape
-        assert L == H * W, "flatten img_tokens has wrong size"
-
-        # partition the q,k,v, image to window
-        q = self.im2win(q, H, W)
-        k = self.im2win(k, H, W)
-        v = self.im2win(v, H, W)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)  # B head N C @ B head C N --> B head N N
-
-        # calculate drpe
-        if self.position_bias:
-            pos = self.pos(self.rpe_biases)
-            # select position bias
-            relative_position_bias = pos[self.relative_position_index.view(-1)].view(
-                self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1
-            )
-            relative_position_bias = relative_position_bias.permute(
-                2, 0, 1
-            ).contiguous()
-            attn = attn + relative_position_bias.unsqueeze(0)
-
-        N = attn.shape[3]
-
-        # use mask for shift window
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(
-                0
-            )
-            attn = attn.view(-1, self.num_heads, N, N)
-
-        attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype)
-        attn = self.attn_drop(attn)
-
-        x = attn @ v
-        x = x.transpose(1, 2).reshape(
-            -1, self.H_sp * self.W_sp, C
-        )  # B head N N @ B head N C
-
-        # merge the window, window to image
-        x = windows2img(x, self.H_sp, self.W_sp, H, W)  # B H' W' C
-
-        return x
-
-
-class Adaptive_Spatial_Attention(nn.Module):
-    # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT
-    """Adaptive Spatial Self-Attention
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads. Default: 6
-        split_size (tuple(int)): Height and Width of spatial window.
-        shift_size (tuple(int)): Shift size for spatial window.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float): Dropout rate. Default: 0.0
-        attn_drop (float): Attention dropout rate. Default: 0.0
-        rg_idx (int): The indentix of Residual Group (RG)
-        b_idx (int): The indentix of Block in each RG
-    """
-
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        reso=64,
-        split_size=[8, 8],
-        shift_size=[1, 2],
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        rg_idx=0,
-        b_idx=0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.split_size = split_size
-        self.shift_size = shift_size
-        self.b_idx = b_idx
-        self.rg_idx = rg_idx
-        self.patches_resolution = reso
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-
-        assert (
-            0 <= self.shift_size[0] < self.split_size[0]
-        ), "shift_size must in 0-split_size0"
-        assert (
-            0 <= self.shift_size[1] < self.split_size[1]
-        ), "shift_size must in 0-split_size1"
-
-        self.branch_num = 2
-
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(drop)
-
-        self.attns = nn.ModuleList(
-            [
-                Spatial_Attention(
-                    dim // 2,
-                    idx=i,
-                    split_size=split_size,
-                    num_heads=num_heads // 2,
-                    dim_out=dim // 2,
-                    qk_scale=qk_scale,
-                    attn_drop=attn_drop,
-                    proj_drop=drop,
-                    position_bias=True,
-                )
-                for i in range(self.branch_num)
-            ]
-        )
-
-        if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or (
-            self.rg_idx % 2 != 0 and self.b_idx % 4 == 0
-        ):
-            attn_mask = self.calculate_mask(
-                self.patches_resolution, self.patches_resolution
-            )
-            self.register_buffer("attn_mask_0", attn_mask[0])
-            self.register_buffer("attn_mask_1", attn_mask[1])
-        else:
-            attn_mask = None
-            self.register_buffer("attn_mask_0", None)
-            self.register_buffer("attn_mask_1", None)
-
-        self.dwconv = nn.Sequential(
-            nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim),
-            nn.BatchNorm2d(dim),
-            nn.GELU(),
-        )
-        self.channel_interaction = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(dim, dim // 8, kernel_size=1),
-            nn.BatchNorm2d(dim // 8),
-            nn.GELU(),
-            nn.Conv2d(dim // 8, dim, kernel_size=1),
-        )
-        self.spatial_interaction = nn.Sequential(
-            nn.Conv2d(dim, dim // 16, kernel_size=1),
-            nn.BatchNorm2d(dim // 16),
-            nn.GELU(),
-            nn.Conv2d(dim // 16, 1, kernel_size=1),
-        )
-
-    def calculate_mask(self, H, W):
-        # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
-        # calculate attention mask for shift window
-        img_mask_0 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=0
-        img_mask_1 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=1
-        h_slices_0 = (
-            slice(0, -self.split_size[0]),
-            slice(-self.split_size[0], -self.shift_size[0]),
-            slice(-self.shift_size[0], None),
-        )
-        w_slices_0 = (
-            slice(0, -self.split_size[1]),
-            slice(-self.split_size[1], -self.shift_size[1]),
-            slice(-self.shift_size[1], None),
-        )
-
-        h_slices_1 = (
-            slice(0, -self.split_size[1]),
-            slice(-self.split_size[1], -self.shift_size[1]),
-            slice(-self.shift_size[1], None),
-        )
-        w_slices_1 = (
-            slice(0, -self.split_size[0]),
-            slice(-self.split_size[0], -self.shift_size[0]),
-            slice(-self.shift_size[0], None),
-        )
-        cnt = 0
-        for h in h_slices_0:
-            for w in w_slices_0:
-                img_mask_0[:, h, w, :] = cnt
-                cnt += 1
-        cnt = 0
-        for h in h_slices_1:
-            for w in w_slices_1:
-                img_mask_1[:, h, w, :] = cnt
-                cnt += 1
-
-        # calculate mask for window-0
-        img_mask_0 = img_mask_0.view(
-            1,
-            H // self.split_size[0],
-            self.split_size[0],
-            W // self.split_size[1],
-            self.split_size[1],
-            1,
-        )
-        img_mask_0 = (
-            img_mask_0.permute(0, 1, 3, 2, 4, 5)
-            .contiguous()
-            .view(-1, self.split_size[0], self.split_size[1], 1)
-        )  # nW, sw[0], sw[1], 1
-        mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1])
-        attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2)
-        attn_mask_0 = attn_mask_0.masked_fill(
-            attn_mask_0 != 0, float(-100.0)
-        ).masked_fill(attn_mask_0 == 0, float(0.0))
-
-        # calculate mask for window-1
-        img_mask_1 = img_mask_1.view(
-            1,
-            H // self.split_size[1],
-            self.split_size[1],
-            W // self.split_size[0],
-            self.split_size[0],
-            1,
-        )
-        img_mask_1 = (
-            img_mask_1.permute(0, 1, 3, 2, 4, 5)
-            .contiguous()
-            .view(-1, self.split_size[1], self.split_size[0], 1)
-        )  # nW, sw[1], sw[0], 1
-        mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0])
-        attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2)
-        attn_mask_1 = attn_mask_1.masked_fill(
-            attn_mask_1 != 0, float(-100.0)
-        ).masked_fill(attn_mask_1 == 0, float(0.0))
-
-        return attn_mask_0, attn_mask_1
-
-    def forward(self, x, H, W):
-        """
-        Input: x: (B, H*W, C), H, W
-        Output: x: (B, H*W, C)
-        """
-        B, L, C = x.shape
-        assert L == H * W, "flatten img_tokens has wrong size"
-
-        qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3)  # 3, B, HW, C
-        # V without partition
-        v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W)
-
-        # image padding
-        max_split_size = max(self.split_size[0], self.split_size[1])
-        pad_l = pad_t = 0
-        pad_r = (max_split_size - W % max_split_size) % max_split_size
-        pad_b = (max_split_size - H % max_split_size) % max_split_size
-
-        qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2)  # 3B C H W
-        qkv = (
-            F.pad(qkv, (pad_l, pad_r, pad_t, pad_b))
-            .reshape(3, B, C, -1)
-            .transpose(-2, -1)
-        )  # l r t b
-        _H = pad_b + H
-        _W = pad_r + W
-        _L = _H * _W
-
-        # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged
-        # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ...
-        if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or (
-            self.rg_idx % 2 != 0 and self.b_idx % 4 == 0
-        ):
-            qkv = qkv.view(3, B, _H, _W, C)
-            qkv_0 = torch.roll(
-                qkv[:, :, :, :, : C // 2],
-                shifts=(-self.shift_size[0], -self.shift_size[1]),
-                dims=(2, 3),
-            )
-            qkv_0 = qkv_0.view(3, B, _L, C // 2)
-            qkv_1 = torch.roll(
-                qkv[:, :, :, :, C // 2 :],
-                shifts=(-self.shift_size[1], -self.shift_size[0]),
-                dims=(2, 3),
-            )
-            qkv_1 = qkv_1.view(3, B, _L, C // 2)
-
-            if self.patches_resolution != _H or self.patches_resolution != _W:
-                mask_tmp = self.calculate_mask(_H, _W)
-                x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device))
-                x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device))
-            else:
-                x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0)
-                x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1)
-
-            x1 = torch.roll(
-                x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2)
-            )
-            x2 = torch.roll(
-                x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2)
-            )
-            x1 = x1[:, :H, :W, :].reshape(B, L, C // 2)
-            x2 = x2[:, :H, :W, :].reshape(B, L, C // 2)
-            # attention output
-            attened_x = torch.cat([x1, x2], dim=2)
-
-        else:
-            x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape(
-                B, L, C // 2
-            )
-            x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape(
-                B, L, C // 2
-            )
-            # attention output
-            attened_x = torch.cat([x1, x2], dim=2)
-
-        # convolution output
-        conv_x = self.dwconv(v)
-
-        # Adaptive Interaction Module (AIM)
-        # C-Map (before sigmoid)
-        channel_map = (
-            self.channel_interaction(conv_x)
-            .permute(0, 2, 3, 1)
-            .contiguous()
-            .view(B, 1, C)
-        )
-        # S-Map (before sigmoid)
-        attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W)
-        spatial_map = self.spatial_interaction(attention_reshape)
-
-        # C-I
-        attened_x = attened_x * torch.sigmoid(channel_map)
-        # S-I
-        conv_x = torch.sigmoid(spatial_map) * conv_x
-        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C)
-
-        x = attened_x + conv_x
-
-        x = self.proj(x)
-        x = self.proj_drop(x)
-
-        return x
-
-
-class Adaptive_Channel_Attention(nn.Module):
-    # The implementation builds on XCiT code https://github.com/facebookresearch/xcit
-    """Adaptive Channel Self-Attention
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads. Default: 6
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set.
-        attn_drop (float): Attention dropout rate. Default: 0.0
-        drop_path (float): Stochastic depth rate. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        num_heads=8,
-        qkv_bias=False,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-        super().__init__()
-        self.num_heads = num_heads
-        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        self.dwconv = nn.Sequential(
-            nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim),
-            nn.BatchNorm2d(dim),
-            nn.GELU(),
-        )
-        self.channel_interaction = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(dim, dim // 8, kernel_size=1),
-            nn.BatchNorm2d(dim // 8),
-            nn.GELU(),
-            nn.Conv2d(dim // 8, dim, kernel_size=1),
-        )
-        self.spatial_interaction = nn.Sequential(
-            nn.Conv2d(dim, dim // 16, kernel_size=1),
-            nn.BatchNorm2d(dim // 16),
-            nn.GELU(),
-            nn.Conv2d(dim // 16, 1, kernel_size=1),
-        )
-
-    def forward(self, x, H, W):
-        """
-        Input: x: (B, H*W, C), H, W
-        Output: x: (B, H*W, C)
-        """
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
-        qkv = qkv.permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]
-
-        q = q.transpose(-2, -1)
-        k = k.transpose(-2, -1)
-        v = v.transpose(-2, -1)
-
-        v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W)
-
-        q = torch.nn.functional.normalize(q, dim=-1)
-        k = torch.nn.functional.normalize(k, dim=-1)
-
-        attn = (q @ k.transpose(-2, -1)) * self.temperature
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        # attention output
-        attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C)
-
-        # convolution output
-        conv_x = self.dwconv(v_)
-
-        # Adaptive Interaction Module (AIM)
-        # C-Map (before sigmoid)
-        attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W)
-        channel_map = self.channel_interaction(attention_reshape)
-        # S-Map (before sigmoid)
-        spatial_map = (
-            self.spatial_interaction(conv_x)
-            .permute(0, 2, 3, 1)
-            .contiguous()
-            .view(B, N, 1)
-        )
-
-        # S-I
-        attened_x = attened_x * torch.sigmoid(spatial_map)
-        # C-I
-        conv_x = conv_x * torch.sigmoid(channel_map)
-        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C)
-
-        x = attened_x + conv_x
-
-        x = self.proj(x)
-        x = self.proj_drop(x)
-
-        return x
-
-
-class DATB(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        reso=64,
-        split_size=[2, 4],
-        shift_size=[1, 2],
-        expansion_factor=4.0,
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        rg_idx=0,
-        b_idx=0,
-    ):
-        super().__init__()
-
-        self.norm1 = norm_layer(dim)
-
-        if b_idx % 2 == 0:
-            # DSTB
-            self.attn = Adaptive_Spatial_Attention(
-                dim,
-                num_heads=num_heads,
-                reso=reso,
-                split_size=split_size,
-                shift_size=shift_size,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop,
-                attn_drop=attn_drop,
-                rg_idx=rg_idx,
-                b_idx=b_idx,
-            )
-        else:
-            # DCTB
-            self.attn = Adaptive_Channel_Attention(
-                dim,
-                num_heads=num_heads,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                attn_drop=attn_drop,
-                proj_drop=drop,
-            )
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-
-        ffn_hidden_dim = int(dim * expansion_factor)
-        self.ffn = SGFN(
-            in_features=dim,
-            hidden_features=ffn_hidden_dim,
-            out_features=dim,
-            act_layer=act_layer,
-        )
-        self.norm2 = norm_layer(dim)
-
-    def forward(self, x, x_size):
-        """
-        Input: x: (B, H*W, C), x_size: (H, W)
-        Output: x: (B, H*W, C)
-        """
-        H, W = x_size
-        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
-        x = x + self.drop_path(self.ffn(self.norm2(x), H, W))
-
-        return x
-
-
-class ResidualGroup(nn.Module):
-    """ResidualGroup
-    Args:
-        dim (int): Number of input channels.
-        reso (int): Input resolution.
-        num_heads (int): Number of attention heads.
-        split_size (tuple(int)): Height and Width of spatial window.
-        expansion_factor (float): Ratio of ffn hidden dim to embedding dim.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop (float): Dropout rate. Default: 0
-        attn_drop(float): Attention dropout rate. Default: 0
-        drop_paths (float | None): Stochastic depth rate.
-        act_layer (nn.Module): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm
-        depth (int): Number of dual aggregation Transformer blocks in residual group.
-        use_chk (bool): Whether to use checkpointing to save memory.
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(
-        self,
-        dim,
-        reso,
-        num_heads,
-        split_size=[2, 4],
-        expansion_factor=4.0,
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_paths=None,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        depth=2,
-        use_chk=False,
-        resi_connection="1conv",
-        rg_idx=0,
-    ):
-        super().__init__()
-        self.use_chk = use_chk
-        self.reso = reso
-
-        self.blocks = nn.ModuleList(
-            [
-                DATB(
-                    dim=dim,
-                    num_heads=num_heads,
-                    reso=reso,
-                    split_size=split_size,
-                    shift_size=[split_size[0] // 2, split_size[1] // 2],
-                    expansion_factor=expansion_factor,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_paths[i],
-                    act_layer=act_layer,
-                    norm_layer=norm_layer,
-                    rg_idx=rg_idx,
-                    b_idx=i,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        if resi_connection == "1conv":
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            self.conv = nn.Sequential(
-                nn.Conv2d(dim, dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim, 3, 1, 1),
-            )
-
-    def forward(self, x, x_size):
-        """
-        Input: x: (B, H*W, C), x_size: (H, W)
-        Output: x: (B, H*W, C)
-        """
-        H, W = x_size
-        res = x
-        for blk in self.blocks:
-            if self.use_chk:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
-        x = self.conv(x)
-        x = rearrange(x, "b c h w -> b (h w) c")
-        x = res + x
-
-        return x
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        h, w = self.input_resolution
-        flops = h * w * self.num_feat * 3 * 9
-        return flops
-
-
-class DAT(nn.Module):
-    """Dual Aggregation Transformer
-    Args:
-        img_size (int): Input image size. Default: 64
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 180
-        depths (tuple(int)): Depth of each residual group (number of DATB in each RG).
-        split_size (tuple(int)): Height and Width of spatial window.
-        num_heads (tuple(int)): Number of attention heads in different residual groups.
-        expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        act_layer (nn.Module): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm
-        use_chk (bool): Whether to use checkpointing to save memory.
-        upscale: Upscale factor. 2/3/4 for image SR
-        img_range: Image range. 1. or 255.
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, state_dict):
-        super().__init__()
-
-        # defaults
-        img_size = 64
-        in_chans = 3
-        embed_dim = 180
-        split_size = [2, 4]
-        depth = [2, 2, 2, 2]
-        num_heads = [2, 2, 2, 2]
-        expansion_factor = 4.0
-        qkv_bias = True
-        qk_scale = None
-        drop_rate = 0.0
-        attn_drop_rate = 0.0
-        drop_path_rate = 0.1
-        act_layer = nn.GELU
-        norm_layer = nn.LayerNorm
-        use_chk = False
-        upscale = 2
-        img_range = 1.0
-        resi_connection = "1conv"
-        upsampler = "pixelshuffle"
-
-        self.model_arch = "DAT"
-        self.sub_type = "SR"
-        self.state = state_dict
-
-        state_keys = state_dict.keys()
-        if "conv_before_upsample.0.weight" in state_keys:
-            if "conv_up1.weight" in state_keys:
-                upsampler = "nearest+conv"
-            else:
-                upsampler = "pixelshuffle"
-                supports_fp16 = False
-        elif "upsample.0.weight" in state_keys:
-            upsampler = "pixelshuffledirect"
-        else:
-            upsampler = ""
-
-        num_feat = (
-            state_dict.get("conv_before_upsample.0.weight", None).shape[1]
-            if state_dict.get("conv_before_upsample.weight", None)
-            else 64
-        )
-
-        num_in_ch = state_dict["conv_first.weight"].shape[1]
-        in_chans = num_in_ch
-        if "conv_last.weight" in state_keys:
-            num_out_ch = state_dict["conv_last.weight"].shape[0]
-        else:
-            num_out_ch = num_in_ch
-
-        upscale = 1
-        if upsampler == "nearest+conv":
-            upsample_keys = [
-                x for x in state_keys if "conv_up" in x and "bias" not in x
-            ]
-
-            for upsample_key in upsample_keys:
-                upscale *= 2
-        elif upsampler == "pixelshuffle":
-            upsample_keys = [
-                x
-                for x in state_keys
-                if "upsample" in x and "conv" not in x and "bias" not in x
-            ]
-            for upsample_key in upsample_keys:
-                shape = state_dict[upsample_key].shape[0]
-                upscale *= math.sqrt(shape // num_feat)
-            upscale = int(upscale)
-        elif upsampler == "pixelshuffledirect":
-            upscale = int(
-                math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch)
-            )
-
-        max_layer_num = 0
-        max_block_num = 0
-        for key in state_keys:
-            result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key)
-            if result:
-                layer_num, block_num = result.groups()
-                max_layer_num = max(max_layer_num, int(layer_num))
-                max_block_num = max(max_block_num, int(block_num))
-
-        depth = [max_block_num + 1 for _ in range(max_layer_num + 1)]
-
-        if "layers.0.blocks.1.attn.temperature" in state_keys:
-            num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0]
-            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
-        else:
-            num_heads = depth
-
-        embed_dim = state_dict["conv_first.weight"].shape[0]
-        expansion_factor = float(
-            state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim
-        )
-
-        # TODO: could actually count the layers, but this should do
-        if "layers.0.conv.4.weight" in state_keys:
-            resi_connection = "3conv"
-        else:
-            resi_connection = "1conv"
-
-        if "layers.0.blocks.2.attn.attn_mask_0" in state_keys:
-            attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[
-                "layers.0.blocks.2.attn.attn_mask_0"
-            ].shape
-
-            img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y))
-
-        if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys:
-            split_sizes = (
-                state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1
-            )
-            split_size = [int(x) for x in split_sizes]
-
-        self.in_nc = num_in_ch
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.depth = depth
-        self.scale = upscale
-        self.upsampler = upsampler
-        self.img_size = img_size
-        self.img_range = img_range
-        self.expansion_factor = expansion_factor
-        self.resi_connection = resi_connection
-        self.split_size = split_size
-
-        self.supports_fp16 = False  # Too much weirdness to support this at the moment
-        self.supports_bfp16 = True
-        self.min_size_restriction = 16
-
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-
-        # ------------------------- 1, Shallow Feature Extraction ------------------------- #
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        # ------------------------- 2, Deep Feature Extraction ------------------------- #
-        self.num_layers = len(depth)
-        self.use_chk = use_chk
-        self.num_features = (
-            self.embed_dim
-        ) = embed_dim  # num_features for consistency with other models
-        heads = num_heads
-
-        self.before_RG = nn.Sequential(
-            Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim)
-        )
-
-        curr_dim = embed_dim
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth))
-        ]  # stochastic depth decay rule
-
-        self.layers = nn.ModuleList()
-        for i in range(self.num_layers):
-            layer = ResidualGroup(
-                dim=embed_dim,
-                num_heads=heads[i],
-                reso=img_size,
-                split_size=split_size,
-                expansion_factor=expansion_factor,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])],
-                act_layer=act_layer,
-                norm_layer=norm_layer,
-                depth=depth[i],
-                use_chk=use_chk,
-                resi_connection=resi_connection,
-                rg_idx=i,
-            )
-            self.layers.append(layer)
-
-        self.norm = norm_layer(curr_dim)
-        # build the last conv layer in deep feature extraction
-        if resi_connection == "1conv":
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(
-                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),
-            )
-
-        # ------------------------- 3, Reconstruction ------------------------- #
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(
-                upscale, embed_dim, num_out_ch, (img_size, img_size)
-            )
-
-        self.apply(self._init_weights)
-        self.load_state_dict(state_dict, strict=True)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(
-            m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d)
-        ):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    def forward_features(self, x):
-        _, _, H, W = x.shape
-        x_size = [H, W]
-        x = self.before_RG(x)
-        for layer in self.layers:
-            x = layer(x, x_size)
-        x = self.norm(x)
-        x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
-
-        return x
-
-    def forward(self, x):
-        """
-        Input: x: (B, C, H, W)
-        """
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == "pixelshuffle":
-            # for image SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-
-        x = x / self.img_range + self.mean
-        return x
diff --git a/comfy_extras/chainner_models/architecture/HAT.py b/comfy_extras/chainner_models/architecture/HAT.py
deleted file mode 100644
index 669474219..000000000
--- a/comfy_extras/chainner_models/architecture/HAT.py
+++ /dev/null
@@ -1,1277 +0,0 @@
-# pylint: skip-file
-# HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py
-import math
-import re
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-
-from .timm.helpers import to_2tuple
-from .timm.weight_init import trunc_normal_
-
-
-def drop_path(x, drop_prob: float = 0.0, training: bool = False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (
-        x.ndim - 1
-    )  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
-    """
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)  # type: ignore
-
-
-class ChannelAttention(nn.Module):
-    """Channel attention used in RCAN.
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        squeeze_factor (int): Channel squeeze factor. Default: 16.
-    """
-
-    def __init__(self, num_feat, squeeze_factor=16):
-        super(ChannelAttention, self).__init__()
-        self.attention = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        y = self.attention(x)
-        return x * y
-
-
-class CAB(nn.Module):
-    def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30):
-        super(CAB, self).__init__()
-
-        self.cab = nn.Sequential(
-            nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1),
-            nn.GELU(),
-            nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1),
-            ChannelAttention(num_feat, squeeze_factor),
-        )
-
-    def forward(self, x):
-        return self.cab(x)
-
-
-class Mlp(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (b, h, w, c)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*b, window_size, window_size, c)
-    """
-    b, h, w, c = x.shape
-    x = x.view(b, h // window_size, window_size, w // window_size, window_size, c)
-    windows = (
-        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c)
-    )
-    return windows
-
-
-def window_reverse(windows, window_size, h, w):
-    """
-    Args:
-        windows: (num_windows*b, window_size, window_size, c)
-        window_size (int): Window size
-        h (int): Height of image
-        w (int): Width of image
-    Returns:
-        x: (b, h, w, c)
-    """
-    b = int(windows.shape[0] / (h * w / window_size / window_size))
-    x = windows.view(
-        b, h // window_size, w // window_size, window_size, window_size, -1
-    )
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(  # type: ignore
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, rpi, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*b, n, c)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        b_, n, c = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(b_, n, 3, self.num_heads, c // self.num_heads)
-            .permute(2, 0, 3, 1, 4)
-        )
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(
-            self.window_size[0] * self.window_size[1],
-            self.window_size[0] * self.window_size[1],
-            -1,
-        )  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nw = mask.shape[0]
-            attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze(
-                1
-            ).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, n, n)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(b_, n, c)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class HAB(nn.Module):
-    r"""Hybrid Attention Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        compress_ratio=3,
-        squeeze_factor=30,
-        conv_scale=0.01,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert (
-            0 <= self.shift_size < self.window_size
-        ), "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-
-        self.conv_scale = conv_scale
-        self.conv_block = CAB(
-            num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor
-        )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-    def forward(self, x, x_size, rpi_sa, attn_mask):
-        h, w = x_size
-        b, _, c = x.shape
-        # assert seq_len == h * w, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(b, h, w, c)
-
-        # Conv_X
-        conv_x = self.conv_block(x.permute(0, 3, 1, 2))
-        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(
-                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
-            )
-            attn_mask = attn_mask
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nw*b, window_size, window_size, c
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, c
-        )  # nw*b, window_size*window_size, c
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask)
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c)
-        shifted_x = window_reverse(attn_windows, self.window_size, h, w)  # b h' w' c
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            attn_x = torch.roll(
-                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
-            )
-        else:
-            attn_x = shifted_x
-        attn_x = attn_x.view(b, h * w, c)
-
-        # FFN
-        x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    r"""Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: b, h*w, c
-        """
-        h, w = self.input_resolution
-        b, seq_len, c = x.shape
-        assert seq_len == h * w, "input feature has wrong size"
-        assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even."
-
-        x = x.view(b, h, w, c)
-
-        x0 = x[:, 0::2, 0::2, :]  # b h/2 w/2 c
-        x1 = x[:, 1::2, 0::2, :]  # b h/2 w/2 c
-        x2 = x[:, 0::2, 1::2, :]  # b h/2 w/2 c
-        x3 = x[:, 1::2, 1::2, :]  # b h/2 w/2 c
-        x = torch.cat([x0, x1, x2, x3], -1)  # b h/2 w/2 4*c
-        x = x.view(b, -1, 4 * c)  # b h/2*w/2 4*c
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class OCAB(nn.Module):
-    # overlapping cross-attention block
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        window_size,
-        overlap_ratio,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        mlp_ratio=2,
-        norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.window_size = window_size
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-        self.overlap_win_size = int(window_size * overlap_ratio) + window_size
-
-        self.norm1 = norm_layer(dim)
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.unfold = nn.Unfold(
-            kernel_size=(self.overlap_win_size, self.overlap_win_size),
-            stride=window_size,
-            padding=(self.overlap_win_size - window_size) // 2,
-        )
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(  # type: ignore
-            torch.zeros(
-                (window_size + self.overlap_win_size - 1)
-                * (window_size + self.overlap_win_size - 1),
-                num_heads,
-            )
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-        self.proj = nn.Linear(dim, dim)
-
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU
-        )
-
-    def forward(self, x, x_size, rpi):
-        h, w = x_size
-        b, _, c = x.shape
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(b, h, w, c)
-
-        qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2)  # 3, b, c, h, w
-        q = qkv[0].permute(0, 2, 3, 1)  # b, h, w, c
-        kv = torch.cat((qkv[1], qkv[2]), dim=1)  # b, 2*c, h, w
-
-        # partition windows
-        q_windows = window_partition(
-            q, self.window_size
-        )  # nw*b, window_size, window_size, c
-        q_windows = q_windows.view(
-            -1, self.window_size * self.window_size, c
-        )  # nw*b, window_size*window_size, c
-
-        kv_windows = self.unfold(kv)  # b, c*w*w, nw
-        kv_windows = rearrange(
-            kv_windows,
-            "b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch",
-            nc=2,
-            ch=c,
-            owh=self.overlap_win_size,
-            oww=self.overlap_win_size,
-        ).contiguous()  # 2, nw*b, ow*ow, c
-        # Do the above rearrangement without the rearrange function
-        # kv_windows = kv_windows.view(
-        #     2, b, self.overlap_win_size, self.overlap_win_size, c, -1
-        # )
-        # kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous()
-        # kv_windows = kv_windows.view(
-        #     2, -1, self.overlap_win_size * self.overlap_win_size, c
-        # )
-
-        k_windows, v_windows = kv_windows[0], kv_windows[1]  # nw*b, ow*ow, c
-
-        b_, nq, _ = q_windows.shape
-        _, n, _ = k_windows.shape
-        d = self.dim // self.num_heads
-        q = q_windows.reshape(b_, nq, self.num_heads, d).permute(
-            0, 2, 1, 3
-        )  # nw*b, nH, nq, d
-        k = k_windows.reshape(b_, n, self.num_heads, d).permute(
-            0, 2, 1, 3
-        )  # nw*b, nH, n, d
-        v = v_windows.reshape(b_, n, self.num_heads, d).permute(
-            0, 2, 1, 3
-        )  # nw*b, nH, n, d
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(
-            self.window_size * self.window_size,
-            self.overlap_win_size * self.overlap_win_size,
-            -1,
-        )  # ws*ws, wse*wse, nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, ws*ws, wse*wse
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        attn = self.softmax(attn)
-        attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim)
-
-        # merge windows
-        attn_windows = attn_windows.view(
-            -1, self.window_size, self.window_size, self.dim
-        )
-        x = window_reverse(attn_windows, self.window_size, h, w)  # b h w c
-        x = x.view(b, h * w, self.dim)
-
-        x = self.proj(x) + shortcut
-
-        x = x + self.mlp(self.norm2(x))
-        return x
-
-
-class AttenBlocks(nn.Module):
-    """A series of attention blocks for one RHAG.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        compress_ratio,
-        squeeze_factor,
-        conv_scale,
-        overlap_ratio,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                HAB(
-                    dim=dim,
-                    input_resolution=input_resolution,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    compress_ratio=compress_ratio,
-                    squeeze_factor=squeeze_factor,
-                    conv_scale=conv_scale,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i]
-                    if isinstance(drop_path, list)
-                    else drop_path,
-                    norm_layer=norm_layer,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # OCAB
-        self.overlap_attn = OCAB(
-            dim=dim,
-            input_resolution=input_resolution,
-            window_size=window_size,
-            overlap_ratio=overlap_ratio,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            mlp_ratio=mlp_ratio,  # type: ignore
-            norm_layer=norm_layer,
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(
-                input_resolution, dim=dim, norm_layer=norm_layer
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size, params):
-        for blk in self.blocks:
-            x = blk(x, x_size, params["rpi_sa"], params["attn_mask"])
-
-        x = self.overlap_attn(x, x_size, params["rpi_oca"])
-
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-
-class RHAG(nn.Module):
-    """Residual Hybrid Attention Group (RHAG).
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        compress_ratio,
-        squeeze_factor,
-        conv_scale,
-        overlap_ratio,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        img_size=224,
-        patch_size=4,
-        resi_connection="1conv",
-    ):
-        super(RHAG, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = AttenBlocks(
-            dim=dim,
-            input_resolution=input_resolution,
-            depth=depth,
-            num_heads=num_heads,
-            window_size=window_size,
-            compress_ratio=compress_ratio,
-            squeeze_factor=squeeze_factor,
-            conv_scale=conv_scale,
-            overlap_ratio=overlap_ratio,
-            mlp_ratio=mlp_ratio,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            drop=drop,
-            attn_drop=attn_drop,
-            drop_path=drop_path,
-            norm_layer=norm_layer,
-            downsample=downsample,
-            use_checkpoint=use_checkpoint,
-        )
-
-        if resi_connection == "1conv":
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == "identity":
-            self.conv = nn.Identity()
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=0,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=0,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-    def forward(self, x, x_size, params):
-        return (
-            self.patch_embed(
-                self.conv(
-                    self.patch_unembed(self.residual_group(x, x_size, params), x_size)
-                )
-            )
-            + x
-        )
-
-
-class PatchEmbed(nn.Module):
-    r"""Image to Patch Embedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [
-            img_size[0] // patch_size[0],  # type: ignore
-            img_size[1] // patch_size[1],  # type: ignore
-        ]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # b Ph*Pw c
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-
-class PatchUnEmbed(nn.Module):
-    r"""Image to Patch Unembedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [
-            img_size[0] // patch_size[0],  # type: ignore
-            img_size[1] // patch_size[1],  # type: ignore
-        ]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        x = (
-            x.transpose(1, 2)
-            .contiguous()
-            .view(x.shape[0], self.embed_dim, x_size[0], x_size[1])
-        )  # b Ph*Pw c
-        return x
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample, self).__init__(*m)
-
-
-class HAT(nn.Module):
-    r"""Hybrid Attention Transformer
-        A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`.
-        Some codes are based on SwinIR.
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        **kwargs,
-    ):
-        super(HAT, self).__init__()
-
-        # Defaults
-        img_size = 64
-        patch_size = 1
-        in_chans = 3
-        embed_dim = 96
-        depths = (6, 6, 6, 6)
-        num_heads = (6, 6, 6, 6)
-        window_size = 7
-        compress_ratio = 3
-        squeeze_factor = 30
-        conv_scale = 0.01
-        overlap_ratio = 0.5
-        mlp_ratio = 4.0
-        qkv_bias = True
-        qk_scale = None
-        drop_rate = 0.0
-        attn_drop_rate = 0.0
-        drop_path_rate = 0.1
-        norm_layer = nn.LayerNorm
-        ape = False
-        patch_norm = True
-        use_checkpoint = False
-        upscale = 2
-        img_range = 1.0
-        upsampler = ""
-        resi_connection = "1conv"
-
-        self.state = state_dict
-        self.model_arch = "HAT"
-        self.sub_type = "SR"
-        self.supports_fp16 = False
-        self.support_bf16 = True
-        self.min_size_restriction = 16
-
-        state_keys = list(state_dict.keys())
-
-        num_feat = state_dict["conv_last.weight"].shape[1]
-        in_chans = state_dict["conv_first.weight"].shape[1]
-        num_out_ch = state_dict["conv_last.weight"].shape[0]
-        embed_dim = state_dict["conv_first.weight"].shape[0]
-
-        if "conv_before_upsample.0.weight" in state_keys:
-            if "conv_up1.weight" in state_keys:
-                upsampler = "nearest+conv"
-            else:
-                upsampler = "pixelshuffle"
-                supports_fp16 = False
-        elif "upsample.0.weight" in state_keys:
-            upsampler = "pixelshuffledirect"
-        else:
-            upsampler = ""
-        upscale = 1
-        if upsampler == "nearest+conv":
-            upsample_keys = [
-                x for x in state_keys if "conv_up" in x and "bias" not in x
-            ]
-
-            for upsample_key in upsample_keys:
-                upscale *= 2
-        elif upsampler == "pixelshuffle":
-            upsample_keys = [
-                x
-                for x in state_keys
-                if "upsample" in x and "conv" not in x and "bias" not in x
-            ]
-            for upsample_key in upsample_keys:
-                shape = self.state[upsample_key].shape[0]
-                upscale *= math.sqrt(shape // num_feat)
-            upscale = int(upscale)
-        elif upsampler == "pixelshuffledirect":
-            upscale = int(
-                math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch)
-            )
-
-        max_layer_num = 0
-        max_block_num = 0
-        for key in state_keys:
-            result = re.match(
-                r"layers.(\d*).residual_group.blocks.(\d*).conv_block.cab.0.weight", key
-            )
-            if result:
-                layer_num, block_num = result.groups()
-                max_layer_num = max(max_layer_num, int(layer_num))
-                max_block_num = max(max_block_num, int(block_num))
-
-        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]
-
-        if (
-            "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            in state_keys
-        ):
-            num_heads_num = self.state[
-                "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            ].shape[-1]
-            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
-        else:
-            num_heads = depths
-
-        mlp_ratio = float(
-            self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0]
-            / embed_dim
-        )
-
-        # TODO: could actually count the layers, but this should do
-        if "layers.0.conv.4.weight" in state_keys:
-            resi_connection = "3conv"
-        else:
-            resi_connection = "1conv"
-
-        window_size = int(math.sqrt(self.state["relative_position_index_SA"].shape[0]))
-
-        # Not sure if this is needed or used at all anywhere in HAT's config
-        if "layers.0.residual_group.blocks.1.attn_mask" in state_keys:
-            img_size = int(
-                math.sqrt(
-                    self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
-                )
-                * window_size
-            )
-
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.overlap_ratio = overlap_ratio
-
-        self.in_nc = in_chans
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.depths = depths
-        self.window_size = window_size
-        self.mlp_ratio = mlp_ratio
-        self.scale = upscale
-        self.upsampler = upsampler
-        self.img_size = img_size
-        self.img_range = img_range
-        self.resi_connection = resi_connection
-
-        num_in_ch = in_chans
-        # num_out_ch = in_chans
-        # num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-
-        # relative position index
-        relative_position_index_SA = self.calculate_rpi_sa()
-        relative_position_index_OCA = self.calculate_rpi_oca()
-        self.register_buffer("relative_position_index_SA", relative_position_index_SA)
-        self.register_buffer("relative_position_index_OCA", relative_position_index_OCA)
-
-        # ------------------------- 1, shallow feature extraction ------------------------- #
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        # ------------------------- 2, deep feature extraction ------------------------- #
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(  # type: ignore[arg-type]
-                torch.zeros(1, num_patches, embed_dim)
-            )
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
-        ]  # stochastic depth decay rule
-
-        # build Residual Hybrid Attention Groups (RHAG)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RHAG(
-                dim=embed_dim,
-                input_resolution=(patches_resolution[0], patches_resolution[1]),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                compress_ratio=compress_ratio,
-                squeeze_factor=squeeze_factor,
-                conv_scale=conv_scale,
-                overlap_ratio=overlap_ratio,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[
-                    sum(depths[:i_layer]) : sum(depths[: i_layer + 1])  # type: ignore
-                ],  # no impact on SR results
-                norm_layer=norm_layer,
-                downsample=None,
-                use_checkpoint=use_checkpoint,
-                img_size=img_size,
-                patch_size=patch_size,
-                resi_connection=resi_connection,
-            )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == "1conv":
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == "identity":
-            self.conv_after_body = nn.Identity()
-
-        # ------------------------- 3, high quality image reconstruction ------------------------- #
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-        self.load_state_dict(self.state, strict=False)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    def calculate_rpi_sa(self):
-        # calculate relative position index for SA
-        coords_h = torch.arange(self.window_size)
-        coords_w = torch.arange(self.window_size)
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = (
-            coords_flatten[:, :, None] - coords_flatten[:, None, :]
-        )  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        return relative_position_index
-
-    def calculate_rpi_oca(self):
-        # calculate relative position index for OCA
-        window_size_ori = self.window_size
-        window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size)
-
-        coords_h = torch.arange(window_size_ori)
-        coords_w = torch.arange(window_size_ori)
-        coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, ws, ws
-        coords_ori_flatten = torch.flatten(coords_ori, 1)  # 2, ws*ws
-
-        coords_h = torch.arange(window_size_ext)
-        coords_w = torch.arange(window_size_ext)
-        coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, wse, wse
-        coords_ext_flatten = torch.flatten(coords_ext, 1)  # 2, wse*wse
-
-        relative_coords = (
-            coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None]
-        )  # 2, ws*ws, wse*wse
-
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # ws*ws, wse*wse, 2
-        relative_coords[:, :, 0] += (
-            window_size_ori - window_size_ext + 1
-        )  # shift to start from 0
-        relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1
-
-        relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1
-        relative_position_index = relative_coords.sum(-1)
-        return relative_position_index
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        h, w = x_size
-        img_mask = torch.zeros((1, h, w, 1))  # 1 h w 1
-        h_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        w_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(
-            img_mask, self.window_size
-        )  # nw, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
-            attn_mask == 0, float(0.0)
-        )
-
-        return attn_mask
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay(self):
-        return {"absolute_pos_embed"}
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay_keywords(self):
-        return {"relative_position_bias_table"}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-
-        # Calculate attention mask and relative position index in advance to speed up inference.
-        # The original code is very time-cosuming for large window size.
-        attn_mask = self.calculate_mask(x_size).to(x.device)
-        params = {
-            "attn_mask": attn_mask,
-            "rpi_sa": self.relative_position_index_SA,
-            "rpi_oca": self.relative_position_index_OCA,
-        }
-
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size, params)
-
-        x = self.norm(x)  # b seq_len c
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-        x = self.check_image_size(x)
-
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, : H * self.upscale, : W * self.upscale]
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-DAT b/comfy_extras/chainner_models/architecture/LICENSE-DAT
deleted file mode 100644
index 261eeb9e9..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-DAT
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [yyyy] [name of copyright owner]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN b/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN
deleted file mode 100644
index 261eeb9e9..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [yyyy] [name of copyright owner]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-HAT b/comfy_extras/chainner_models/architecture/LICENSE-HAT
deleted file mode 100644
index 003e97e96..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-HAT
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Xiangyu Chen
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN b/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN
deleted file mode 100644
index 552a1eeaf..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN
+++ /dev/null
@@ -1,29 +0,0 @@
-BSD 3-Clause License
-
-Copyright (c) 2021, Xintao Wang
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its
-   contributors may be used to endorse or promote products derived from
-   this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SCUNet b/comfy_extras/chainner_models/architecture/LICENSE-SCUNet
deleted file mode 100644
index ff75c988f..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-SCUNet
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
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diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SPSR b/comfy_extras/chainner_models/architecture/LICENSE-SPSR
deleted file mode 100644
index 3245f3f9e..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-SPSR
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
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-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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-   1. Definitions.
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-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
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-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
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-      other commercial damages or losses), even if such Contributor
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-      defend, and hold each Contributor harmless for any liability
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diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN b/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN
deleted file mode 100644
index 0e259d42c..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN
+++ /dev/null
@@ -1,121 +0,0 @@
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diff --git a/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR b/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR
deleted file mode 100644
index e5e4ee061..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
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-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [2021] [SwinIR Authors]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SwinIR b/comfy_extras/chainner_models/architecture/LICENSE-SwinIR
deleted file mode 100644
index e5e4ee061..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-SwinIR
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [2021] [SwinIR Authors]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-lama b/comfy_extras/chainner_models/architecture/LICENSE-lama
deleted file mode 100644
index ca822bb5f..000000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-lama
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [2021] Samsung Research
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LaMa.py b/comfy_extras/chainner_models/architecture/LaMa.py
deleted file mode 100644
index a781f3e4d..000000000
--- a/comfy_extras/chainner_models/architecture/LaMa.py
+++ /dev/null
@@ -1,694 +0,0 @@
-# pylint: skip-file
-"""
-Model adapted from advimman's lama project: https://github.com/advimman/lama
-"""
-
-# Fast Fourier Convolution NeurIPS 2020
-# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
-# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
-
-from typing import List
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.transforms.functional import InterpolationMode, rotate
-
-
-class LearnableSpatialTransformWrapper(nn.Module):
-    def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True):
-        super().__init__()
-        self.impl = impl
-        self.angle = torch.rand(1) * angle_init_range
-        if train_angle:
-            self.angle = nn.Parameter(self.angle, requires_grad=True)
-        self.pad_coef = pad_coef
-
-    def forward(self, x):
-        if torch.is_tensor(x):
-            return self.inverse_transform(self.impl(self.transform(x)), x)
-        elif isinstance(x, tuple):
-            x_trans = tuple(self.transform(elem) for elem in x)
-            y_trans = self.impl(x_trans)
-            return tuple(
-                self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x)
-            )
-        else:
-            raise ValueError(f"Unexpected input type {type(x)}")
-
-    def transform(self, x):
-        height, width = x.shape[2:]
-        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
-        x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode="reflect")
-        x_padded_rotated = rotate(
-            x_padded, self.angle.to(x_padded), InterpolationMode.BILINEAR, fill=0
-        )
-
-        return x_padded_rotated
-
-    def inverse_transform(self, y_padded_rotated, orig_x):
-        height, width = orig_x.shape[2:]
-        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
-
-        y_padded = rotate(
-            y_padded_rotated,
-            -self.angle.to(y_padded_rotated),
-            InterpolationMode.BILINEAR,
-            fill=0,
-        )
-        y_height, y_width = y_padded.shape[2:]
-        y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w]
-        return y
-
-
-class SELayer(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(SELayer, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel, bias=False),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        res = x * y.expand_as(x)
-        return res
-
-
-class FourierUnit(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        groups=1,
-        spatial_scale_factor=None,
-        spatial_scale_mode="bilinear",
-        spectral_pos_encoding=False,
-        use_se=False,
-        se_kwargs=None,
-        ffc3d=False,
-        fft_norm="ortho",
-    ):
-        # bn_layer not used
-        super(FourierUnit, self).__init__()
-        self.groups = groups
-
-        self.conv_layer = torch.nn.Conv2d(
-            in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
-            out_channels=out_channels * 2,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            groups=self.groups,
-            bias=False,
-        )
-        self.bn = torch.nn.BatchNorm2d(out_channels * 2)
-        self.relu = torch.nn.ReLU(inplace=True)
-
-        # squeeze and excitation block
-        self.use_se = use_se
-        if use_se:
-            if se_kwargs is None:
-                se_kwargs = {}
-            self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
-
-        self.spatial_scale_factor = spatial_scale_factor
-        self.spatial_scale_mode = spatial_scale_mode
-        self.spectral_pos_encoding = spectral_pos_encoding
-        self.ffc3d = ffc3d
-        self.fft_norm = fft_norm
-
-    def forward(self, x):
-        half_check = False
-        if x.type() == "torch.cuda.HalfTensor":
-            # half only works on gpu anyway
-            half_check = True
-
-        batch = x.shape[0]
-
-        if self.spatial_scale_factor is not None:
-            orig_size = x.shape[-2:]
-            x = F.interpolate(
-                x,
-                scale_factor=self.spatial_scale_factor,
-                mode=self.spatial_scale_mode,
-                align_corners=False,
-            )
-
-        # (batch, c, h, w/2+1, 2)
-        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
-        if half_check == True:
-            ffted = torch.fft.rfftn(
-                x.float(), dim=fft_dim, norm=self.fft_norm
-            )  # .type(torch.cuda.HalfTensor)
-        else:
-            ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
-
-        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
-        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)
-        ffted = ffted.view(
-            (
-                batch,
-                -1,
-            )
-            + ffted.size()[3:]
-        )
-
-        if self.spectral_pos_encoding:
-            height, width = ffted.shape[-2:]
-            coords_vert = (
-                torch.linspace(0, 1, height)[None, None, :, None]
-                .expand(batch, 1, height, width)
-                .to(ffted)
-            )
-            coords_hor = (
-                torch.linspace(0, 1, width)[None, None, None, :]
-                .expand(batch, 1, height, width)
-                .to(ffted)
-            )
-            ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
-
-        if self.use_se:
-            ffted = self.se(ffted)
-
-        if half_check == True:
-            ffted = self.conv_layer(ffted.half())  # (batch, c*2, h, w/2+1)
-        else:
-            ffted = self.conv_layer(
-                ffted
-            )  # .type(torch.cuda.FloatTensor)  # (batch, c*2, h, w/2+1)
-
-        ffted = self.relu(self.bn(ffted))
-        # forcing to be always float
-        ffted = ffted.float()
-
-        ffted = (
-            ffted.view(
-                (
-                    batch,
-                    -1,
-                    2,
-                )
-                + ffted.size()[2:]
-            )
-            .permute(0, 1, 3, 4, 2)
-            .contiguous()
-        )  # (batch,c, t, h, w/2+1, 2)
-
-        ffted = torch.complex(ffted[..., 0], ffted[..., 1])
-
-        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
-        output = torch.fft.irfftn(
-            ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm
-        )
-
-        if half_check == True:
-            output = output.half()
-
-        if self.spatial_scale_factor is not None:
-            output = F.interpolate(
-                output,
-                size=orig_size,
-                mode=self.spatial_scale_mode,
-                align_corners=False,
-            )
-
-        return output
-
-
-class SpectralTransform(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        stride=1,
-        groups=1,
-        enable_lfu=True,
-        separable_fu=False,
-        **fu_kwargs,
-    ):
-        # bn_layer not used
-        super(SpectralTransform, self).__init__()
-        self.enable_lfu = enable_lfu
-        if stride == 2:
-            self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
-        else:
-            self.downsample = nn.Identity()
-
-        self.stride = stride
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(
-                in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False
-            ),
-            nn.BatchNorm2d(out_channels // 2),
-            nn.ReLU(inplace=True),
-        )
-        fu_class = FourierUnit
-        self.fu = fu_class(out_channels // 2, out_channels // 2, groups, **fu_kwargs)
-        if self.enable_lfu:
-            self.lfu = fu_class(out_channels // 2, out_channels // 2, groups)
-        self.conv2 = torch.nn.Conv2d(
-            out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False
-        )
-
-    def forward(self, x):
-        x = self.downsample(x)
-        x = self.conv1(x)
-        output = self.fu(x)
-
-        if self.enable_lfu:
-            _, c, h, _ = x.shape
-            split_no = 2
-            split_s = h // split_no
-            xs = torch.cat(
-                torch.split(x[:, : c // 4], split_s, dim=-2), dim=1
-            ).contiguous()
-            xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous()
-            xs = self.lfu(xs)
-            xs = xs.repeat(1, 1, split_no, split_no).contiguous()
-        else:
-            xs = 0
-
-        output = self.conv2(x + output + xs)
-
-        return output
-
-
-class FFC(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        ratio_gin,
-        ratio_gout,
-        stride=1,
-        padding=0,
-        dilation=1,
-        groups=1,
-        bias=False,
-        enable_lfu=True,
-        padding_type="reflect",
-        gated=False,
-        **spectral_kwargs,
-    ):
-        super(FFC, self).__init__()
-
-        assert stride == 1 or stride == 2, "Stride should be 1 or 2."
-        self.stride = stride
-
-        in_cg = int(in_channels * ratio_gin)
-        in_cl = in_channels - in_cg
-        out_cg = int(out_channels * ratio_gout)
-        out_cl = out_channels - out_cg
-        # groups_g = 1 if groups == 1 else int(groups * ratio_gout)
-        # groups_l = 1 if groups == 1 else groups - groups_g
-
-        self.ratio_gin = ratio_gin
-        self.ratio_gout = ratio_gout
-        self.global_in_num = in_cg
-
-        module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
-        self.convl2l = module(
-            in_cl,
-            out_cl,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            padding_mode=padding_type,
-        )
-        module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
-        self.convl2g = module(
-            in_cl,
-            out_cg,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            padding_mode=padding_type,
-        )
-        module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
-        self.convg2l = module(
-            in_cg,
-            out_cl,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            padding_mode=padding_type,
-        )
-        module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
-        self.convg2g = module(
-            in_cg,
-            out_cg,
-            stride,
-            1 if groups == 1 else groups // 2,
-            enable_lfu,
-            **spectral_kwargs,
-        )
-
-        self.gated = gated
-        module = (
-            nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
-        )
-        self.gate = module(in_channels, 2, 1)
-
-    def forward(self, x):
-        x_l, x_g = x if type(x) is tuple else (x, 0)
-        out_xl, out_xg = 0, 0
-
-        if self.gated:
-            total_input_parts = [x_l]
-            if torch.is_tensor(x_g):
-                total_input_parts.append(x_g)
-            total_input = torch.cat(total_input_parts, dim=1)
-
-            gates = torch.sigmoid(self.gate(total_input))
-            g2l_gate, l2g_gate = gates.chunk(2, dim=1)
-        else:
-            g2l_gate, l2g_gate = 1, 1
-
-        if self.ratio_gout != 1:
-            out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
-        if self.ratio_gout != 0:
-            out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)
-
-        return out_xl, out_xg
-
-
-class FFC_BN_ACT(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        ratio_gin,
-        ratio_gout,
-        stride=1,
-        padding=0,
-        dilation=1,
-        groups=1,
-        bias=False,
-        norm_layer=nn.BatchNorm2d,
-        activation_layer=nn.Identity,
-        padding_type="reflect",
-        enable_lfu=True,
-        **kwargs,
-    ):
-        super(FFC_BN_ACT, self).__init__()
-        self.ffc = FFC(
-            in_channels,
-            out_channels,
-            kernel_size,
-            ratio_gin,
-            ratio_gout,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            enable_lfu,
-            padding_type=padding_type,
-            **kwargs,
-        )
-        lnorm = nn.Identity if ratio_gout == 1 else norm_layer
-        gnorm = nn.Identity if ratio_gout == 0 else norm_layer
-        global_channels = int(out_channels * ratio_gout)
-        self.bn_l = lnorm(out_channels - global_channels)
-        self.bn_g = gnorm(global_channels)
-
-        lact = nn.Identity if ratio_gout == 1 else activation_layer
-        gact = nn.Identity if ratio_gout == 0 else activation_layer
-        self.act_l = lact(inplace=True)
-        self.act_g = gact(inplace=True)
-
-    def forward(self, x):
-        x_l, x_g = self.ffc(x)
-        x_l = self.act_l(self.bn_l(x_l))
-        x_g = self.act_g(self.bn_g(x_g))
-        return x_l, x_g
-
-
-class FFCResnetBlock(nn.Module):
-    def __init__(
-        self,
-        dim,
-        padding_type,
-        norm_layer,
-        activation_layer=nn.ReLU,
-        dilation=1,
-        spatial_transform_kwargs=None,
-        inline=False,
-        **conv_kwargs,
-    ):
-        super().__init__()
-        self.conv1 = FFC_BN_ACT(
-            dim,
-            dim,
-            kernel_size=3,
-            padding=dilation,
-            dilation=dilation,
-            norm_layer=norm_layer,
-            activation_layer=activation_layer,
-            padding_type=padding_type,
-            **conv_kwargs,
-        )
-        self.conv2 = FFC_BN_ACT(
-            dim,
-            dim,
-            kernel_size=3,
-            padding=dilation,
-            dilation=dilation,
-            norm_layer=norm_layer,
-            activation_layer=activation_layer,
-            padding_type=padding_type,
-            **conv_kwargs,
-        )
-        if spatial_transform_kwargs is not None:
-            self.conv1 = LearnableSpatialTransformWrapper(
-                self.conv1, **spatial_transform_kwargs
-            )
-            self.conv2 = LearnableSpatialTransformWrapper(
-                self.conv2, **spatial_transform_kwargs
-            )
-        self.inline = inline
-
-    def forward(self, x):
-        if self.inline:
-            x_l, x_g = (
-                x[:, : -self.conv1.ffc.global_in_num],
-                x[:, -self.conv1.ffc.global_in_num :],
-            )
-        else:
-            x_l, x_g = x if type(x) is tuple else (x, 0)
-
-        id_l, id_g = x_l, x_g
-
-        x_l, x_g = self.conv1((x_l, x_g))
-        x_l, x_g = self.conv2((x_l, x_g))
-
-        x_l, x_g = id_l + x_l, id_g + x_g
-        out = x_l, x_g
-        if self.inline:
-            out = torch.cat(out, dim=1)
-        return out
-
-
-class ConcatTupleLayer(nn.Module):
-    def forward(self, x):
-        assert isinstance(x, tuple)
-        x_l, x_g = x
-        assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
-        if not torch.is_tensor(x_g):
-            return x_l
-        return torch.cat(x, dim=1)
-
-
-class FFCResNetGenerator(nn.Module):
-    def __init__(
-        self,
-        input_nc,
-        output_nc,
-        ngf=64,
-        n_downsampling=3,
-        n_blocks=18,
-        norm_layer=nn.BatchNorm2d,
-        padding_type="reflect",
-        activation_layer=nn.ReLU,
-        up_norm_layer=nn.BatchNorm2d,
-        up_activation=nn.ReLU(True),
-        init_conv_kwargs={},
-        downsample_conv_kwargs={},
-        resnet_conv_kwargs={},
-        spatial_transform_layers=None,
-        spatial_transform_kwargs={},
-        max_features=1024,
-        out_ffc=False,
-        out_ffc_kwargs={},
-    ):
-        assert n_blocks >= 0
-        super().__init__()
-        """
-        init_conv_kwargs = {'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False}
-        downsample_conv_kwargs = {'ratio_gin': '${generator.init_conv_kwargs.ratio_gout}', 'ratio_gout': '${generator.downsample_conv_kwargs.ratio_gin}', 'enable_lfu': False}
-        resnet_conv_kwargs = {'ratio_gin': 0.75, 'ratio_gout': '${generator.resnet_conv_kwargs.ratio_gin}', 'enable_lfu': False}
-        spatial_transform_kwargs = {}
-        out_ffc_kwargs = {}
-        """
-        """
-        print(input_nc, output_nc, ngf, n_downsampling, n_blocks, norm_layer,
-                padding_type, activation_layer,
-                up_norm_layer, up_activation,
-                spatial_transform_layers,
-                add_out_act, max_features, out_ffc, file=sys.stderr)
-
-        4 3 64 3 18 <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
-        reflect <class 'torch.nn.modules.activation.ReLU'>
-        <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
-        ReLU(inplace=True)
-        None sigmoid 1024 False
-        """
-        init_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False}
-        downsample_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False}
-        resnet_conv_kwargs = {
-            "ratio_gin": 0.75,
-            "ratio_gout": 0.75,
-            "enable_lfu": False,
-        }
-        spatial_transform_kwargs = {}
-        out_ffc_kwargs = {}
-
-        model = [
-            nn.ReflectionPad2d(3),
-            FFC_BN_ACT(
-                input_nc,
-                ngf,
-                kernel_size=7,
-                padding=0,
-                norm_layer=norm_layer,
-                activation_layer=activation_layer,
-                **init_conv_kwargs,
-            ),
-        ]
-
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2**i
-            if i == n_downsampling - 1:
-                cur_conv_kwargs = dict(downsample_conv_kwargs)
-                cur_conv_kwargs["ratio_gout"] = resnet_conv_kwargs.get("ratio_gin", 0)
-            else:
-                cur_conv_kwargs = downsample_conv_kwargs
-            model += [
-                FFC_BN_ACT(
-                    min(max_features, ngf * mult),
-                    min(max_features, ngf * mult * 2),
-                    kernel_size=3,
-                    stride=2,
-                    padding=1,
-                    norm_layer=norm_layer,
-                    activation_layer=activation_layer,
-                    **cur_conv_kwargs,
-                )
-            ]
-
-        mult = 2**n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            cur_resblock = FFCResnetBlock(
-                feats_num_bottleneck,
-                padding_type=padding_type,
-                activation_layer=activation_layer,
-                norm_layer=norm_layer,
-                **resnet_conv_kwargs,
-            )
-            if spatial_transform_layers is not None and i in spatial_transform_layers:
-                cur_resblock = LearnableSpatialTransformWrapper(
-                    cur_resblock, **spatial_transform_kwargs
-                )
-            model += [cur_resblock]
-
-        model += [ConcatTupleLayer()]
-
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += [
-                nn.ConvTranspose2d(
-                    min(max_features, ngf * mult),
-                    min(max_features, int(ngf * mult / 2)),
-                    kernel_size=3,
-                    stride=2,
-                    padding=1,
-                    output_padding=1,
-                ),
-                up_norm_layer(min(max_features, int(ngf * mult / 2))),
-                up_activation,
-            ]
-
-        if out_ffc:
-            model += [
-                FFCResnetBlock(
-                    ngf,
-                    padding_type=padding_type,
-                    activation_layer=activation_layer,
-                    norm_layer=norm_layer,
-                    inline=True,
-                    **out_ffc_kwargs,
-                )
-            ]
-
-        model += [
-            nn.ReflectionPad2d(3),
-            nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
-        ]
-        model.append(nn.Sigmoid())
-        self.model = nn.Sequential(*model)
-
-    def forward(self, image, mask):
-        return self.model(torch.cat([image, mask], dim=1))
-
-
-class LaMa(nn.Module):
-    def __init__(self, state_dict) -> None:
-        super(LaMa, self).__init__()
-        self.model_arch = "LaMa"
-        self.sub_type = "Inpaint"
-        self.in_nc = 4
-        self.out_nc = 3
-        self.scale = 1
-
-        self.min_size = None
-        self.pad_mod = 8
-        self.pad_to_square = False
-
-        self.model = FFCResNetGenerator(self.in_nc, self.out_nc)
-        self.state = {
-            k.replace("generator.model", "model.model"): v
-            for k, v in state_dict.items()
-        }
-
-        self.supports_fp16 = False
-        self.support_bf16 = True
-
-        self.load_state_dict(self.state, strict=False)
-
-    def forward(self, img, mask):
-        masked_img = img * (1 - mask)
-        inpainted_mask = mask * self.model.forward(masked_img, mask)
-        result = inpainted_mask + (1 - mask) * img
-        return result
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py b/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py
deleted file mode 100644
index f4d52aa1e..000000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import math
-
-import torch.nn as nn
-
-
-class CA_layer(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(CA_layer, self).__init__()
-        # global average pooling
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False),
-            nn.GELU(),
-            nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False),
-            # nn.Sigmoid()
-        )
-
-    def forward(self, x):
-        y = self.fc(self.gap(x))
-        return x * y.expand_as(x)
-
-
-class Simple_CA_layer(nn.Module):
-    def __init__(self, channel):
-        super(Simple_CA_layer, self).__init__()
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Conv2d(
-            in_channels=channel,
-            out_channels=channel,
-            kernel_size=1,
-            padding=0,
-            stride=1,
-            groups=1,
-            bias=True,
-        )
-
-    def forward(self, x):
-        return x * self.fc(self.gap(x))
-
-
-class ECA_layer(nn.Module):
-    """Constructs a ECA module.
-    Args:
-        channel: Number of channels of the input feature map
-        k_size: Adaptive selection of kernel size
-    """
-
-    def __init__(self, channel):
-        super(ECA_layer, self).__init__()
-
-        b = 1
-        gamma = 2
-        k_size = int(abs(math.log(channel, 2) + b) / gamma)
-        k_size = k_size if k_size % 2 else k_size + 1
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.conv = nn.Conv1d(
-            1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
-        )
-        # self.sigmoid = nn.Sigmoid()
-
-    def forward(self, x):
-        # x: input features with shape [b, c, h, w]
-        # b, c, h, w = x.size()
-
-        # feature descriptor on the global spatial information
-        y = self.avg_pool(x)
-
-        # Two different branches of ECA module
-        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
-
-        # Multi-scale information fusion
-        # y = self.sigmoid(y)
-
-        return x * y.expand_as(x)
-
-
-class ECA_MaxPool_layer(nn.Module):
-    """Constructs a ECA module.
-    Args:
-        channel: Number of channels of the input feature map
-        k_size: Adaptive selection of kernel size
-    """
-
-    def __init__(self, channel):
-        super(ECA_MaxPool_layer, self).__init__()
-
-        b = 1
-        gamma = 2
-        k_size = int(abs(math.log(channel, 2) + b) / gamma)
-        k_size = k_size if k_size % 2 else k_size + 1
-        self.max_pool = nn.AdaptiveMaxPool2d(1)
-        self.conv = nn.Conv1d(
-            1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
-        )
-        # self.sigmoid = nn.Sigmoid()
-
-    def forward(self, x):
-        # x: input features with shape [b, c, h, w]
-        # b, c, h, w = x.size()
-
-        # feature descriptor on the global spatial information
-        y = self.max_pool(x)
-
-        # Two different branches of ECA module
-        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
-
-        # Multi-scale information fusion
-        # y = self.sigmoid(y)
-
-        return x * y.expand_as(x)
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/LICENSE b/comfy_extras/chainner_models/architecture/OmniSR/LICENSE
deleted file mode 100644
index 261eeb9e9..000000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
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-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
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-   You may obtain a copy of the License at
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-       http://www.apache.org/licenses/LICENSE-2.0
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-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OSA.py b/comfy_extras/chainner_models/architecture/OmniSR/OSA.py
deleted file mode 100644
index d7a129696..000000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/OSA.py
+++ /dev/null
@@ -1,577 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: OSA.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Sunday, 23rd April 2023 3:07:42 pm
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange, repeat
-from einops.layers.torch import Rearrange, Reduce
-from torch import einsum, nn
-
-from .layernorm import LayerNorm2d
-
-# helpers
-
-
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    return val if exists(val) else d
-
-
-def cast_tuple(val, length=1):
-    return val if isinstance(val, tuple) else ((val,) * length)
-
-
-# helper classes
-
-
-class PreNormResidual(nn.Module):
-    def __init__(self, dim, fn):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim)
-        self.fn = fn
-
-    def forward(self, x):
-        return self.fn(self.norm(x)) + x
-
-
-class Conv_PreNormResidual(nn.Module):
-    def __init__(self, dim, fn):
-        super().__init__()
-        self.norm = LayerNorm2d(dim)
-        self.fn = fn
-
-    def forward(self, x):
-        return self.fn(self.norm(x)) + x
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, mult=2, dropout=0.0):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        self.net = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU(),
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim),
-            nn.Dropout(dropout),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class Conv_FeedForward(nn.Module):
-    def __init__(self, dim, mult=2, dropout=0.0):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        self.net = nn.Sequential(
-            nn.Conv2d(dim, inner_dim, 1, 1, 0),
-            nn.GELU(),
-            nn.Dropout(dropout),
-            nn.Conv2d(inner_dim, dim, 1, 1, 0),
-            nn.Dropout(dropout),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class Gated_Conv_FeedForward(nn.Module):
-    def __init__(self, dim, mult=1, bias=False, dropout=0.0):
-        super().__init__()
-
-        hidden_features = int(dim * mult)
-
-        self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)
-
-        self.dwconv = nn.Conv2d(
-            hidden_features * 2,
-            hidden_features * 2,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            groups=hidden_features * 2,
-            bias=bias,
-        )
-
-        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        x = self.project_in(x)
-        x1, x2 = self.dwconv(x).chunk(2, dim=1)
-        x = F.gelu(x1) * x2
-        x = self.project_out(x)
-        return x
-
-
-# MBConv
-
-
-class SqueezeExcitation(nn.Module):
-    def __init__(self, dim, shrinkage_rate=0.25):
-        super().__init__()
-        hidden_dim = int(dim * shrinkage_rate)
-
-        self.gate = nn.Sequential(
-            Reduce("b c h w -> b c", "mean"),
-            nn.Linear(dim, hidden_dim, bias=False),
-            nn.SiLU(),
-            nn.Linear(hidden_dim, dim, bias=False),
-            nn.Sigmoid(),
-            Rearrange("b c -> b c 1 1"),
-        )
-
-    def forward(self, x):
-        return x * self.gate(x)
-
-
-class MBConvResidual(nn.Module):
-    def __init__(self, fn, dropout=0.0):
-        super().__init__()
-        self.fn = fn
-        self.dropsample = Dropsample(dropout)
-
-    def forward(self, x):
-        out = self.fn(x)
-        out = self.dropsample(out)
-        return out + x
-
-
-class Dropsample(nn.Module):
-    def __init__(self, prob=0):
-        super().__init__()
-        self.prob = prob
-
-    def forward(self, x):
-        device = x.device
-
-        if self.prob == 0.0 or (not self.training):
-            return x
-
-        keep_mask = (
-            torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_()
-            > self.prob
-        )
-        return x * keep_mask / (1 - self.prob)
-
-
-def MBConv(
-    dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0
-):
-    hidden_dim = int(expansion_rate * dim_out)
-    stride = 2 if downsample else 1
-
-    net = nn.Sequential(
-        nn.Conv2d(dim_in, hidden_dim, 1),
-        # nn.BatchNorm2d(hidden_dim),
-        nn.GELU(),
-        nn.Conv2d(
-            hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim
-        ),
-        # nn.BatchNorm2d(hidden_dim),
-        nn.GELU(),
-        SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate),
-        nn.Conv2d(hidden_dim, dim_out, 1),
-        # nn.BatchNorm2d(dim_out)
-    )
-
-    if dim_in == dim_out and not downsample:
-        net = MBConvResidual(net, dropout=dropout)
-
-    return net
-
-
-# attention related classes
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_head=32,
-        dropout=0.0,
-        window_size=7,
-        with_pe=True,
-    ):
-        super().__init__()
-        assert (
-            dim % dim_head
-        ) == 0, "dimension should be divisible by dimension per head"
-
-        self.heads = dim // dim_head
-        self.scale = dim_head**-0.5
-        self.with_pe = with_pe
-
-        self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
-
-        self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))
-
-        self.to_out = nn.Sequential(
-            nn.Linear(dim, dim, bias=False), nn.Dropout(dropout)
-        )
-
-        # relative positional bias
-        if self.with_pe:
-            self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads)
-
-            pos = torch.arange(window_size)
-            grid = torch.stack(torch.meshgrid(pos, pos))
-            grid = rearrange(grid, "c i j -> (i j) c")
-            rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange(
-                grid, "j ... -> 1 j ..."
-            )
-            rel_pos += window_size - 1
-            rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum(
-                dim=-1
-            )
-
-            self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False)
-
-    def forward(self, x):
-        batch, height, width, window_height, window_width, _, device, h = (
-            *x.shape,
-            x.device,
-            self.heads,
-        )
-
-        # flatten
-
-        x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d")
-
-        # project for queries, keys, values
-
-        q, k, v = self.to_qkv(x).chunk(3, dim=-1)
-
-        # split heads
-
-        q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v))
-
-        # scale
-
-        q = q * self.scale
-
-        # sim
-
-        sim = einsum("b h i d, b h j d -> b h i j", q, k)
-
-        # add positional bias
-        if self.with_pe:
-            bias = self.rel_pos_bias(self.rel_pos_indices)
-            sim = sim + rearrange(bias, "i j h -> h i j")
-
-        # attention
-
-        attn = self.attend(sim)
-
-        # aggregate
-
-        out = einsum("b h i j, b h j d -> b h i d", attn, v)
-
-        # merge heads
-
-        out = rearrange(
-            out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width
-        )
-
-        # combine heads out
-
-        out = self.to_out(out)
-        return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width)
-
-
-class Block_Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_head=32,
-        bias=False,
-        dropout=0.0,
-        window_size=7,
-        with_pe=True,
-    ):
-        super().__init__()
-        assert (
-            dim % dim_head
-        ) == 0, "dimension should be divisible by dimension per head"
-
-        self.heads = dim // dim_head
-        self.ps = window_size
-        self.scale = dim_head**-0.5
-        self.with_pe = with_pe
-
-        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
-        self.qkv_dwconv = nn.Conv2d(
-            dim * 3,
-            dim * 3,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            groups=dim * 3,
-            bias=bias,
-        )
-
-        self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))
-
-        self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        # project for queries, keys, values
-        b, c, h, w = x.shape
-
-        qkv = self.qkv_dwconv(self.qkv(x))
-        q, k, v = qkv.chunk(3, dim=1)
-
-        # split heads
-
-        q, k, v = map(
-            lambda t: rearrange(
-                t,
-                "b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d",
-                h=self.heads,
-                w1=self.ps,
-                w2=self.ps,
-            ),
-            (q, k, v),
-        )
-
-        # scale
-
-        q = q * self.scale
-
-        # sim
-
-        sim = einsum("b h i d, b h j d -> b h i j", q, k)
-
-        # attention
-        attn = self.attend(sim)
-
-        # aggregate
-
-        out = einsum("b h i j, b h j d -> b h i d", attn, v)
-
-        # merge heads
-        out = rearrange(
-            out,
-            "(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)",
-            x=h // self.ps,
-            y=w // self.ps,
-            head=self.heads,
-            w1=self.ps,
-            w2=self.ps,
-        )
-
-        out = self.to_out(out)
-        return out
-
-
-class Channel_Attention(nn.Module):
-    def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):
-        super(Channel_Attention, self).__init__()
-        self.heads = heads
-
-        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
-
-        self.ps = window_size
-
-        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
-        self.qkv_dwconv = nn.Conv2d(
-            dim * 3,
-            dim * 3,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            groups=dim * 3,
-            bias=bias,
-        )
-        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-
-        qkv = self.qkv_dwconv(self.qkv(x))
-        qkv = qkv.chunk(3, dim=1)
-
-        q, k, v = map(
-            lambda t: rearrange(
-                t,
-                "b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)",
-                ph=self.ps,
-                pw=self.ps,
-                head=self.heads,
-            ),
-            qkv,
-        )
-
-        q = F.normalize(q, dim=-1)
-        k = F.normalize(k, dim=-1)
-
-        attn = (q @ k.transpose(-2, -1)) * self.temperature
-        attn = attn.softmax(dim=-1)
-        out = attn @ v
-
-        out = rearrange(
-            out,
-            "b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)",
-            h=h // self.ps,
-            w=w // self.ps,
-            ph=self.ps,
-            pw=self.ps,
-            head=self.heads,
-        )
-
-        out = self.project_out(out)
-
-        return out
-
-
-class Channel_Attention_grid(nn.Module):
-    def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):
-        super(Channel_Attention_grid, self).__init__()
-        self.heads = heads
-
-        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
-
-        self.ps = window_size
-
-        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
-        self.qkv_dwconv = nn.Conv2d(
-            dim * 3,
-            dim * 3,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            groups=dim * 3,
-            bias=bias,
-        )
-        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-
-        qkv = self.qkv_dwconv(self.qkv(x))
-        qkv = qkv.chunk(3, dim=1)
-
-        q, k, v = map(
-            lambda t: rearrange(
-                t,
-                "b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)",
-                ph=self.ps,
-                pw=self.ps,
-                head=self.heads,
-            ),
-            qkv,
-        )
-
-        q = F.normalize(q, dim=-1)
-        k = F.normalize(k, dim=-1)
-
-        attn = (q @ k.transpose(-2, -1)) * self.temperature
-        attn = attn.softmax(dim=-1)
-        out = attn @ v
-
-        out = rearrange(
-            out,
-            "b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)",
-            h=h // self.ps,
-            w=w // self.ps,
-            ph=self.ps,
-            pw=self.ps,
-            head=self.heads,
-        )
-
-        out = self.project_out(out)
-
-        return out
-
-
-class OSA_Block(nn.Module):
-    def __init__(
-        self,
-        channel_num=64,
-        bias=True,
-        ffn_bias=True,
-        window_size=8,
-        with_pe=False,
-        dropout=0.0,
-    ):
-        super(OSA_Block, self).__init__()
-
-        w = window_size
-
-        self.layer = nn.Sequential(
-            MBConv(
-                channel_num,
-                channel_num,
-                downsample=False,
-                expansion_rate=1,
-                shrinkage_rate=0.25,
-            ),
-            Rearrange(
-                "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w
-            ),  # block-like attention
-            PreNormResidual(
-                channel_num,
-                Attention(
-                    dim=channel_num,
-                    dim_head=channel_num // 4,
-                    dropout=dropout,
-                    window_size=window_size,
-                    with_pe=with_pe,
-                ),
-            ),
-            Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"),
-            Conv_PreNormResidual(
-                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
-            ),
-            # channel-like attention
-            Conv_PreNormResidual(
-                channel_num,
-                Channel_Attention(
-                    dim=channel_num, heads=4, dropout=dropout, window_size=window_size
-                ),
-            ),
-            Conv_PreNormResidual(
-                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
-            ),
-            Rearrange(
-                "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w
-            ),  # grid-like attention
-            PreNormResidual(
-                channel_num,
-                Attention(
-                    dim=channel_num,
-                    dim_head=channel_num // 4,
-                    dropout=dropout,
-                    window_size=window_size,
-                    with_pe=with_pe,
-                ),
-            ),
-            Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"),
-            Conv_PreNormResidual(
-                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
-            ),
-            # channel-like attention
-            Conv_PreNormResidual(
-                channel_num,
-                Channel_Attention_grid(
-                    dim=channel_num, heads=4, dropout=dropout, window_size=window_size
-                ),
-            ),
-            Conv_PreNormResidual(
-                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
-            ),
-        )
-
-    def forward(self, x):
-        out = self.layer(x)
-        return out
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py b/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py
deleted file mode 100644
index 477e81f9d..000000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: OSAG.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Sunday, 23rd April 2023 3:08:49 pm
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-
-import torch.nn as nn
-
-from .esa import ESA
-from .OSA import OSA_Block
-
-
-class OSAG(nn.Module):
-    def __init__(
-        self,
-        channel_num=64,
-        bias=True,
-        block_num=4,
-        ffn_bias=False,
-        window_size=0,
-        pe=False,
-    ):
-        super(OSAG, self).__init__()
-
-        # print("window_size: %d" % (window_size))
-        # print("with_pe", pe)
-        # print("ffn_bias: %d" % (ffn_bias))
-
-        # block_script_name = kwargs.get("block_script_name", "OSA")
-        # block_class_name = kwargs.get("block_class_name", "OSA_Block")
-
-        # script_name = "." + block_script_name
-        # package = __import__(script_name, fromlist=True)
-        block_class = OSA_Block  # getattr(package, block_class_name)
-        group_list = []
-        for _ in range(block_num):
-            temp_res = block_class(
-                channel_num,
-                bias,
-                ffn_bias=ffn_bias,
-                window_size=window_size,
-                with_pe=pe,
-            )
-            group_list.append(temp_res)
-        group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias))
-        self.residual_layer = nn.Sequential(*group_list)
-        esa_channel = max(channel_num // 4, 16)
-        self.esa = ESA(esa_channel, channel_num)
-
-    def forward(self, x):
-        out = self.residual_layer(x)
-        out = out + x
-        return self.esa(out)
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py b/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py
deleted file mode 100644
index 1e1c3f35e..000000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py
+++ /dev/null
@@ -1,143 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: OmniSR.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Sunday, 23rd April 2023 3:06:36 pm
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .OSAG import OSAG
-from .pixelshuffle import pixelshuffle_block
-
-
-class OmniSR(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-        **kwargs,
-    ):
-        super(OmniSR, self).__init__()
-        self.state = state_dict
-
-        bias = True  # Fine to assume this for now
-        block_num = 1  # Fine to assume this for now
-        ffn_bias = True
-        pe = True
-
-        num_feat = state_dict["input.weight"].shape[0] or 64
-        num_in_ch = state_dict["input.weight"].shape[1] or 3
-        num_out_ch = num_in_ch  # we can just assume this for now. pixelshuffle smh
-
-        pixelshuffle_shape = state_dict["up.0.weight"].shape[0]
-        up_scale = math.sqrt(pixelshuffle_shape / num_out_ch)
-        if up_scale - int(up_scale) > 0:
-            print(
-                "out_nc is probably different than in_nc, scale calculation might be wrong"
-            )
-        up_scale = int(up_scale)
-        res_num = 0
-        for key in state_dict.keys():
-            if "residual_layer" in key:
-                temp_res_num = int(key.split(".")[1])
-                if temp_res_num > res_num:
-                    res_num = temp_res_num
-        res_num = res_num + 1  # zero-indexed
-
-        residual_layer = []
-        self.res_num = res_num
-
-        if (
-            "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight"
-            in state_dict.keys()
-        ):
-            rel_pos_bias_weight = state_dict[
-                "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight"
-            ].shape[0]
-            self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2)
-        else:
-            self.window_size = 8
-
-        self.up_scale = up_scale
-
-        for _ in range(res_num):
-            temp_res = OSAG(
-                channel_num=num_feat,
-                bias=bias,
-                block_num=block_num,
-                ffn_bias=ffn_bias,
-                window_size=self.window_size,
-                pe=pe,
-            )
-            residual_layer.append(temp_res)
-        self.residual_layer = nn.Sequential(*residual_layer)
-        self.input = nn.Conv2d(
-            in_channels=num_in_ch,
-            out_channels=num_feat,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=bias,
-        )
-        self.output = nn.Conv2d(
-            in_channels=num_feat,
-            out_channels=num_feat,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=bias,
-        )
-        self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias)
-
-        # self.tail   = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias)
-
-        # for m in self.modules():
-        #     if isinstance(m, nn.Conv2d):
-        #         n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-        #         m.weight.data.normal_(0, sqrt(2. / n))
-
-        # chaiNNer specific stuff
-        self.model_arch = "OmniSR"
-        self.sub_type = "SR"
-        self.in_nc = num_in_ch
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.scale = up_scale
-
-        self.supports_fp16 = True  # TODO: Test this
-        self.supports_bfp16 = True
-        self.min_size_restriction = 16
-
-        self.load_state_dict(state_dict, strict=False)
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        # import pdb; pdb.set_trace()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        # x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0)
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        residual = self.input(x)
-        out = self.residual_layer(residual)
-
-        # origin
-        out = torch.add(self.output(out), residual)
-        out = self.up(out)
-
-        out = out[:, :, : H * self.up_scale, : W * self.up_scale]
-        return out
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/esa.py b/comfy_extras/chainner_models/architecture/OmniSR/esa.py
deleted file mode 100644
index f9ce7f7a6..000000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/esa.py
+++ /dev/null
@@ -1,294 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: esa.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Thursday, 20th April 2023 9:28:06 am
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .layernorm import LayerNorm2d
-
-
-def moment(x, dim=(2, 3), k=2):
-    assert len(x.size()) == 4
-    mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1)
-    mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim)
-    return mk
-
-
-class ESA(nn.Module):
-    """
-    Modification of Enhanced Spatial Attention (ESA), which is proposed by
-    `Residual Feature Aggregation Network for Image Super-Resolution`
-    Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes
-    are deleted.
-    """
-
-    def __init__(self, esa_channels, n_feats, conv=nn.Conv2d):
-        super(ESA, self).__init__()
-        f = esa_channels
-        self.conv1 = conv(n_feats, f, kernel_size=1)
-        self.conv_f = conv(f, f, kernel_size=1)
-        self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0)
-        self.conv3 = conv(f, f, kernel_size=3, padding=1)
-        self.conv4 = conv(f, n_feats, kernel_size=1)
-        self.sigmoid = nn.Sigmoid()
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        c1_ = self.conv1(x)
-        c1 = self.conv2(c1_)
-        v_max = F.max_pool2d(c1, kernel_size=7, stride=3)
-        c3 = self.conv3(v_max)
-        c3 = F.interpolate(
-            c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False
-        )
-        cf = self.conv_f(c1_)
-        c4 = self.conv4(c3 + cf)
-        m = self.sigmoid(c4)
-        return x * m
-
-
-class LK_ESA(nn.Module):
-    def __init__(
-        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
-    ):
-        super(LK_ESA, self).__init__()
-        f = esa_channels
-        self.conv1 = conv(n_feats, f, kernel_size=1)
-        self.conv_f = conv(f, f, kernel_size=1)
-
-        kernel_size = 17
-        kernel_expand = kernel_expand
-        padding = kernel_size // 2
-
-        self.vec_conv = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(1, kernel_size),
-            padding=(0, padding),
-            groups=2,
-            bias=bias,
-        )
-        self.vec_conv3x1 = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(1, 3),
-            padding=(0, 1),
-            groups=2,
-            bias=bias,
-        )
-
-        self.hor_conv = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(kernel_size, 1),
-            padding=(padding, 0),
-            groups=2,
-            bias=bias,
-        )
-        self.hor_conv1x3 = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(3, 1),
-            padding=(1, 0),
-            groups=2,
-            bias=bias,
-        )
-
-        self.conv4 = conv(f, n_feats, kernel_size=1)
-        self.sigmoid = nn.Sigmoid()
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        c1_ = self.conv1(x)
-
-        res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)
-        res = self.hor_conv(res) + self.hor_conv1x3(res)
-
-        cf = self.conv_f(c1_)
-        c4 = self.conv4(res + cf)
-        m = self.sigmoid(c4)
-        return x * m
-
-
-class LK_ESA_LN(nn.Module):
-    def __init__(
-        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
-    ):
-        super(LK_ESA_LN, self).__init__()
-        f = esa_channels
-        self.conv1 = conv(n_feats, f, kernel_size=1)
-        self.conv_f = conv(f, f, kernel_size=1)
-
-        kernel_size = 17
-        kernel_expand = kernel_expand
-        padding = kernel_size // 2
-
-        self.norm = LayerNorm2d(n_feats)
-
-        self.vec_conv = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(1, kernel_size),
-            padding=(0, padding),
-            groups=2,
-            bias=bias,
-        )
-        self.vec_conv3x1 = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(1, 3),
-            padding=(0, 1),
-            groups=2,
-            bias=bias,
-        )
-
-        self.hor_conv = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(kernel_size, 1),
-            padding=(padding, 0),
-            groups=2,
-            bias=bias,
-        )
-        self.hor_conv1x3 = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(3, 1),
-            padding=(1, 0),
-            groups=2,
-            bias=bias,
-        )
-
-        self.conv4 = conv(f, n_feats, kernel_size=1)
-        self.sigmoid = nn.Sigmoid()
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        c1_ = self.norm(x)
-        c1_ = self.conv1(c1_)
-
-        res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)
-        res = self.hor_conv(res) + self.hor_conv1x3(res)
-
-        cf = self.conv_f(c1_)
-        c4 = self.conv4(res + cf)
-        m = self.sigmoid(c4)
-        return x * m
-
-
-class AdaGuidedFilter(nn.Module):
-    def __init__(
-        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
-    ):
-        super(AdaGuidedFilter, self).__init__()
-
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Conv2d(
-            in_channels=n_feats,
-            out_channels=1,
-            kernel_size=1,
-            padding=0,
-            stride=1,
-            groups=1,
-            bias=True,
-        )
-
-        self.r = 5
-
-    def box_filter(self, x, r):
-        channel = x.shape[1]
-        kernel_size = 2 * r + 1
-        weight = 1.0 / (kernel_size**2)
-        box_kernel = weight * torch.ones(
-            (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device
-        )
-        output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel)
-        return output
-
-    def forward(self, x):
-        _, _, H, W = x.shape
-        N = self.box_filter(
-            torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r
-        )
-
-        # epsilon = self.fc(self.gap(x))
-        # epsilon = torch.pow(epsilon, 2)
-        epsilon = 1e-2
-
-        mean_x = self.box_filter(x, self.r) / N
-        var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x
-
-        A = var_x / (var_x + epsilon)
-        b = (1 - A) * mean_x
-        m = A * x + b
-
-        # mean_A = self.box_filter(A, self.r) / N
-        # mean_b = self.box_filter(b, self.r) / N
-        # m = mean_A * x + mean_b
-        return x * m
-
-
-class AdaConvGuidedFilter(nn.Module):
-    def __init__(
-        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
-    ):
-        super(AdaConvGuidedFilter, self).__init__()
-        f = esa_channels
-
-        self.conv_f = conv(f, f, kernel_size=1)
-
-        kernel_size = 17
-        kernel_expand = kernel_expand
-        padding = kernel_size // 2
-
-        self.vec_conv = nn.Conv2d(
-            in_channels=f,
-            out_channels=f,
-            kernel_size=(1, kernel_size),
-            padding=(0, padding),
-            groups=f,
-            bias=bias,
-        )
-
-        self.hor_conv = nn.Conv2d(
-            in_channels=f,
-            out_channels=f,
-            kernel_size=(kernel_size, 1),
-            padding=(padding, 0),
-            groups=f,
-            bias=bias,
-        )
-
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Conv2d(
-            in_channels=f,
-            out_channels=f,
-            kernel_size=1,
-            padding=0,
-            stride=1,
-            groups=1,
-            bias=True,
-        )
-
-    def forward(self, x):
-        y = self.vec_conv(x)
-        y = self.hor_conv(y)
-
-        sigma = torch.pow(y, 2)
-        epsilon = self.fc(self.gap(y))
-
-        weight = sigma / (sigma + epsilon)
-
-        m = weight * x + (1 - weight)
-
-        return x * m
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py b/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py
deleted file mode 100644
index 731a25f75..000000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: layernorm.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Thursday, 20th April 2023 9:28:20 am
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-import torch
-import torch.nn as nn
-
-
-class LayerNormFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x, weight, bias, eps):
-        ctx.eps = eps
-        N, C, H, W = x.size()
-        mu = x.mean(1, keepdim=True)
-        var = (x - mu).pow(2).mean(1, keepdim=True)
-        y = (x - mu) / (var + eps).sqrt()
-        ctx.save_for_backward(y, var, weight)
-        y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
-        return y
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        eps = ctx.eps
-
-        N, C, H, W = grad_output.size()
-        y, var, weight = ctx.saved_variables
-        g = grad_output * weight.view(1, C, 1, 1)
-        mean_g = g.mean(dim=1, keepdim=True)
-
-        mean_gy = (g * y).mean(dim=1, keepdim=True)
-        gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
-        return (
-            gx,
-            (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0),
-            grad_output.sum(dim=3).sum(dim=2).sum(dim=0),
-            None,
-        )
-
-
-class LayerNorm2d(nn.Module):
-    def __init__(self, channels, eps=1e-6):
-        super(LayerNorm2d, self).__init__()
-        self.register_parameter("weight", nn.Parameter(torch.ones(channels)))
-        self.register_parameter("bias", nn.Parameter(torch.zeros(channels)))
-        self.eps = eps
-
-    def forward(self, x):
-        return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
-
-
-class GRN(nn.Module):
-    """GRN (Global Response Normalization) layer"""
-
-    def __init__(self, dim):
-        super().__init__()
-        self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1))
-        self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1))
-
-    def forward(self, x):
-        Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True)
-        Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6)
-        return self.gamma * (x * Nx) + self.beta + x
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py b/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py
deleted file mode 100644
index 4260fb7c9..000000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: pixelshuffle.py
-# Created Date: Friday July 1st 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Friday, 1st July 2022 10:18:39 am
-# Modified By: Chen Xuanhong
-# Copyright (c) 2022 Shanghai Jiao Tong University
-#############################################################
-
-import torch.nn as nn
-
-
-def pixelshuffle_block(
-    in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False
-):
-    """
-    Upsample features according to `upscale_factor`.
-    """
-    padding = kernel_size // 2
-    conv = nn.Conv2d(
-        in_channels,
-        out_channels * (upscale_factor**2),
-        kernel_size,
-        padding=1,
-        bias=bias,
-    )
-    pixel_shuffle = nn.PixelShuffle(upscale_factor)
-    return nn.Sequential(*[conv, pixel_shuffle])
diff --git a/comfy_extras/chainner_models/architecture/RRDB.py b/comfy_extras/chainner_models/architecture/RRDB.py
deleted file mode 100644
index b50db7c24..000000000
--- a/comfy_extras/chainner_models/architecture/RRDB.py
+++ /dev/null
@@ -1,296 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import functools
-import math
-import re
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from . import block as B
-
-
-# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/ESRGAN.py
-# Which enhanced stuff that was already here
-class RRDBNet(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-        norm=None,
-        act: str = "leakyrelu",
-        upsampler: str = "upconv",
-        mode: B.ConvMode = "CNA",
-    ) -> None:
-        """
-        ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks.
-        By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao,
-        and Chen Change Loy.
-        This is old-arch Residual in Residual Dense Block Network and is not
-        the newest revision that's available at github.com/xinntao/ESRGAN.
-        This is on purpose, the newest Network has severely limited the
-        potential use of the Network with no benefits.
-        This network supports model files from both new and old-arch.
-        Args:
-            norm: Normalization layer
-            act: Activation layer
-            upsampler: Upsample layer. upconv, pixel_shuffle
-            mode: Convolution mode
-        """
-        super(RRDBNet, self).__init__()
-        self.model_arch = "ESRGAN"
-        self.sub_type = "SR"
-
-        self.state = state_dict
-        self.norm = norm
-        self.act = act
-        self.upsampler = upsampler
-        self.mode = mode
-
-        self.state_map = {
-            # currently supports old, new, and newer RRDBNet arch models
-            # ESRGAN, BSRGAN/RealSR, Real-ESRGAN
-            "model.0.weight": ("conv_first.weight",),
-            "model.0.bias": ("conv_first.bias",),
-            "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"),
-            "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"),
-            r"model.1.sub.\1.RDB\2.conv\3.0.\4": (
-                r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)",
-                r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)",
-            ),
-        }
-        if "params_ema" in self.state:
-            self.state = self.state["params_ema"]
-            # self.model_arch = "RealESRGAN"
-        self.num_blocks = self.get_num_blocks()
-        self.plus = any("conv1x1" in k for k in self.state.keys())
-        if self.plus:
-            self.model_arch = "ESRGAN+"
-
-        self.state = self.new_to_old_arch(self.state)
-
-        self.key_arr = list(self.state.keys())
-
-        self.in_nc: int = self.state[self.key_arr[0]].shape[1]
-        self.out_nc: int = self.state[self.key_arr[-1]].shape[0]
-
-        self.scale: int = self.get_scale()
-        self.num_filters: int = self.state[self.key_arr[0]].shape[0]
-
-        c2x2 = False
-        if self.state["model.0.weight"].shape[-2] == 2:
-            c2x2 = True
-            self.scale = round(math.sqrt(self.scale / 4))
-            self.model_arch = "ESRGAN-2c2"
-
-        self.supports_fp16 = True
-        self.supports_bfp16 = True
-        self.min_size_restriction = None
-
-        # Detect if pixelunshuffle was used (Real-ESRGAN)
-        if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in (
-            self.in_nc / 4,
-            self.in_nc / 16,
-        ):
-            self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc))
-        else:
-            self.shuffle_factor = None
-
-        upsample_block = {
-            "upconv": B.upconv_block,
-            "pixel_shuffle": B.pixelshuffle_block,
-        }.get(self.upsampler)
-        if upsample_block is None:
-            raise NotImplementedError(f"Upsample mode [{self.upsampler}] is not found")
-
-        if self.scale == 3:
-            upsample_blocks = upsample_block(
-                in_nc=self.num_filters,
-                out_nc=self.num_filters,
-                upscale_factor=3,
-                act_type=self.act,
-                c2x2=c2x2,
-            )
-        else:
-            upsample_blocks = [
-                upsample_block(
-                    in_nc=self.num_filters,
-                    out_nc=self.num_filters,
-                    act_type=self.act,
-                    c2x2=c2x2,
-                )
-                for _ in range(int(math.log(self.scale, 2)))
-            ]
-
-        self.model = B.sequential(
-            # fea conv
-            B.conv_block(
-                in_nc=self.in_nc,
-                out_nc=self.num_filters,
-                kernel_size=3,
-                norm_type=None,
-                act_type=None,
-                c2x2=c2x2,
-            ),
-            B.ShortcutBlock(
-                B.sequential(
-                    # rrdb blocks
-                    *[
-                        B.RRDB(
-                            nf=self.num_filters,
-                            kernel_size=3,
-                            gc=32,
-                            stride=1,
-                            bias=True,
-                            pad_type="zero",
-                            norm_type=self.norm,
-                            act_type=self.act,
-                            mode="CNA",
-                            plus=self.plus,
-                            c2x2=c2x2,
-                        )
-                        for _ in range(self.num_blocks)
-                    ],
-                    # lr conv
-                    B.conv_block(
-                        in_nc=self.num_filters,
-                        out_nc=self.num_filters,
-                        kernel_size=3,
-                        norm_type=self.norm,
-                        act_type=None,
-                        mode=self.mode,
-                        c2x2=c2x2,
-                    ),
-                )
-            ),
-            *upsample_blocks,
-            # hr_conv0
-            B.conv_block(
-                in_nc=self.num_filters,
-                out_nc=self.num_filters,
-                kernel_size=3,
-                norm_type=None,
-                act_type=self.act,
-                c2x2=c2x2,
-            ),
-            # hr_conv1
-            B.conv_block(
-                in_nc=self.num_filters,
-                out_nc=self.out_nc,
-                kernel_size=3,
-                norm_type=None,
-                act_type=None,
-                c2x2=c2x2,
-            ),
-        )
-
-        # Adjust these properties for calculations outside of the model
-        if self.shuffle_factor:
-            self.in_nc //= self.shuffle_factor**2
-            self.scale //= self.shuffle_factor
-
-        self.load_state_dict(self.state, strict=False)
-
-    def new_to_old_arch(self, state):
-        """Convert a new-arch model state dictionary to an old-arch dictionary."""
-        if "params_ema" in state:
-            state = state["params_ema"]
-
-        if "conv_first.weight" not in state:
-            # model is already old arch, this is a loose check, but should be sufficient
-            return state
-
-        # add nb to state keys
-        for kind in ("weight", "bias"):
-            self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[
-                f"model.1.sub./NB/.{kind}"
-            ]
-            del self.state_map[f"model.1.sub./NB/.{kind}"]
-
-        old_state = OrderedDict()
-        for old_key, new_keys in self.state_map.items():
-            for new_key in new_keys:
-                if r"\1" in old_key:
-                    for k, v in state.items():
-                        sub = re.sub(new_key, old_key, k)
-                        if sub != k:
-                            old_state[sub] = v
-                else:
-                    if new_key in state:
-                        old_state[old_key] = state[new_key]
-
-        # upconv layers
-        max_upconv = 0
-        for key in state.keys():
-            match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key)
-            if match is not None:
-                _, key_num, key_type = match.groups()
-                old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key]
-                max_upconv = max(max_upconv, int(key_num) * 3)
-
-        # final layers
-        for key in state.keys():
-            if key in ("HRconv.weight", "conv_hr.weight"):
-                old_state[f"model.{max_upconv + 2}.weight"] = state[key]
-            elif key in ("HRconv.bias", "conv_hr.bias"):
-                old_state[f"model.{max_upconv + 2}.bias"] = state[key]
-            elif key in ("conv_last.weight",):
-                old_state[f"model.{max_upconv + 4}.weight"] = state[key]
-            elif key in ("conv_last.bias",):
-                old_state[f"model.{max_upconv + 4}.bias"] = state[key]
-
-        # Sort by first numeric value of each layer
-        def compare(item1, item2):
-            parts1 = item1.split(".")
-            parts2 = item2.split(".")
-            int1 = int(parts1[1])
-            int2 = int(parts2[1])
-            return int1 - int2
-
-        sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare))
-
-        # Rebuild the output dict in the right order
-        out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys)
-
-        return out_dict
-
-    def get_scale(self, min_part: int = 6) -> int:
-        n = 0
-        for part in list(self.state):
-            parts = part.split(".")[1:]
-            if len(parts) == 2:
-                part_num = int(parts[0])
-                if part_num > min_part and parts[1] == "weight":
-                    n += 1
-        return 2**n
-
-    def get_num_blocks(self) -> int:
-        nbs = []
-        state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + (
-            r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)",
-        )
-        for state_key in state_keys:
-            for k in self.state:
-                m = re.search(state_key, k)
-                if m:
-                    nbs.append(int(m.group(1)))
-            if nbs:
-                break
-        return max(*nbs) + 1
-
-    def forward(self, x):
-        if self.shuffle_factor:
-            _, _, h, w = x.size()
-            mod_pad_h = (
-                self.shuffle_factor - h % self.shuffle_factor
-            ) % self.shuffle_factor
-            mod_pad_w = (
-                self.shuffle_factor - w % self.shuffle_factor
-            ) % self.shuffle_factor
-            x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-            x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor)
-            x = self.model(x)
-            return x[:, :, : h * self.scale, : w * self.scale]
-        return self.model(x)
diff --git a/comfy_extras/chainner_models/architecture/SCUNet.py b/comfy_extras/chainner_models/architecture/SCUNet.py
deleted file mode 100644
index b8354a873..000000000
--- a/comfy_extras/chainner_models/architecture/SCUNet.py
+++ /dev/null
@@ -1,455 +0,0 @@
-# pylint: skip-file
-# -----------------------------------------------------------------------------------
-# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278
-# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc
-# -----------------------------------------------------------------------------------
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-from einops.layers.torch import Rearrange
-
-from .timm.drop import DropPath
-from .timm.weight_init import trunc_normal_
-
-
-# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py
-class WMSA(nn.Module):
-    """Self-attention module in Swin Transformer"""
-
-    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
-        super(WMSA, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.head_dim = head_dim
-        self.scale = self.head_dim**-0.5
-        self.n_heads = input_dim // head_dim
-        self.window_size = window_size
-        self.type = type
-        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
-
-        self.relative_position_params = nn.Parameter(
-            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)
-        )
-        # TODO recover
-        # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1))
-        self.relative_position_params = nn.Parameter(
-            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)
-        )
-
-        self.linear = nn.Linear(self.input_dim, self.output_dim)
-
-        trunc_normal_(self.relative_position_params, std=0.02)
-        self.relative_position_params = torch.nn.Parameter(
-            self.relative_position_params.view(
-                2 * window_size - 1, 2 * window_size - 1, self.n_heads
-            )
-            .transpose(1, 2)
-            .transpose(0, 1)
-        )
-
-    def generate_mask(self, h, w, p, shift):
-        """generating the mask of SW-MSA
-        Args:
-            shift: shift parameters in CyclicShift.
-        Returns:
-            attn_mask: should be (1 1 w p p),
-        """
-        # supporting square.
-        attn_mask = torch.zeros(
-            h,
-            w,
-            p,
-            p,
-            p,
-            p,
-            dtype=torch.bool,
-            device=self.relative_position_params.device,
-        )
-        if self.type == "W":
-            return attn_mask
-
-        s = p - shift
-        attn_mask[-1, :, :s, :, s:, :] = True
-        attn_mask[-1, :, s:, :, :s, :] = True
-        attn_mask[:, -1, :, :s, :, s:] = True
-        attn_mask[:, -1, :, s:, :, :s] = True
-        attn_mask = rearrange(
-            attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)"
-        )
-        return attn_mask
-
-    def forward(self, x):
-        """Forward pass of Window Multi-head Self-attention module.
-        Args:
-            x: input tensor with shape of [b h w c];
-            attn_mask: attention mask, fill -inf where the value is True;
-        Returns:
-            output: tensor shape [b h w c]
-        """
-        if self.type != "W":
-            x = torch.roll(
-                x,
-                shifts=(-(self.window_size // 2), -(self.window_size // 2)),
-                dims=(1, 2),
-            )
-
-        x = rearrange(
-            x,
-            "b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c",
-            p1=self.window_size,
-            p2=self.window_size,
-        )
-        h_windows = x.size(1)
-        w_windows = x.size(2)
-        # square validation
-        # assert h_windows == w_windows
-
-        x = rearrange(
-            x,
-            "b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c",
-            p1=self.window_size,
-            p2=self.window_size,
-        )
-        qkv = self.embedding_layer(x)
-        q, k, v = rearrange(
-            qkv, "b nw np (threeh c) -> threeh b nw np c", c=self.head_dim
-        ).chunk(3, dim=0)
-        sim = torch.einsum("hbwpc,hbwqc->hbwpq", q, k) * self.scale
-        # Adding learnable relative embedding
-        sim = sim + rearrange(self.relative_embedding(), "h p q -> h 1 1 p q")
-        # Using Attn Mask to distinguish different subwindows.
-        if self.type != "W":
-            attn_mask = self.generate_mask(
-                h_windows, w_windows, self.window_size, shift=self.window_size // 2
-            )
-            sim = sim.masked_fill_(attn_mask, float("-inf"))
-
-        probs = nn.functional.softmax(sim, dim=-1)
-        output = torch.einsum("hbwij,hbwjc->hbwic", probs, v)
-        output = rearrange(output, "h b w p c -> b w p (h c)")
-        output = self.linear(output)
-        output = rearrange(
-            output,
-            "b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c",
-            w1=h_windows,
-            p1=self.window_size,
-        )
-
-        if self.type != "W":
-            output = torch.roll(
-                output,
-                shifts=(self.window_size // 2, self.window_size // 2),
-                dims=(1, 2),
-            )
-
-        return output
-
-    def relative_embedding(self):
-        cord = torch.tensor(
-            np.array(
-                [
-                    [i, j]
-                    for i in range(self.window_size)
-                    for j in range(self.window_size)
-                ]
-            )
-        )
-        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
-        # negative is allowed
-        return self.relative_position_params[
-            :, relation[:, :, 0].long(), relation[:, :, 1].long()
-        ]
-
-
-class Block(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        output_dim,
-        head_dim,
-        window_size,
-        drop_path,
-        type="W",
-        input_resolution=None,
-    ):
-        """SwinTransformer Block"""
-        super(Block, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        assert type in ["W", "SW"]
-        self.type = type
-        if input_resolution <= window_size:
-            self.type = "W"
-
-        self.ln1 = nn.LayerNorm(input_dim)
-        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.ln2 = nn.LayerNorm(input_dim)
-        self.mlp = nn.Sequential(
-            nn.Linear(input_dim, 4 * input_dim),
-            nn.GELU(),
-            nn.Linear(4 * input_dim, output_dim),
-        )
-
-    def forward(self, x):
-        x = x + self.drop_path(self.msa(self.ln1(x)))
-        x = x + self.drop_path(self.mlp(self.ln2(x)))
-        return x
-
-
-class ConvTransBlock(nn.Module):
-    def __init__(
-        self,
-        conv_dim,
-        trans_dim,
-        head_dim,
-        window_size,
-        drop_path,
-        type="W",
-        input_resolution=None,
-    ):
-        """SwinTransformer and Conv Block"""
-        super(ConvTransBlock, self).__init__()
-        self.conv_dim = conv_dim
-        self.trans_dim = trans_dim
-        self.head_dim = head_dim
-        self.window_size = window_size
-        self.drop_path = drop_path
-        self.type = type
-        self.input_resolution = input_resolution
-
-        assert self.type in ["W", "SW"]
-        if self.input_resolution <= self.window_size:
-            self.type = "W"
-
-        self.trans_block = Block(
-            self.trans_dim,
-            self.trans_dim,
-            self.head_dim,
-            self.window_size,
-            self.drop_path,
-            self.type,
-            self.input_resolution,
-        )
-        self.conv1_1 = nn.Conv2d(
-            self.conv_dim + self.trans_dim,
-            self.conv_dim + self.trans_dim,
-            1,
-            1,
-            0,
-            bias=True,
-        )
-        self.conv1_2 = nn.Conv2d(
-            self.conv_dim + self.trans_dim,
-            self.conv_dim + self.trans_dim,
-            1,
-            1,
-            0,
-            bias=True,
-        )
-
-        self.conv_block = nn.Sequential(
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
-            nn.ReLU(True),
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
-        )
-
-    def forward(self, x):
-        conv_x, trans_x = torch.split(
-            self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1
-        )
-        conv_x = self.conv_block(conv_x) + conv_x
-        trans_x = Rearrange("b c h w -> b h w c")(trans_x)
-        trans_x = self.trans_block(trans_x)
-        trans_x = Rearrange("b h w c -> b c h w")(trans_x)
-        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
-        x = x + res
-
-        return x
-
-
-class SCUNet(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-        in_nc=3,
-        config=[4, 4, 4, 4, 4, 4, 4],
-        dim=64,
-        drop_path_rate=0.0,
-        input_resolution=256,
-    ):
-        super(SCUNet, self).__init__()
-        self.model_arch = "SCUNet"
-        self.sub_type = "SR"
-
-        self.num_filters: int = 0
-
-        self.state = state_dict
-        self.config = config
-        self.dim = dim
-        self.head_dim = 32
-        self.window_size = 8
-
-        self.in_nc = in_nc
-        self.out_nc = self.in_nc
-        self.scale = 1
-        self.supports_fp16 = True
-
-        # drop path rate for each layer
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
-
-        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
-
-        begin = 0
-        self.m_down1 = [
-            ConvTransBlock(
-                dim // 2,
-                dim // 2,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution,
-            )
-            for i in range(config[0])
-        ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[0]
-        self.m_down2 = [
-            ConvTransBlock(
-                dim,
-                dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 2,
-            )
-            for i in range(config[1])
-        ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[1]
-        self.m_down3 = [
-            ConvTransBlock(
-                2 * dim,
-                2 * dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 4,
-            )
-            for i in range(config[2])
-        ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[2]
-        self.m_body = [
-            ConvTransBlock(
-                4 * dim,
-                4 * dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 8,
-            )
-            for i in range(config[3])
-        ]
-
-        begin += config[3]
-        self.m_up3 = [
-            nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False),
-        ] + [
-            ConvTransBlock(
-                2 * dim,
-                2 * dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 4,
-            )
-            for i in range(config[4])
-        ]
-
-        begin += config[4]
-        self.m_up2 = [
-            nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False),
-        ] + [
-            ConvTransBlock(
-                dim,
-                dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 2,
-            )
-            for i in range(config[5])
-        ]
-
-        begin += config[5]
-        self.m_up1 = [
-            nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False),
-        ] + [
-            ConvTransBlock(
-                dim // 2,
-                dim // 2,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution,
-            )
-            for i in range(config[6])
-        ]
-
-        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
-
-        self.m_head = nn.Sequential(*self.m_head)
-        self.m_down1 = nn.Sequential(*self.m_down1)
-        self.m_down2 = nn.Sequential(*self.m_down2)
-        self.m_down3 = nn.Sequential(*self.m_down3)
-        self.m_body = nn.Sequential(*self.m_body)
-        self.m_up3 = nn.Sequential(*self.m_up3)
-        self.m_up2 = nn.Sequential(*self.m_up2)
-        self.m_up1 = nn.Sequential(*self.m_up1)
-        self.m_tail = nn.Sequential(*self.m_tail)
-        # self.apply(self._init_weights)
-        self.load_state_dict(state_dict, strict=True)
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (64 - h % 64) % 64
-        mod_pad_w = (64 - w % 64) % 64
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-        return x
-
-    def forward(self, x0):
-        h, w = x0.size()[-2:]
-        x0 = self.check_image_size(x0)
-
-        x1 = self.m_head(x0)
-        x2 = self.m_down1(x1)
-        x3 = self.m_down2(x2)
-        x4 = self.m_down3(x3)
-        x = self.m_body(x4)
-        x = self.m_up3(x + x4)
-        x = self.m_up2(x + x3)
-        x = self.m_up1(x + x2)
-        x = self.m_tail(x + x1)
-
-        x = x[:, :, :h, :w]
-        return x
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
diff --git a/comfy_extras/chainner_models/architecture/SPSR.py b/comfy_extras/chainner_models/architecture/SPSR.py
deleted file mode 100644
index c3cefff19..000000000
--- a/comfy_extras/chainner_models/architecture/SPSR.py
+++ /dev/null
@@ -1,383 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from . import block as B
-
-
-class Get_gradient_nopadding(nn.Module):
-    def __init__(self):
-        super(Get_gradient_nopadding, self).__init__()
-        kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
-        kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
-        kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
-        kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
-        self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False)  # type: ignore
-
-        self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False)  # type: ignore
-
-    def forward(self, x):
-        x_list = []
-        for i in range(x.shape[1]):
-            x_i = x[:, i]
-            x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1)
-            x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1)
-            x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6)
-            x_list.append(x_i)
-
-        x = torch.cat(x_list, dim=1)
-
-        return x
-
-
-class SPSRNet(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-        norm=None,
-        act: str = "leakyrelu",
-        upsampler: str = "upconv",
-        mode: B.ConvMode = "CNA",
-    ):
-        super(SPSRNet, self).__init__()
-        self.model_arch = "SPSR"
-        self.sub_type = "SR"
-
-        self.state = state_dict
-        self.norm = norm
-        self.act = act
-        self.upsampler = upsampler
-        self.mode = mode
-
-        self.num_blocks = self.get_num_blocks()
-
-        self.in_nc: int = self.state["model.0.weight"].shape[1]
-        self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0]
-
-        self.scale = self.get_scale(4)
-        self.num_filters: int = self.state["model.0.weight"].shape[0]
-
-        self.supports_fp16 = True
-        self.supports_bfp16 = True
-        self.min_size_restriction = None
-
-        n_upscale = int(math.log(self.scale, 2))
-        if self.scale == 3:
-            n_upscale = 1
-
-        fea_conv = B.conv_block(
-            self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None
-        )
-        rb_blocks = [
-            B.RRDB(
-                self.num_filters,
-                kernel_size=3,
-                gc=32,
-                stride=1,
-                bias=True,
-                pad_type="zero",
-                norm_type=norm,
-                act_type=act,
-                mode="CNA",
-            )
-            for _ in range(self.num_blocks)
-        ]
-        LR_conv = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=norm,
-            act_type=None,
-            mode=mode,
-        )
-
-        if upsampler == "upconv":
-            upsample_block = B.upconv_block
-        elif upsampler == "pixelshuffle":
-            upsample_block = B.pixelshuffle_block
-        else:
-            raise NotImplementedError(f"upsample mode [{upsampler}] is not found")
-        if self.scale == 3:
-            a_upsampler = upsample_block(
-                self.num_filters, self.num_filters, 3, act_type=act
-            )
-        else:
-            a_upsampler = [
-                upsample_block(self.num_filters, self.num_filters, act_type=act)
-                for _ in range(n_upscale)
-            ]
-        self.HR_conv0_new = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=act,
-        )
-        self.HR_conv1_new = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-
-        self.model = B.sequential(
-            fea_conv,
-            B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)),
-            *a_upsampler,
-            self.HR_conv0_new,
-        )
-
-        self.get_g_nopadding = Get_gradient_nopadding()
-
-        self.b_fea_conv = B.conv_block(
-            self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None
-        )
-
-        self.b_concat_1 = B.conv_block(
-            2 * self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-        self.b_block_1 = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.b_concat_2 = B.conv_block(
-            2 * self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-        self.b_block_2 = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.b_concat_3 = B.conv_block(
-            2 * self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-        self.b_block_3 = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.b_concat_4 = B.conv_block(
-            2 * self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-        self.b_block_4 = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.b_LR_conv = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=norm,
-            act_type=None,
-            mode=mode,
-        )
-
-        if upsampler == "upconv":
-            upsample_block = B.upconv_block
-        elif upsampler == "pixelshuffle":
-            upsample_block = B.pixelshuffle_block
-        else:
-            raise NotImplementedError(f"upsample mode [{upsampler}] is not found")
-        if self.scale == 3:
-            b_upsampler = upsample_block(
-                self.num_filters, self.num_filters, 3, act_type=act
-            )
-        else:
-            b_upsampler = [
-                upsample_block(self.num_filters, self.num_filters, act_type=act)
-                for _ in range(n_upscale)
-            ]
-
-        b_HR_conv0 = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=act,
-        )
-        b_HR_conv1 = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-
-        self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1)
-
-        self.conv_w = B.conv_block(
-            self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None
-        )
-
-        self.f_concat = B.conv_block(
-            self.num_filters * 2,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-
-        self.f_block = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.f_HR_conv0 = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=act,
-        )
-        self.f_HR_conv1 = B.conv_block(
-            self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None
-        )
-
-        self.load_state_dict(self.state, strict=False)
-
-    def get_scale(self, min_part: int = 4) -> int:
-        n = 0
-        for part in list(self.state):
-            parts = part.split(".")
-            if len(parts) == 3:
-                part_num = int(parts[1])
-                if part_num > min_part and parts[0] == "model" and parts[2] == "weight":
-                    n += 1
-        return 2**n
-
-    def get_num_blocks(self) -> int:
-        nb = 0
-        for part in list(self.state):
-            parts = part.split(".")
-            n_parts = len(parts)
-            if n_parts == 5 and parts[2] == "sub":
-                nb = int(parts[3])
-        return nb
-
-    def forward(self, x):
-        x_grad = self.get_g_nopadding(x)
-        x = self.model[0](x)
-
-        x, block_list = self.model[1](x)
-
-        x_ori = x
-        for i in range(5):
-            x = block_list[i](x)
-        x_fea1 = x
-
-        for i in range(5):
-            x = block_list[i + 5](x)
-        x_fea2 = x
-
-        for i in range(5):
-            x = block_list[i + 10](x)
-        x_fea3 = x
-
-        for i in range(5):
-            x = block_list[i + 15](x)
-        x_fea4 = x
-
-        x = block_list[20:](x)
-        # short cut
-        x = x_ori + x
-        x = self.model[2:](x)
-        x = self.HR_conv1_new(x)
-
-        x_b_fea = self.b_fea_conv(x_grad)
-        x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1)
-
-        x_cat_1 = self.b_block_1(x_cat_1)
-        x_cat_1 = self.b_concat_1(x_cat_1)
-
-        x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)
-
-        x_cat_2 = self.b_block_2(x_cat_2)
-        x_cat_2 = self.b_concat_2(x_cat_2)
-
-        x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)
-
-        x_cat_3 = self.b_block_3(x_cat_3)
-        x_cat_3 = self.b_concat_3(x_cat_3)
-
-        x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1)
-
-        x_cat_4 = self.b_block_4(x_cat_4)
-        x_cat_4 = self.b_concat_4(x_cat_4)
-
-        x_cat_4 = self.b_LR_conv(x_cat_4)
-
-        # short cut
-        x_cat_4 = x_cat_4 + x_b_fea
-        x_branch = self.b_module(x_cat_4)
-
-        # x_out_branch = self.conv_w(x_branch)
-        ########
-        x_branch_d = x_branch
-        x_f_cat = torch.cat([x_branch_d, x], dim=1)
-        x_f_cat = self.f_block(x_f_cat)
-        x_out = self.f_concat(x_f_cat)
-        x_out = self.f_HR_conv0(x_out)
-        x_out = self.f_HR_conv1(x_out)
-
-        #########
-        # return x_out_branch, x_out, x_grad
-        return x_out
diff --git a/comfy_extras/chainner_models/architecture/SRVGG.py b/comfy_extras/chainner_models/architecture/SRVGG.py
deleted file mode 100644
index 7a8ec37ae..000000000
--- a/comfy_extras/chainner_models/architecture/SRVGG.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import math
-
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class SRVGGNetCompact(nn.Module):
-    """A compact VGG-style network structure for super-resolution.
-    It is a compact network structure, which performs upsampling in the last layer and no convolution is
-    conducted on the HR feature space.
-    Args:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_out_ch (int): Channel number of outputs. Default: 3.
-        num_feat (int): Channel number of intermediate features. Default: 64.
-        num_conv (int): Number of convolution layers in the body network. Default: 16.
-        upscale (int): Upsampling factor. Default: 4.
-        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        act_type: str = "prelu",
-    ):
-        super(SRVGGNetCompact, self).__init__()
-        self.model_arch = "SRVGG (RealESRGAN)"
-        self.sub_type = "SR"
-
-        self.act_type = act_type
-
-        self.state = state_dict
-
-        if "params" in self.state:
-            self.state = self.state["params"]
-
-        self.key_arr = list(self.state.keys())
-
-        self.in_nc = self.get_in_nc()
-        self.num_feat = self.get_num_feats()
-        self.num_conv = self.get_num_conv()
-        self.out_nc = self.in_nc  # :(
-        self.pixelshuffle_shape = None  # Defined in get_scale()
-        self.scale = self.get_scale()
-
-        self.supports_fp16 = True
-        self.supports_bfp16 = True
-        self.min_size_restriction = None
-
-        self.body = nn.ModuleList()
-        # the first conv
-        self.body.append(nn.Conv2d(self.in_nc, self.num_feat, 3, 1, 1))
-        # the first activation
-        if act_type == "relu":
-            activation = nn.ReLU(inplace=True)
-        elif act_type == "prelu":
-            activation = nn.PReLU(num_parameters=self.num_feat)
-        elif act_type == "leakyrelu":
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.body.append(activation)  # type: ignore
-
-        # the body structure
-        for _ in range(self.num_conv):
-            self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1))
-            # activation
-            if act_type == "relu":
-                activation = nn.ReLU(inplace=True)
-            elif act_type == "prelu":
-                activation = nn.PReLU(num_parameters=self.num_feat)
-            elif act_type == "leakyrelu":
-                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-            self.body.append(activation)  # type: ignore
-
-        # the last conv
-        self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1))  # type: ignore
-        # upsample
-        self.upsampler = nn.PixelShuffle(self.scale)
-
-        self.load_state_dict(self.state, strict=False)
-
-    def get_num_conv(self) -> int:
-        return (int(self.key_arr[-1].split(".")[1]) - 2) // 2
-
-    def get_num_feats(self) -> int:
-        return self.state[self.key_arr[0]].shape[0]
-
-    def get_in_nc(self) -> int:
-        return self.state[self.key_arr[0]].shape[1]
-
-    def get_scale(self) -> int:
-        self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0]
-        # Assume out_nc is the same as in_nc
-        # I cant think of a better way to do that
-        self.out_nc = self.in_nc
-        scale = math.sqrt(self.pixelshuffle_shape / self.out_nc)
-        if scale - int(scale) > 0:
-            print(
-                "out_nc is probably different than in_nc, scale calculation might be wrong"
-            )
-        scale = int(scale)
-        return scale
-
-    def forward(self, x):
-        out = x
-        for i in range(0, len(self.body)):
-            out = self.body[i](out)
-
-        out = self.upsampler(out)
-        # add the nearest upsampled image, so that the network learns the residual
-        base = F.interpolate(x, scale_factor=self.scale, mode="nearest")
-        out += base
-        return out
diff --git a/comfy_extras/chainner_models/architecture/SwiftSRGAN.py b/comfy_extras/chainner_models/architecture/SwiftSRGAN.py
deleted file mode 100644
index dbb7725b0..000000000
--- a/comfy_extras/chainner_models/architecture/SwiftSRGAN.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# From https://github.com/Koushik0901/Swift-SRGAN/blob/master/swift-srgan/models.py
-
-import torch
-from torch import nn
-
-
-class SeperableConv2d(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, stride=1, padding=1, bias=True
-    ):
-        super(SeperableConv2d, self).__init__()
-        self.depthwise = nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            groups=in_channels,
-            bias=bias,
-            padding=padding,
-        )
-        self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        return self.pointwise(self.depthwise(x))
-
-
-class ConvBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        use_act=True,
-        use_bn=True,
-        discriminator=False,
-        **kwargs,
-    ):
-        super(ConvBlock, self).__init__()
-
-        self.use_act = use_act
-        self.cnn = SeperableConv2d(in_channels, out_channels, **kwargs, bias=not use_bn)
-        self.bn = nn.BatchNorm2d(out_channels) if use_bn else nn.Identity()
-        self.act = (
-            nn.LeakyReLU(0.2, inplace=True)
-            if discriminator
-            else nn.PReLU(num_parameters=out_channels)
-        )
-
-    def forward(self, x):
-        return self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x))
-
-
-class UpsampleBlock(nn.Module):
-    def __init__(self, in_channels, scale_factor):
-        super(UpsampleBlock, self).__init__()
-
-        self.conv = SeperableConv2d(
-            in_channels,
-            in_channels * scale_factor**2,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-        )
-        self.ps = nn.PixelShuffle(
-            scale_factor
-        )  # (in_channels * 4, H, W) -> (in_channels, H*2, W*2)
-        self.act = nn.PReLU(num_parameters=in_channels)
-
-    def forward(self, x):
-        return self.act(self.ps(self.conv(x)))
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, in_channels):
-        super(ResidualBlock, self).__init__()
-
-        self.block1 = ConvBlock(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1
-        )
-        self.block2 = ConvBlock(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1, use_act=False
-        )
-
-    def forward(self, x):
-        out = self.block1(x)
-        out = self.block2(out)
-        return out + x
-
-
-class Generator(nn.Module):
-    """Swift-SRGAN Generator
-    Args:
-        in_channels (int): number of input image channels.
-        num_channels (int): number of hidden channels.
-        num_blocks (int): number of residual blocks.
-        upscale_factor (int): factor to upscale the image [2x, 4x, 8x].
-    Returns:
-        torch.Tensor: super resolution image
-    """
-
-    def __init__(
-        self,
-        state_dict,
-    ):
-        super(Generator, self).__init__()
-        self.model_arch = "Swift-SRGAN"
-        self.sub_type = "SR"
-        self.state = state_dict
-        if "model" in self.state:
-            self.state = self.state["model"]
-
-        self.in_nc: int = self.state["initial.cnn.depthwise.weight"].shape[0]
-        self.out_nc: int = self.state["final_conv.pointwise.weight"].shape[0]
-        self.num_filters: int = self.state["initial.cnn.pointwise.weight"].shape[0]
-        self.num_blocks = len(
-            set([x.split(".")[1] for x in self.state.keys() if "residual" in x])
-        )
-        self.scale: int = 2 ** len(
-            set([x.split(".")[1] for x in self.state.keys() if "upsampler" in x])
-        )
-
-        in_channels = self.in_nc
-        num_channels = self.num_filters
-        num_blocks = self.num_blocks
-        upscale_factor = self.scale
-
-        self.supports_fp16 = True
-        self.supports_bfp16 = True
-        self.min_size_restriction = None
-
-        self.initial = ConvBlock(
-            in_channels, num_channels, kernel_size=9, stride=1, padding=4, use_bn=False
-        )
-        self.residual = nn.Sequential(
-            *[ResidualBlock(num_channels) for _ in range(num_blocks)]
-        )
-        self.convblock = ConvBlock(
-            num_channels,
-            num_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            use_act=False,
-        )
-        self.upsampler = nn.Sequential(
-            *[
-                UpsampleBlock(num_channels, scale_factor=2)
-                for _ in range(upscale_factor // 2)
-            ]
-        )
-        self.final_conv = SeperableConv2d(
-            num_channels, in_channels, kernel_size=9, stride=1, padding=4
-        )
-
-        self.load_state_dict(self.state, strict=False)
-
-    def forward(self, x):
-        initial = self.initial(x)
-        x = self.residual(initial)
-        x = self.convblock(x) + initial
-        x = self.upsampler(x)
-        return (torch.tanh(self.final_conv(x)) + 1) / 2
diff --git a/comfy_extras/chainner_models/architecture/Swin2SR.py b/comfy_extras/chainner_models/architecture/Swin2SR.py
deleted file mode 100644
index cb57ecfc4..000000000
--- a/comfy_extras/chainner_models/architecture/Swin2SR.py
+++ /dev/null
@@ -1,1377 +0,0 @@
-# pylint: skip-file
-# -----------------------------------------------------------------------------------
-# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345
-# Written by Conde and Choi et al.
-# From: https://raw.githubusercontent.com/mv-lab/swin2sr/main/models/network_swin2sr.py
-# -----------------------------------------------------------------------------------
-
-import math
-import re
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-
-# Originally from the timm package
-from .timm.drop import DropPath
-from .timm.helpers import to_2tuple
-from .timm.weight_init import trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = (
-        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    )
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(
-        B, H // window_size, W // window_size, window_size, window_size, -1
-    )
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        attn_drop=0.0,
-        proj_drop=0.0,
-        pretrained_window_size=[0, 0],
-    ):
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.pretrained_window_size = pretrained_window_size
-        self.num_heads = num_heads
-
-        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)  # type: ignore
-
-        # mlp to generate continuous relative position bias
-        self.cpb_mlp = nn.Sequential(
-            nn.Linear(2, 512, bias=True),
-            nn.ReLU(inplace=True),
-            nn.Linear(512, num_heads, bias=False),
-        )
-
-        # get relative_coords_table
-        relative_coords_h = torch.arange(
-            -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32
-        )
-        relative_coords_w = torch.arange(
-            -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32
-        )
-        relative_coords_table = (
-            torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))
-            .permute(1, 2, 0)
-            .contiguous()
-            .unsqueeze(0)
-        )  # 1, 2*Wh-1, 2*Ww-1, 2
-        if pretrained_window_size[0] > 0:
-            relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1
-            relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1
-        else:
-            relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1
-            relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1
-        relative_coords_table *= 8  # normalize to -8, 8
-        relative_coords_table = (
-            torch.sign(relative_coords_table)
-            * torch.log2(torch.abs(relative_coords_table) + 1.0)
-            / np.log2(8)
-        )
-
-        self.register_buffer("relative_coords_table", relative_coords_table)
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = (
-            coords_flatten[:, :, None] - coords_flatten[:, None, :]
-        )  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=False)
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(dim))  # type: ignore
-            self.v_bias = nn.Parameter(torch.zeros(dim))  # type: ignore
-        else:
-            self.q_bias = None
-            self.v_bias = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))  # type: ignore
-        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        # cosine attention
-        attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)
-        logit_scale = torch.clamp(
-            self.logit_scale,
-            max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device),
-        ).exp()
-        attn = attn * logit_scale
-
-        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(
-            -1, self.num_heads
-        )
-        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(  # type: ignore
-            self.window_size[0] * self.window_size[1],
-            self.window_size[0] * self.window_size[1],
-            -1,
-        )  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, Wh*Ww, Wh*Ww
-        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
-                1
-            ).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return (
-            f"dim={self.dim}, window_size={self.window_size}, "
-            f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}"
-        )
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r"""Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-        pretrained_window_size (int): Window size in pre-training.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        pretrained_window_size=0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert (
-            0 <= self.shift_size < self.window_size
-        ), "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-            pretrained_window_size=to_2tuple(pretrained_window_size),
-        )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        w_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(
-            img_mask, self.window_size
-        )  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
-            attn_mask == 0, float(0.0)
-        )
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(
-                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
-            )
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, C
-        )  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(
-                x_windows, mask=self.attn_mask
-            )  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(
-                x_windows, mask=self.calculate_mask(x_size).to(x.device)
-            )
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(
-                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
-            )
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-        x = shortcut + self.drop_path(self.norm1(x))
-
-        # FFN
-        x = x + self.drop_path(self.norm2(self.mlp(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return (
-            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
-            f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-        )
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r"""Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(2 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.reduction(x)
-        x = self.norm(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        flops += H * W * self.dim // 2
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        pretrained_window_size (int): Local window size in pre-training.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        pretrained_window_size=0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                SwinTransformerBlock(
-                    dim=dim,
-                    input_resolution=input_resolution,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i]
-                    if isinstance(drop_path, list)
-                    else drop_path,
-                    norm_layer=norm_layer,
-                    pretrained_window_size=pretrained_window_size,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(
-                input_resolution, dim=dim, norm_layer=norm_layer
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()  # type: ignore
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-    def _init_respostnorm(self):
-        for blk in self.blocks:
-            nn.init.constant_(blk.norm1.bias, 0)  # type: ignore
-            nn.init.constant_(blk.norm1.weight, 0)  # type: ignore
-            nn.init.constant_(blk.norm2.bias, 0)  # type: ignore
-            nn.init.constant_(blk.norm2.weight, 0)  # type: ignore
-
-
-class PatchEmbed(nn.Module):
-    r"""Image to Patch Embedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]  # type: ignore
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(
-            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size  # type: ignore
-        )
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1],
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])  # type: ignore
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        img_size=224,
-        patch_size=4,
-        resi_connection="1conv",
-    ):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(
-            dim=dim,
-            input_resolution=input_resolution,
-            depth=depth,
-            num_heads=num_heads,
-            window_size=window_size,
-            mlp_ratio=mlp_ratio,
-            qkv_bias=qkv_bias,
-            drop=drop,
-            attn_drop=attn_drop,
-            drop_path=drop_path,
-            norm_layer=norm_layer,
-            downsample=downsample,
-            use_checkpoint=use_checkpoint,
-        )
-
-        if resi_connection == "1conv":
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv = nn.Sequential(
-                nn.Conv2d(dim, dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim, 3, 1, 1),
-            )
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=dim,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=dim,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-    def forward(self, x, x_size):
-        return (
-            self.patch_embed(
-                self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))
-            )
-            + x
-        )
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r"""Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]  # type: ignore
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample, self).__init__(*m)
-
-
-class Upsample_hf(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample_hf, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution  # type: ignore
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class Swin2SR(nn.Module):
-    r"""Swin2SR
-        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        **kwargs,
-    ):
-        super(Swin2SR, self).__init__()
-
-        # Defaults
-        img_size = 128
-        patch_size = 1
-        in_chans = 3
-        embed_dim = 96
-        depths = [6, 6, 6, 6]
-        num_heads = [6, 6, 6, 6]
-        window_size = 7
-        mlp_ratio = 4.0
-        qkv_bias = True
-        drop_rate = 0.0
-        attn_drop_rate = 0.0
-        drop_path_rate = 0.1
-        norm_layer = nn.LayerNorm
-        ape = False
-        patch_norm = True
-        use_checkpoint = False
-        upscale = 2
-        img_range = 1.0
-        upsampler = ""
-        resi_connection = "1conv"
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-
-        self.model_arch = "Swin2SR"
-        self.sub_type = "SR"
-        self.state = state_dict
-        if "params_ema" in self.state:
-            self.state = self.state["params_ema"]
-        elif "params" in self.state:
-            self.state = self.state["params"]
-
-        state_keys = self.state.keys()
-
-        if "conv_before_upsample.0.weight" in state_keys:
-            if "conv_aux.weight" in state_keys:
-                upsampler = "pixelshuffle_aux"
-            elif "conv_up1.weight" in state_keys:
-                upsampler = "nearest+conv"
-            else:
-                upsampler = "pixelshuffle"
-                supports_fp16 = False
-        elif "upsample.0.weight" in state_keys:
-            upsampler = "pixelshuffledirect"
-        else:
-            upsampler = ""
-
-        num_feat = (
-            self.state.get("conv_before_upsample.0.weight", None).shape[1]
-            if self.state.get("conv_before_upsample.weight", None)
-            else 64
-        )
-
-        num_in_ch = self.state["conv_first.weight"].shape[1]
-        in_chans = num_in_ch
-        if "conv_last.weight" in state_keys:
-            num_out_ch = self.state["conv_last.weight"].shape[0]
-        else:
-            num_out_ch = num_in_ch
-
-        upscale = 1
-        if upsampler == "nearest+conv":
-            upsample_keys = [
-                x for x in state_keys if "conv_up" in x and "bias" not in x
-            ]
-
-            for upsample_key in upsample_keys:
-                upscale *= 2
-        elif upsampler == "pixelshuffle" or upsampler == "pixelshuffle_aux":
-            upsample_keys = [
-                x
-                for x in state_keys
-                if "upsample" in x and "conv" not in x and "bias" not in x
-            ]
-            for upsample_key in upsample_keys:
-                shape = self.state[upsample_key].shape[0]
-                upscale *= math.sqrt(shape // num_feat)
-            upscale = int(upscale)
-        elif upsampler == "pixelshuffledirect":
-            upscale = int(
-                math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch)
-            )
-
-        max_layer_num = 0
-        max_block_num = 0
-        for key in state_keys:
-            result = re.match(
-                r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key
-            )
-            if result:
-                layer_num, block_num = result.groups()
-                max_layer_num = max(max_layer_num, int(layer_num))
-                max_block_num = max(max_block_num, int(block_num))
-
-        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]
-
-        if (
-            "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            in state_keys
-        ):
-            num_heads_num = self.state[
-                "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            ].shape[-1]
-            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
-        else:
-            num_heads = depths
-
-        embed_dim = self.state["conv_first.weight"].shape[0]
-
-        mlp_ratio = float(
-            self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0]
-            / embed_dim
-        )
-
-        # TODO: could actually count the layers, but this should do
-        if "layers.0.conv.4.weight" in state_keys:
-            resi_connection = "3conv"
-        else:
-            resi_connection = "1conv"
-
-        window_size = int(
-            math.sqrt(
-                self.state[
-                    "layers.0.residual_group.blocks.0.attn.relative_position_index"
-                ].shape[0]
-            )
-        )
-
-        if "layers.0.residual_group.blocks.1.attn_mask" in state_keys:
-            img_size = int(
-                math.sqrt(
-                    self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
-                )
-                * window_size
-            )
-
-        # The JPEG models are the only ones with window-size 7, and they also use this range
-        img_range = 255.0 if window_size == 7 else 1.0
-
-        self.in_nc = num_in_ch
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.depths = depths
-        self.window_size = window_size
-        self.mlp_ratio = mlp_ratio
-        self.scale = upscale
-        self.upsampler = upsampler
-        self.img_size = img_size
-        self.img_range = img_range
-        self.resi_connection = resi_connection
-
-        self.supports_fp16 = False  # Too much weirdness to support this at the moment
-        self.supports_bfp16 = True
-        self.min_size_restriction = 16
-
-        ## END AUTO DETECTION
-
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))  # type: ignore
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
-        ]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(
-                dim=embed_dim,
-                input_resolution=(patches_resolution[0], patches_resolution[1]),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=qkv_bias,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],  # type: ignore    # no impact on SR results
-                norm_layer=norm_layer,
-                downsample=None,
-                use_checkpoint=use_checkpoint,
-                img_size=img_size,
-                patch_size=patch_size,
-                resi_connection=resi_connection,
-            )
-            self.layers.append(layer)
-
-        if self.upsampler == "pixelshuffle_hf":
-            self.layers_hf = nn.ModuleList()
-            for i_layer in range(self.num_layers):
-                layer = RSTB(
-                    dim=embed_dim,
-                    input_resolution=(patches_resolution[0], patches_resolution[1]),
-                    depth=depths[i_layer],
-                    num_heads=num_heads[i_layer],
-                    window_size=window_size,
-                    mlp_ratio=self.mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    drop=drop_rate,
-                    attn_drop=attn_drop_rate,
-                    drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],  # type: ignore    # no impact on SR results # type: ignore
-                    norm_layer=norm_layer,
-                    downsample=None,
-                    use_checkpoint=use_checkpoint,
-                    img_size=img_size,
-                    patch_size=patch_size,
-                    resi_connection=resi_connection,
-                )
-                self.layers_hf.append(layer)
-
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == "1conv":
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(
-                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),
-            )
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == "pixelshuffle_aux":
-            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_after_aux = nn.Sequential(
-                nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        elif self.upsampler == "pixelshuffle_hf":
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.upsample_hf = Upsample_hf(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_first_hf = nn.Sequential(
-                nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-            self.conv_before_upsample_hf = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(
-                upscale,
-                embed_dim,
-                num_out_ch,
-                (patches_resolution[0], patches_resolution[1]),
-            )
-        elif self.upsampler == "nearest+conv":
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, "only support x4 now."
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-        self.load_state_dict(state_dict)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay(self):
-        return {"absolute_pos_embed"}
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay_keywords(self):
-        return {"relative_position_bias_table"}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward_features_hf(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers_hf:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == "pixelshuffle_aux":
-            bicubic = F.interpolate(
-                x,
-                size=(H * self.upscale, W * self.upscale),
-                mode="bicubic",
-                align_corners=False,
-            )
-            bicubic = self.conv_bicubic(bicubic)
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            aux = self.conv_aux(x)  # b, 3, LR_H, LR_W
-            x = self.conv_after_aux(aux)
-            x = (
-                self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale]
-                + bicubic[:, :, : H * self.upscale, : W * self.upscale]
-            )
-            x = self.conv_last(x)
-            aux = aux / self.img_range + self.mean
-        elif self.upsampler == "pixelshuffle_hf":
-            # for classical SR with HF
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x_before = self.conv_before_upsample(x)
-            x_out = self.conv_last(self.upsample(x_before))
-
-            x_hf = self.conv_first_hf(x_before)
-            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
-            x_hf = self.conv_before_upsample_hf(x_hf)
-            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
-            x = x_out + x_hf
-            x_hf = x_hf / self.img_range + self.mean
-
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == "nearest+conv":
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(
-                self.conv_up1(
-                    torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest")
-                )
-            )
-            x = self.lrelu(
-                self.conv_up2(
-                    torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest")
-                )
-            )
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-        if self.upsampler == "pixelshuffle_aux":
-            # NOTE: I removed an "aux" output here. not sure what that was for
-            return x[:, :, : H * self.upscale, : W * self.upscale]  # type: ignore
-
-        elif self.upsampler == "pixelshuffle_hf":
-            x_out = x_out / self.img_range + self.mean  # type: ignore
-            return x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale]  # type: ignore
-
-        else:
-            return x[:, :, : H * self.upscale, : W * self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()  # type: ignore
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()  # type: ignore
-        return flops
diff --git a/comfy_extras/chainner_models/architecture/SwinIR.py b/comfy_extras/chainner_models/architecture/SwinIR.py
deleted file mode 100644
index 439dcbcb2..000000000
--- a/comfy_extras/chainner_models/architecture/SwinIR.py
+++ /dev/null
@@ -1,1224 +0,0 @@
-# pylint: skip-file
-# -----------------------------------------------------------------------------------
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-# -----------------------------------------------------------------------------------
-
-import math
-import re
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-
-# Originally from the timm package
-from .timm.drop import DropPath
-from .timm.helpers import to_2tuple
-from .timm.weight_init import trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = (
-        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    )
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(
-        B, H // window_size, W // window_size, window_size, window_size, -1
-    )
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(  # type: ignore
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = (
-            coords_flatten[:, :, None] - coords_flatten[:, None, :]
-        )  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
-            .permute(2, 0, 3, 1, 4)
-        )
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[
-            self.relative_position_index.view(-1)  # type: ignore
-        ].view(
-            self.window_size[0] * self.window_size[1],
-            self.window_size[0] * self.window_size[1],
-            -1,
-        )  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
-                1
-            ).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}"
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r"""Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert (
-            0 <= self.shift_size < self.window_size
-        ), "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        w_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(
-            img_mask, self.window_size
-        )  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
-            attn_mask == 0, float(0.0)
-        )
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(
-                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
-            )
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, C
-        )  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(
-                x_windows, mask=self.attn_mask
-            )  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(
-                x_windows, mask=self.calculate_mask(x_size).to(x.device)
-            )
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(
-                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
-            )
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return (
-            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
-            f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-        )
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r"""Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                SwinTransformerBlock(
-                    dim=dim,
-                    input_resolution=input_resolution,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i]
-                    if isinstance(drop_path, list)
-                    else drop_path,
-                    norm_layer=norm_layer,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(
-                input_resolution, dim=dim, norm_layer=norm_layer
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()  # type: ignore
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        img_size=224,
-        patch_size=4,
-        resi_connection="1conv",
-    ):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(
-            dim=dim,
-            input_resolution=input_resolution,
-            depth=depth,
-            num_heads=num_heads,
-            window_size=window_size,
-            mlp_ratio=mlp_ratio,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            drop=drop,
-            attn_drop=attn_drop,
-            drop_path=drop_path,
-            norm_layer=norm_layer,
-            downsample=downsample,
-            use_checkpoint=use_checkpoint,
-        )
-
-        if resi_connection == "1conv":
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv = nn.Sequential(
-                nn.Conv2d(dim, dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim, 3, 1, 1),
-            )
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=0,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=0,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-    def forward(self, x, x_size):
-        return (
-            self.patch_embed(
-                self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))
-            )
-            + x
-        )
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r"""Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [
-            img_size[0] // patch_size[0],  # type: ignore
-            img_size[1] // patch_size[1],  # type: ignore
-        ]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        H, W = self.img_size
-        if self.norm is not None:
-            flops += H * W * self.embed_dim  # type: ignore
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r"""Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [
-            img_size[0] // patch_size[0],  # type: ignore
-            img_size[1] // patch_size[1],  # type: ignore
-        ]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution  # type: ignore
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class SwinIR(nn.Module):
-    r"""SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        **kwargs,
-    ):
-        super(SwinIR, self).__init__()
-
-        # Defaults
-        img_size = 64
-        patch_size = 1
-        in_chans = 3
-        embed_dim = 96
-        depths = [6, 6, 6, 6]
-        num_heads = [6, 6, 6, 6]
-        window_size = 7
-        mlp_ratio = 4.0
-        qkv_bias = True
-        qk_scale = None
-        drop_rate = 0.0
-        attn_drop_rate = 0.0
-        drop_path_rate = 0.1
-        norm_layer = nn.LayerNorm
-        ape = False
-        patch_norm = True
-        use_checkpoint = False
-        upscale = 2
-        img_range = 1.0
-        upsampler = ""
-        resi_connection = "1conv"
-        num_feat = 64
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        supports_fp16 = True
-        self.start_unshuffle = 1
-
-        self.model_arch = "SwinIR"
-        self.sub_type = "SR"
-        self.state = state_dict
-        if "params_ema" in self.state:
-            self.state = self.state["params_ema"]
-        elif "params" in self.state:
-            self.state = self.state["params"]
-
-        state_keys = self.state.keys()
-
-        if "conv_before_upsample.0.weight" in state_keys:
-            if "conv_up1.weight" in state_keys:
-                upsampler = "nearest+conv"
-            else:
-                upsampler = "pixelshuffle"
-                supports_fp16 = False
-        elif "upsample.0.weight" in state_keys:
-            upsampler = "pixelshuffledirect"
-        else:
-            upsampler = ""
-
-        num_feat = (
-            self.state.get("conv_before_upsample.0.weight", None).shape[1]
-            if self.state.get("conv_before_upsample.weight", None)
-            else 64
-        )
-
-        if "conv_first.1.weight" in self.state:
-            self.state["conv_first.weight"] = self.state.pop("conv_first.1.weight")
-            self.state["conv_first.bias"] = self.state.pop("conv_first.1.bias")
-            self.start_unshuffle = round(math.sqrt(self.state["conv_first.weight"].shape[1] // 3))
-
-        num_in_ch = self.state["conv_first.weight"].shape[1]
-        in_chans = num_in_ch
-        if "conv_last.weight" in state_keys:
-            num_out_ch = self.state["conv_last.weight"].shape[0]
-        else:
-            num_out_ch = num_in_ch
-
-        upscale = 1
-        if upsampler == "nearest+conv":
-            upsample_keys = [
-                x for x in state_keys if "conv_up" in x and "bias" not in x
-            ]
-
-            for upsample_key in upsample_keys:
-                upscale *= 2
-        elif upsampler == "pixelshuffle":
-            upsample_keys = [
-                x
-                for x in state_keys
-                if "upsample" in x and "conv" not in x and "bias" not in x
-            ]
-            for upsample_key in upsample_keys:
-                shape = self.state[upsample_key].shape[0]
-                upscale *= math.sqrt(shape // num_feat)
-            upscale = int(upscale)
-        elif upsampler == "pixelshuffledirect":
-            upscale = int(
-                math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch)
-            )
-
-        max_layer_num = 0
-        max_block_num = 0
-        for key in state_keys:
-            result = re.match(
-                r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key
-            )
-            if result:
-                layer_num, block_num = result.groups()
-                max_layer_num = max(max_layer_num, int(layer_num))
-                max_block_num = max(max_block_num, int(block_num))
-
-        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]
-
-        if (
-            "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            in state_keys
-        ):
-            num_heads_num = self.state[
-                "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            ].shape[-1]
-            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
-        else:
-            num_heads = depths
-
-        embed_dim = self.state["conv_first.weight"].shape[0]
-
-        mlp_ratio = float(
-            self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0]
-            / embed_dim
-        )
-
-        # TODO: could actually count the layers, but this should do
-        if "layers.0.conv.4.weight" in state_keys:
-            resi_connection = "3conv"
-        else:
-            resi_connection = "1conv"
-
-        window_size = int(
-            math.sqrt(
-                self.state[
-                    "layers.0.residual_group.blocks.0.attn.relative_position_index"
-                ].shape[0]
-            )
-        )
-
-        if "layers.0.residual_group.blocks.1.attn_mask" in state_keys:
-            img_size = int(
-                math.sqrt(
-                    self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
-                )
-                * window_size
-            )
-
-        # The JPEG models are the only ones with window-size 7, and they also use this range
-        img_range = 255.0 if window_size == 7 else 1.0
-
-        self.in_nc = num_in_ch
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.depths = depths
-        self.window_size = window_size
-        self.mlp_ratio = mlp_ratio
-        self.scale = upscale / self.start_unshuffle
-        self.upsampler = upsampler
-        self.img_size = img_size
-        self.img_range = img_range
-        self.resi_connection = resi_connection
-
-        self.supports_fp16 = False  # Too much weirdness to support this at the moment
-        self.supports_bfp16 = True
-        self.min_size_restriction = 16
-
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(  # type: ignore
-                torch.zeros(1, num_patches, embed_dim)
-            )
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
-        ]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(
-                dim=embed_dim,
-                input_resolution=(patches_resolution[0], patches_resolution[1]),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[
-                    sum(depths[:i_layer]) : sum(depths[: i_layer + 1])  # type: ignore
-                ],  # no impact on SR results
-                norm_layer=norm_layer,
-                downsample=None,
-                use_checkpoint=use_checkpoint,
-                img_size=img_size,
-                patch_size=patch_size,
-                resi_connection=resi_connection,
-            )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == "1conv":
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(
-                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),
-            )
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(
-                upscale,
-                embed_dim,
-                num_out_ch,
-                (patches_resolution[0], patches_resolution[1]),
-            )
-        elif self.upsampler == "nearest+conv":
-            # for real-world SR (less artifacts)
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            if self.upscale == 4:
-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            elif self.upscale == 8:
-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-                self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-        self.load_state_dict(self.state, strict=False)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay(self):
-        return {"absolute_pos_embed"}
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay_keywords(self):
-        return {"relative_position_bias_table"}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.start_unshuffle > 1:
-            x = torch.nn.functional.pixel_unshuffle(x, self.start_unshuffle)
-
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == "nearest+conv":
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(
-                self.conv_up1(
-                    torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest")  # type: ignore
-                )
-            )
-            if self.upscale == 4:
-                x = self.lrelu(
-                    self.conv_up2(
-                        torch.nn.functional.interpolate(  # type: ignore
-                            x, scale_factor=2, mode="nearest"
-                        )
-                    )
-                )
-            elif self.upscale == 8:
-                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-                x = self.lrelu(self.conv_up3(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, : H * self.upscale, : W * self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()  # type: ignore
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()  # type: ignore
-        return flops
diff --git a/comfy_extras/chainner_models/architecture/__init__.py b/comfy_extras/chainner_models/architecture/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/comfy_extras/chainner_models/architecture/block.py b/comfy_extras/chainner_models/architecture/block.py
deleted file mode 100644
index d7bc5d227..000000000
--- a/comfy_extras/chainner_models/architecture/block.py
+++ /dev/null
@@ -1,546 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-from __future__ import annotations
-
-from collections import OrderedDict
-try:
-    from typing import Literal
-except ImportError:
-    from typing_extensions import Literal
-
-import torch
-import torch.nn as nn
-
-####################
-# Basic blocks
-####################
-
-
-def act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1):
-    # helper selecting activation
-    # neg_slope: for leakyrelu and init of prelu
-    # n_prelu: for p_relu num_parameters
-    act_type = act_type.lower()
-    if act_type == "relu":
-        layer = nn.ReLU(inplace)
-    elif act_type == "leakyrelu":
-        layer = nn.LeakyReLU(neg_slope, inplace)
-    elif act_type == "prelu":
-        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
-    else:
-        raise NotImplementedError(
-            "activation layer [{:s}] is not found".format(act_type)
-        )
-    return layer
-
-
-def norm(norm_type: str, nc: int):
-    # helper selecting normalization layer
-    norm_type = norm_type.lower()
-    if norm_type == "batch":
-        layer = nn.BatchNorm2d(nc, affine=True)
-    elif norm_type == "instance":
-        layer = nn.InstanceNorm2d(nc, affine=False)
-    else:
-        raise NotImplementedError(
-            "normalization layer [{:s}] is not found".format(norm_type)
-        )
-    return layer
-
-
-def pad(pad_type: str, padding):
-    # helper selecting padding layer
-    # if padding is 'zero', do by conv layers
-    pad_type = pad_type.lower()
-    if padding == 0:
-        return None
-    if pad_type == "reflect":
-        layer = nn.ReflectionPad2d(padding)
-    elif pad_type == "replicate":
-        layer = nn.ReplicationPad2d(padding)
-    else:
-        raise NotImplementedError(
-            "padding layer [{:s}] is not implemented".format(pad_type)
-        )
-    return layer
-
-
-def get_valid_padding(kernel_size, dilation):
-    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
-    padding = (kernel_size - 1) // 2
-    return padding
-
-
-class ConcatBlock(nn.Module):
-    # Concat the output of a submodule to its input
-    def __init__(self, submodule):
-        super(ConcatBlock, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        output = torch.cat((x, self.sub(x)), dim=1)
-        return output
-
-    def __repr__(self):
-        tmpstr = "Identity .. \n|"
-        modstr = self.sub.__repr__().replace("\n", "\n|")
-        tmpstr = tmpstr + modstr
-        return tmpstr
-
-
-class ShortcutBlock(nn.Module):
-    # Elementwise sum the output of a submodule to its input
-    def __init__(self, submodule):
-        super(ShortcutBlock, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        output = x + self.sub(x)
-        return output
-
-    def __repr__(self):
-        tmpstr = "Identity + \n|"
-        modstr = self.sub.__repr__().replace("\n", "\n|")
-        tmpstr = tmpstr + modstr
-        return tmpstr
-
-
-class ShortcutBlockSPSR(nn.Module):
-    # Elementwise sum the output of a submodule to its input
-    def __init__(self, submodule):
-        super(ShortcutBlockSPSR, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        return x, self.sub
-
-    def __repr__(self):
-        tmpstr = "Identity + \n|"
-        modstr = self.sub.__repr__().replace("\n", "\n|")
-        tmpstr = tmpstr + modstr
-        return tmpstr
-
-
-def sequential(*args):
-    # Flatten Sequential. It unwraps nn.Sequential.
-    if len(args) == 1:
-        if isinstance(args[0], OrderedDict):
-            raise NotImplementedError("sequential does not support OrderedDict input.")
-        return args[0]  # No sequential is needed.
-    modules = []
-    for module in args:
-        if isinstance(module, nn.Sequential):
-            for submodule in module.children():
-                modules.append(submodule)
-        elif isinstance(module, nn.Module):
-            modules.append(module)
-    return nn.Sequential(*modules)
-
-
-ConvMode = Literal["CNA", "NAC", "CNAC"]
-
-
-# 2x2x2 Conv Block
-def conv_block_2c2(
-    in_nc,
-    out_nc,
-    act_type="relu",
-):
-    return sequential(
-        nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1),
-        nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0),
-        act(act_type) if act_type else None,
-    )
-
-
-def conv_block(
-    in_nc: int,
-    out_nc: int,
-    kernel_size,
-    stride=1,
-    dilation=1,
-    groups=1,
-    bias=True,
-    pad_type="zero",
-    norm_type: str | None = None,
-    act_type: str | None = "relu",
-    mode: ConvMode = "CNA",
-    c2x2=False,
-):
-    """
-    Conv layer with padding, normalization, activation
-    mode: CNA --> Conv -> Norm -> Act
-        NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
-    """
-
-    if c2x2:
-        return conv_block_2c2(in_nc, out_nc, act_type=act_type)
-
-    assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode)
-    padding = get_valid_padding(kernel_size, dilation)
-    p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None
-    padding = padding if pad_type == "zero" else 0
-
-    c = nn.Conv2d(
-        in_nc,
-        out_nc,
-        kernel_size=kernel_size,
-        stride=stride,
-        padding=padding,
-        dilation=dilation,
-        bias=bias,
-        groups=groups,
-    )
-    a = act(act_type) if act_type else None
-    if mode in ("CNA", "CNAC"):
-        n = norm(norm_type, out_nc) if norm_type else None
-        return sequential(p, c, n, a)
-    elif mode == "NAC":
-        if norm_type is None and act_type is not None:
-            a = act(act_type, inplace=False)
-            # Important!
-            # input----ReLU(inplace)----Conv--+----output
-            #        |________________________|
-            # inplace ReLU will modify the input, therefore wrong output
-        n = norm(norm_type, in_nc) if norm_type else None
-        return sequential(n, a, p, c)
-    else:
-        assert False, f"Invalid conv mode {mode}"
-
-
-####################
-# Useful blocks
-####################
-
-
-class ResNetBlock(nn.Module):
-    """
-    ResNet Block, 3-3 style
-    with extra residual scaling used in EDSR
-    (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17)
-    """
-
-    def __init__(
-        self,
-        in_nc,
-        mid_nc,
-        out_nc,
-        kernel_size=3,
-        stride=1,
-        dilation=1,
-        groups=1,
-        bias=True,
-        pad_type="zero",
-        norm_type=None,
-        act_type="relu",
-        mode: ConvMode = "CNA",
-        res_scale=1,
-    ):
-        super(ResNetBlock, self).__init__()
-        conv0 = conv_block(
-            in_nc,
-            mid_nc,
-            kernel_size,
-            stride,
-            dilation,
-            groups,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-        )
-        if mode == "CNA":
-            act_type = None
-        if mode == "CNAC":  # Residual path: |-CNAC-|
-            act_type = None
-            norm_type = None
-        conv1 = conv_block(
-            mid_nc,
-            out_nc,
-            kernel_size,
-            stride,
-            dilation,
-            groups,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-        )
-        # if in_nc != out_nc:
-        #     self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \
-        #         None, None)
-        #     print('Need a projecter in ResNetBlock.')
-        # else:
-        #     self.project = lambda x:x
-        self.res = sequential(conv0, conv1)
-        self.res_scale = res_scale
-
-    def forward(self, x):
-        res = self.res(x).mul(self.res_scale)
-        return x + res
-
-
-class RRDB(nn.Module):
-    """
-    Residual in Residual Dense Block
-    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
-    """
-
-    def __init__(
-        self,
-        nf,
-        kernel_size=3,
-        gc=32,
-        stride=1,
-        bias: bool = True,
-        pad_type="zero",
-        norm_type=None,
-        act_type="leakyrelu",
-        mode: ConvMode = "CNA",
-        _convtype="Conv2D",
-        _spectral_norm=False,
-        plus=False,
-        c2x2=False,
-    ):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(
-            nf,
-            kernel_size,
-            gc,
-            stride,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-            plus=plus,
-            c2x2=c2x2,
-        )
-        self.RDB2 = ResidualDenseBlock_5C(
-            nf,
-            kernel_size,
-            gc,
-            stride,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-            plus=plus,
-            c2x2=c2x2,
-        )
-        self.RDB3 = ResidualDenseBlock_5C(
-            nf,
-            kernel_size,
-            gc,
-            stride,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-            plus=plus,
-            c2x2=c2x2,
-        )
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    """
-    Residual Dense Block
-    style: 5 convs
-    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
-    Modified options that can be used:
-        - "Partial Convolution based Padding" arXiv:1811.11718
-        - "Spectral normalization" arXiv:1802.05957
-        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
-            {Rakotonirina} and A. {Rasoanaivo}
-
-    Args:
-        nf (int): Channel number of intermediate features (num_feat).
-        gc (int): Channels for each growth (num_grow_ch: growth channel,
-            i.e. intermediate channels).
-        convtype (str): the type of convolution to use. Default: 'Conv2D'
-        gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new
-            trainable parameters)
-        plus (bool): enable the additional residual paths from ESRGAN+
-            (adds trainable parameters)
-    """
-
-    def __init__(
-        self,
-        nf=64,
-        kernel_size=3,
-        gc=32,
-        stride=1,
-        bias: bool = True,
-        pad_type="zero",
-        norm_type=None,
-        act_type="leakyrelu",
-        mode: ConvMode = "CNA",
-        plus=False,
-        c2x2=False,
-    ):
-        super(ResidualDenseBlock_5C, self).__init__()
-
-        ## +
-        self.conv1x1 = conv1x1(nf, gc) if plus else None
-        ## +
-
-        self.conv1 = conv_block(
-            nf,
-            gc,
-            kernel_size,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=act_type,
-            mode=mode,
-            c2x2=c2x2,
-        )
-        self.conv2 = conv_block(
-            nf + gc,
-            gc,
-            kernel_size,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=act_type,
-            mode=mode,
-            c2x2=c2x2,
-        )
-        self.conv3 = conv_block(
-            nf + 2 * gc,
-            gc,
-            kernel_size,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=act_type,
-            mode=mode,
-            c2x2=c2x2,
-        )
-        self.conv4 = conv_block(
-            nf + 3 * gc,
-            gc,
-            kernel_size,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=act_type,
-            mode=mode,
-            c2x2=c2x2,
-        )
-        if mode == "CNA":
-            last_act = None
-        else:
-            last_act = act_type
-        self.conv5 = conv_block(
-            nf + 4 * gc,
-            nf,
-            3,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=last_act,
-            mode=mode,
-            c2x2=c2x2,
-        )
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv2(torch.cat((x, x1), 1))
-        if self.conv1x1:
-            # pylint: disable=not-callable
-            x2 = x2 + self.conv1x1(x)  # +
-        x3 = self.conv3(torch.cat((x, x1, x2), 1))
-        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
-        if self.conv1x1:
-            x4 = x4 + x2  # +
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-####################
-# Upsampler
-####################
-
-
-def pixelshuffle_block(
-    in_nc: int,
-    out_nc: int,
-    upscale_factor=2,
-    kernel_size=3,
-    stride=1,
-    bias=True,
-    pad_type="zero",
-    norm_type: str | None = None,
-    act_type="relu",
-):
-    """
-    Pixel shuffle layer
-    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
-    Neural Network, CVPR17)
-    """
-    conv = conv_block(
-        in_nc,
-        out_nc * (upscale_factor**2),
-        kernel_size,
-        stride,
-        bias=bias,
-        pad_type=pad_type,
-        norm_type=None,
-        act_type=None,
-    )
-    pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
-    n = norm(norm_type, out_nc) if norm_type else None
-    a = act(act_type) if act_type else None
-    return sequential(conv, pixel_shuffle, n, a)
-
-
-def upconv_block(
-    in_nc: int,
-    out_nc: int,
-    upscale_factor=2,
-    kernel_size=3,
-    stride=1,
-    bias=True,
-    pad_type="zero",
-    norm_type: str | None = None,
-    act_type="relu",
-    mode="nearest",
-    c2x2=False,
-):
-    # Up conv
-    # described in https://distill.pub/2016/deconv-checkerboard/
-    upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode)
-    conv = conv_block(
-        in_nc,
-        out_nc,
-        kernel_size,
-        stride,
-        bias=bias,
-        pad_type=pad_type,
-        norm_type=norm_type,
-        act_type=act_type,
-        c2x2=c2x2,
-    )
-    return sequential(upsample, conv)
diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN b/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN
deleted file mode 100644
index 5ac273fd5..000000000
--- a/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN
+++ /dev/null
@@ -1,351 +0,0 @@
-Tencent is pleased to support the open source community by making GFPGAN available.
-
-Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
-
-GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.
-
-
-Terms of the Apache License Version 2.0:
----------------------------------------------
-Apache License
-
-Version 2.0, January 2004
-
-http://www.apache.org/licenses/
-
-TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-1. Definitions.
-
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-
-“Licensor” shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
-
-“Legal Entity” shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, “control” means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
-
-“You” (or “Your”) shall mean an individual or Legal Entity exercising permissions granted by this License.
-
-“Source” form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
-
-“Object” form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
-
-“Work” shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
-
-“Derivative Works” shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
-
-“Contribution” shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, “submitted” means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as “Not a Contribution.”
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-END OF TERMS AND CONDITIONS
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-Other  dependencies and licenses:
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-
-Open Source Software licensed under the Apache 2.0 license and Other Licenses of the Third-Party Components therein:
----------------------------------------------
-1. basicsr
-Copyright 2018-2020 BasicSR Authors
-
-
-This BasicSR project is released under the Apache 2.0 license.
-
-A copy of Apache 2.0 is included in this file.
-
-StyleGAN2
-The codes are modified from the repository stylegan2-pytorch. Many thanks to the author - Kim Seonghyeon 😊 for translating from the official TensorFlow codes to PyTorch ones. Here is the license of stylegan2-pytorch.
-The official repository is https://github.com/NVlabs/stylegan2, and here is the NVIDIA license.
-DFDNet
-The codes are largely modified from the repository DFDNet. Their license is Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
-
-Terms of the Nvidia License:
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-    3.5 Trademarks. This License does not grant any rights to use any
-    Licensor's or its affiliates' names, logos, or trademarks, except
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-
-    3.6 Termination. If you violate any term of this License, then your
-    rights under this License (including the grants in Sections 2.1 and
-    2.2) will terminate immediately.
-
-4. Disclaimer of Warranty.
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-THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY
-KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
-NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
-THIS LICENSE.
-
-5. Limitation of Liability.
-
-EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
-THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
-SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
-INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
-OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
-(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
-LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER
-COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
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-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-
-
-
-Open Source Software licensed under the BSD 3-Clause license:
----------------------------------------------
-1. torchvision
-Copyright (c) Soumith Chintala 2016,
-All rights reserved.
-
-2. torch
-Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
-Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
-Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
-Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
-Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
-Copyright (c) 2011-2013 NYU                      (Clement Farabet)
-Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
-Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
-Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
-
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-Terms of the BSD 3-Clause License:
----------------------------------------------
-Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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-2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
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-Open Source Software licensed under the BSD 3-Clause License and Other Licenses of the Third-Party Components therein:
----------------------------------------------
-1. numpy
-Copyright (c) 2005-2020, NumPy Developers.
-All rights reserved.
-
-A copy of BSD 3-Clause License is included in this file.
-
-The NumPy repository and source distributions bundle several libraries that are
-compatibly licensed.  We list these here.
-
-Name: Numpydoc
-Files: doc/sphinxext/numpydoc/*
-License: BSD-2-Clause
-  For details, see doc/sphinxext/LICENSE.txt
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-Name: scipy-sphinx-theme
-Files: doc/scipy-sphinx-theme/*
-License: BSD-3-Clause AND PSF-2.0 AND Apache-2.0
-  For details, see doc/scipy-sphinx-theme/LICENSE.txt
-
-Name: lapack-lite
-Files: numpy/linalg/lapack_lite/*
-License: BSD-3-Clause
-  For details, see numpy/linalg/lapack_lite/LICENSE.txt
-
-Name: tempita
-Files: tools/npy_tempita/*
-License: MIT
-  For details, see tools/npy_tempita/license.txt
-
-Name: dragon4
-Files: numpy/core/src/multiarray/dragon4.c
-License: MIT
-  For license text, see numpy/core/src/multiarray/dragon4.c
-
-
-
-Open Source Software licensed under the MIT license:
----------------------------------------------
-1. facexlib
-Copyright (c) 2020 Xintao Wang
-
-2. opencv-python
-Copyright (c) Olli-Pekka Heinisuo
-Please note that only files in cv2 package are used.
-
-
-Terms of the MIT License:
----------------------------------------------
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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-Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:
----------------------------------------------
-1. tqdm
-Copyright (c) 2013 noamraph
-
-`tqdm` is a product of collaborative work.
-Unless otherwise stated, all authors (see commit logs) retain copyright
-for their respective work, and release the work under the MIT licence
-(text below).
-
-Exceptions or notable authors are listed below
-in reverse chronological order:
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-* files: *
-  MPLv2.0 2015-2020 (c) Casper da Costa-Luis
-  [casperdcl](https://github.com/casperdcl).
-* files: tqdm/_tqdm.py
-  MIT 2016 (c) [PR #96] on behalf of Google Inc.
-* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore
-  MIT 2013 (c) Noam Yorav-Raphael, original author.
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-[PR #96]: https://github.com/tqdm/tqdm/pull/96
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-Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
------------------------------------------------
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-This Source Code Form is subject to the terms of the
-Mozilla Public License, v. 2.0.
-If a copy of the MPL was not distributed with this file,
-You can obtain one at https://mozilla.org/MPL/2.0/.
-
-
-MIT License (MIT)
------------------
-
-Copyright (c) 2013 noamraph
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in
-the Software without restriction, including without limitation the rights to
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software is furnished to do so,
-subject to the following conditions:
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-The above copyright notice and this permission notice shall be included in all
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-COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
-IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-RestoreFormer b/comfy_extras/chainner_models/architecture/face/LICENSE-RestoreFormer
deleted file mode 100644
index 5ac273fd5..000000000
--- a/comfy_extras/chainner_models/architecture/face/LICENSE-RestoreFormer
+++ /dev/null
@@ -1,351 +0,0 @@
-Tencent is pleased to support the open source community by making GFPGAN available.
-
-Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
-
-GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.
-
-
-Terms of the Apache License Version 2.0:
----------------------------------------------
-Apache License
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-1. basicsr
-Copyright 2018-2020 BasicSR Authors
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-A copy of Apache 2.0 is included in this file.
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-StyleGAN2
-The codes are modified from the repository stylegan2-pytorch. Many thanks to the author - Kim Seonghyeon 😊 for translating from the official TensorFlow codes to PyTorch ones. Here is the license of stylegan2-pytorch.
-The official repository is https://github.com/NVlabs/stylegan2, and here is the NVIDIA license.
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-COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
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-Permission is hereby granted, free of charge, to any person obtaining a copy
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-SOFTWARE.
-
-
-
-Open Source Software licensed under the BSD 3-Clause license:
----------------------------------------------
-1. torchvision
-Copyright (c) Soumith Chintala 2016,
-All rights reserved.
-
-2. torch
-Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
-Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
-Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
-Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
-Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
-Copyright (c) 2011-2013 NYU                      (Clement Farabet)
-Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
-Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
-Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
-
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-Terms of the BSD 3-Clause License:
----------------------------------------------
-Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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-A copy of BSD 3-Clause License is included in this file.
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-The NumPy repository and source distributions bundle several libraries that are
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-Files: doc/sphinxext/numpydoc/*
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-Name: scipy-sphinx-theme
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-License: BSD-3-Clause AND PSF-2.0 AND Apache-2.0
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-Name: lapack-lite
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-Name: tempita
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-License: MIT
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-License: MIT
-  For license text, see numpy/core/src/multiarray/dragon4.c
-
-
-
-Open Source Software licensed under the MIT license:
----------------------------------------------
-1. facexlib
-Copyright (c) 2020 Xintao Wang
-
-2. opencv-python
-Copyright (c) Olli-Pekka Heinisuo
-Please note that only files in cv2 package are used.
-
-
-Terms of the MIT License:
----------------------------------------------
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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----------------------------------------------
-1. tqdm
-Copyright (c) 2013 noamraph
-
-`tqdm` is a product of collaborative work.
-Unless otherwise stated, all authors (see commit logs) retain copyright
-for their respective work, and release the work under the MIT licence
-(text below).
-
-Exceptions or notable authors are listed below
-in reverse chronological order:
-
-* files: *
-  MPLv2.0 2015-2020 (c) Casper da Costa-Luis
-  [casperdcl](https://github.com/casperdcl).
-* files: tqdm/_tqdm.py
-  MIT 2016 (c) [PR #96] on behalf of Google Inc.
-* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore
-  MIT 2013 (c) Noam Yorav-Raphael, original author.
-
-[PR #96]: https://github.com/tqdm/tqdm/pull/96
-
-
-Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
------------------------------------------------
-
-This Source Code Form is subject to the terms of the
-Mozilla Public License, v. 2.0.
-If a copy of the MPL was not distributed with this file,
-You can obtain one at https://mozilla.org/MPL/2.0/.
-
-
-MIT License (MIT)
------------------
-
-Copyright (c) 2013 noamraph
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in
-the Software without restriction, including without limitation the rights to
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software is furnished to do so,
-subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
-FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
-COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
-IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer b/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer
deleted file mode 100644
index be6c4ed80..000000000
--- a/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer
+++ /dev/null
@@ -1,35 +0,0 @@
-S-Lab License 1.0
-
-Copyright 2022 S-Lab
-
-Redistribution and use for non-commercial purpose in source and
-binary forms, with or without modification, are permitted provided
-that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright
-   notice, this list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright
-   notice, this list of conditions and the following disclaimer in
-   the documentation and/or other materials provided with the
-   distribution.
-
-3. Neither the name of the copyright holder nor the names of its
-   contributors may be used to endorse or promote products derived
-   from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-In the event that redistribution and/or use for commercial purpose in
-source or binary forms, with or without modification is required,
-please contact the contributor(s) of the work.
diff --git a/comfy_extras/chainner_models/architecture/face/arcface_arch.py b/comfy_extras/chainner_models/architecture/face/arcface_arch.py
deleted file mode 100644
index b548af059..000000000
--- a/comfy_extras/chainner_models/architecture/face/arcface_arch.py
+++ /dev/null
@@ -1,265 +0,0 @@
-import torch.nn as nn
-
-
-def conv3x3(inplanes, outplanes, stride=1):
-    """A simple wrapper for 3x3 convolution with padding.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        outplanes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-    """
-    return nn.Conv2d(
-        inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False
-    )
-
-
-class BasicBlock(nn.Module):
-    """Basic residual block used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-    """
-
-    expansion = 1  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class IRBlock(nn.Module):
-    """Improved residual block (IR Block) used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
-    """
-
-    expansion = 1  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
-        super(IRBlock, self).__init__()
-        self.bn0 = nn.BatchNorm2d(inplanes)
-        self.conv1 = conv3x3(inplanes, inplanes)
-        self.bn1 = nn.BatchNorm2d(inplanes)
-        self.prelu = nn.PReLU()
-        self.conv2 = conv3x3(inplanes, planes, stride)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.use_se = use_se
-        if self.use_se:
-            self.se = SEBlock(planes)
-
-    def forward(self, x):
-        residual = x
-        out = self.bn0(x)
-        out = self.conv1(out)
-        out = self.bn1(out)
-        out = self.prelu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        if self.use_se:
-            out = self.se(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.prelu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    """Bottleneck block used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-    """
-
-    expansion = 4  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(
-            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
-        )
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(
-            planes, planes * self.expansion, kernel_size=1, bias=False
-        )
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class SEBlock(nn.Module):
-    """The squeeze-and-excitation block (SEBlock) used in the IRBlock.
-
-    Args:
-        channel (int): Channel number of inputs.
-        reduction (int): Channel reduction ration. Default: 16.
-    """
-
-    def __init__(self, channel, reduction=16):
-        super(SEBlock, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(
-            1
-        )  # pool to 1x1 without spatial information
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction),
-            nn.PReLU(),
-            nn.Linear(channel // reduction, channel),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y
-
-
-class ResNetArcFace(nn.Module):
-    """ArcFace with ResNet architectures.
-
-    Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
-
-    Args:
-        block (str): Block used in the ArcFace architecture.
-        layers (tuple(int)): Block numbers in each layer.
-        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
-    """
-
-    def __init__(self, block, layers, use_se=True):
-        if block == "IRBlock":
-            block = IRBlock
-        self.inplanes = 64
-        self.use_se = use_se
-        super(ResNetArcFace, self).__init__()
-
-        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.prelu = nn.PReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.bn4 = nn.BatchNorm2d(512)
-        self.dropout = nn.Dropout()
-        self.fc5 = nn.Linear(512 * 8 * 8, 512)
-        self.bn5 = nn.BatchNorm1d(512)
-
-        # initialization
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.xavier_normal_(m.weight)
-            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.xavier_normal_(m.weight)
-                nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, block, planes, num_blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(
-                    self.inplanes,
-                    planes * block.expansion,
-                    kernel_size=1,
-                    stride=stride,
-                    bias=False,
-                ),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-        layers = []
-        layers.append(
-            block(self.inplanes, planes, stride, downsample, use_se=self.use_se)
-        )
-        self.inplanes = planes
-        for _ in range(1, num_blocks):
-            layers.append(block(self.inplanes, planes, use_se=self.use_se))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.prelu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.bn4(x)
-        x = self.dropout(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc5(x)
-        x = self.bn5(x)
-
-        return x
diff --git a/comfy_extras/chainner_models/architecture/face/codeformer.py b/comfy_extras/chainner_models/architecture/face/codeformer.py
deleted file mode 100644
index 066140078..000000000
--- a/comfy_extras/chainner_models/architecture/face/codeformer.py
+++ /dev/null
@@ -1,790 +0,0 @@
-"""
-Modified from https://github.com/sczhou/CodeFormer
-VQGAN code, adapted from the original created by the Unleashing Transformers authors:
-https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
-This verison of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.
-"""
-import math
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import logging as logger
-from torch import Tensor
-
-
-class VectorQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
-        self.embedding.weight.data.uniform_(
-            -1.0 / self.codebook_size, 1.0 / self.codebook_size
-        )
-
-    def forward(self, z):
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.emb_dim)
-
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-        d = (
-            (z_flattened**2).sum(dim=1, keepdim=True)
-            + (self.embedding.weight**2).sum(1)
-            - 2 * torch.matmul(z_flattened, self.embedding.weight.t())
-        )
-
-        mean_distance = torch.mean(d)
-        # find closest encodings
-        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-        min_encoding_scores, min_encoding_indices = torch.topk(
-            d, 1, dim=1, largest=False
-        )
-        # [0-1], higher score, higher confidence
-        min_encoding_scores = torch.exp(-min_encoding_scores / 10)
-
-        min_encodings = torch.zeros(
-            min_encoding_indices.shape[0], self.codebook_size
-        ).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
-            (z_q - z.detach()) ** 2
-        )
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return (
-            z_q,
-            loss,
-            {
-                "perplexity": perplexity,
-                "min_encodings": min_encodings,
-                "min_encoding_indices": min_encoding_indices,
-                "min_encoding_scores": min_encoding_scores,
-                "mean_distance": mean_distance,
-            },
-        )
-
-    def get_codebook_feat(self, indices, shape):
-        # input indices: batch*token_num -> (batch*token_num)*1
-        # shape: batch, height, width, channel
-        indices = indices.view(-1, 1)
-        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
-        min_encodings.scatter_(1, indices, 1)
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:  # reshape back to match original input shape
-            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-class GumbelQuantizer(nn.Module):
-    def __init__(
-        self,
-        codebook_size,
-        emb_dim,
-        num_hiddens,
-        straight_through=False,
-        kl_weight=5e-4,
-        temp_init=1.0,
-    ):
-        super().__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.straight_through = straight_through
-        self.temperature = temp_init
-        self.kl_weight = kl_weight
-        self.proj = nn.Conv2d(
-            num_hiddens, codebook_size, 1
-        )  # projects last encoder layer to quantized logits
-        self.embed = nn.Embedding(codebook_size, emb_dim)
-
-    def forward(self, z):
-        hard = self.straight_through if self.training else True
-
-        logits = self.proj(z)
-
-        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
-
-        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
-
-        # + kl divergence to the prior loss
-        qy = F.softmax(logits, dim=1)
-        diff = (
-            self.kl_weight
-            * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
-        )
-        min_encoding_indices = soft_one_hot.argmax(dim=1)
-
-        return z_q, diff, {"min_encoding_indices": min_encoding_indices}
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=3, stride=2, padding=0
-        )
-
-    def forward(self, x):
-        pad = (0, 1, 0, 1)
-        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = nn.Conv2d(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1
-        )
-
-    def forward(self, x):
-        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-
-        return x
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = normalize(in_channels)
-        self.q = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, c, h * w)
-        q = q.permute(0, 2, 1)
-        k = k.reshape(b, c, h * w)
-        w_ = torch.bmm(q, k)
-        w_ = w_ * (int(c) ** (-0.5))
-        w_ = F.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b, c, h * w)
-        w_ = w_.permute(0, 2, 1)
-        h_ = torch.bmm(v, w_)
-        h_ = h_.reshape(b, c, h, w)
-
-        h_ = self.proj_out(h_)
-
-        return x + h_
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        nf,
-        out_channels,
-        ch_mult,
-        num_res_blocks,
-        resolution,
-        attn_resolutions,
-    ):
-        super().__init__()
-        self.nf = nf
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.attn_resolutions = attn_resolutions
-
-        curr_res = self.resolution
-        in_ch_mult = (1,) + tuple(ch_mult)
-
-        blocks = []
-        # initial convultion
-        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
-
-        # residual and downsampling blocks, with attention on smaller res (16x16)
-        for i in range(self.num_resolutions):
-            block_in_ch = nf * in_ch_mult[i]
-            block_out_ch = nf * ch_mult[i]
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-                if curr_res in attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != self.num_resolutions - 1:
-                blocks.append(Downsample(block_in_ch))
-                curr_res = curr_res // 2
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))  # type: ignore
-        blocks.append(AttnBlock(block_in_ch))  # type: ignore
-        blocks.append(ResBlock(block_in_ch, block_in_ch))  # type: ignore
-
-        # normalise and convert to latent size
-        blocks.append(normalize(block_in_ch))  # type: ignore
-        blocks.append(
-            nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1)  # type: ignore
-        )
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-
-        return x
-
-
-class Generator(nn.Module):
-    def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim):
-        super().__init__()
-        self.nf = nf
-        self.ch_mult = ch_mult
-        self.num_resolutions = len(self.ch_mult)
-        self.num_res_blocks = res_blocks
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
-        self.in_channels = emb_dim
-        self.out_channels = 3
-        block_in_ch = self.nf * self.ch_mult[-1]
-        curr_res = self.resolution // 2 ** (self.num_resolutions - 1)
-
-        blocks = []
-        # initial conv
-        blocks.append(
-            nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)
-        )
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        for i in reversed(range(self.num_resolutions)):
-            block_out_ch = self.nf * self.ch_mult[i]
-
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-
-                if curr_res in self.attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != 0:
-                blocks.append(Upsample(block_in_ch))
-                curr_res = curr_res * 2
-
-        blocks.append(normalize(block_in_ch))
-        blocks.append(
-            nn.Conv2d(
-                block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1
-            )
-        )
-
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-
-        return x
-
-
-class VQAutoEncoder(nn.Module):
-    def __init__(
-        self,
-        img_size,
-        nf,
-        ch_mult,
-        quantizer="nearest",
-        res_blocks=2,
-        attn_resolutions=[16],
-        codebook_size=1024,
-        emb_dim=256,
-        beta=0.25,
-        gumbel_straight_through=False,
-        gumbel_kl_weight=1e-8,
-        model_path=None,
-    ):
-        super().__init__()
-        self.in_channels = 3
-        self.nf = nf
-        self.n_blocks = res_blocks
-        self.codebook_size = codebook_size
-        self.embed_dim = emb_dim
-        self.ch_mult = ch_mult
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
-        self.quantizer_type = quantizer
-        self.encoder = Encoder(
-            self.in_channels,
-            self.nf,
-            self.embed_dim,
-            self.ch_mult,
-            self.n_blocks,
-            self.resolution,
-            self.attn_resolutions,
-        )
-        if self.quantizer_type == "nearest":
-            self.beta = beta  # 0.25
-            self.quantize = VectorQuantizer(
-                self.codebook_size, self.embed_dim, self.beta
-            )
-        elif self.quantizer_type == "gumbel":
-            self.gumbel_num_hiddens = emb_dim
-            self.straight_through = gumbel_straight_through
-            self.kl_weight = gumbel_kl_weight
-            self.quantize = GumbelQuantizer(
-                self.codebook_size,
-                self.embed_dim,
-                self.gumbel_num_hiddens,
-                self.straight_through,
-                self.kl_weight,
-            )
-        self.generator = Generator(
-            nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim
-        )
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location="cpu")
-            if "params_ema" in chkpt:
-                self.load_state_dict(
-                    torch.load(model_path, map_location="cpu")["params_ema"]
-                )
-                logger.info(f"vqgan is loaded from: {model_path} [params_ema]")
-            elif "params" in chkpt:
-                self.load_state_dict(
-                    torch.load(model_path, map_location="cpu")["params"]
-                )
-                logger.info(f"vqgan is loaded from: {model_path} [params]")
-            else:
-                raise ValueError("Wrong params!")
-
-    def forward(self, x):
-        x = self.encoder(x)
-        quant, codebook_loss, quant_stats = self.quantize(x)
-        x = self.generator(quant)
-        return x, codebook_loss, quant_stats
-
-
-def calc_mean_std(feat, eps=1e-5):
-    """Calculate mean and std for adaptive_instance_normalization.
-    Args:
-        feat (Tensor): 4D tensor.
-        eps (float): A small value added to the variance to avoid
-            divide-by-zero. Default: 1e-5.
-    """
-    size = feat.size()
-    assert len(size) == 4, "The input feature should be 4D tensor."
-    b, c = size[:2]
-    feat_var = feat.view(b, c, -1).var(dim=2) + eps
-    feat_std = feat_var.sqrt().view(b, c, 1, 1)
-    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
-    return feat_mean, feat_std
-
-
-def adaptive_instance_normalization(content_feat, style_feat):
-    """Adaptive instance normalization.
-    Adjust the reference features to have the similar color and illuminations
-    as those in the degradate features.
-    Args:
-        content_feat (Tensor): The reference feature.
-        style_feat (Tensor): The degradate features.
-    """
-    size = content_feat.size()
-    style_mean, style_std = calc_mean_std(style_feat)
-    content_mean, content_std = calc_mean_std(content_feat)
-    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(
-        size
-    )
-    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(
-        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
-    ):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, x, mask=None):
-        if mask is None:
-            mask = torch.zeros(
-                (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool
-            )
-        not_mask = ~mask  # pylint: disable=invalid-unary-operand-type
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-def _get_activation_fn(activation):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
-
-
-class TransformerSALayer(nn.Module):
-    def __init__(
-        self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
-        # Implementation of Feedforward model - MLP
-        self.linear1 = nn.Linear(embed_dim, dim_mlp)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_mlp, embed_dim)
-
-        self.norm1 = nn.LayerNorm(embed_dim)
-        self.norm2 = nn.LayerNorm(embed_dim)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward(
-        self,
-        tgt,
-        tgt_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        # self attention
-        tgt2 = self.norm1(tgt)
-        q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(
-            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
-        )[0]
-        tgt = tgt + self.dropout1(tgt2)
-
-        # ffn
-        tgt2 = self.norm2(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout2(tgt2)
-        return tgt
-
-
-def normalize(in_channels):
-    return torch.nn.GroupNorm(
-        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
-    )
-
-
-@torch.jit.script  # type: ignore
-def swish(x):
-    return x * torch.sigmoid(x)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channels, out_channels=None):
-        super(ResBlock, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = in_channels if out_channels is None else out_channels
-        self.norm1 = normalize(in_channels)
-        self.conv1 = nn.Conv2d(
-            in_channels, out_channels, kernel_size=3, stride=1, padding=1  # type: ignore
-        )
-        self.norm2 = normalize(out_channels)
-        self.conv2 = nn.Conv2d(
-            out_channels, out_channels, kernel_size=3, stride=1, padding=1  # type: ignore
-        )
-        if self.in_channels != self.out_channels:
-            self.conv_out = nn.Conv2d(
-                in_channels, out_channels, kernel_size=1, stride=1, padding=0  # type: ignore
-            )
-
-    def forward(self, x_in):
-        x = x_in
-        x = self.norm1(x)
-        x = swish(x)
-        x = self.conv1(x)
-        x = self.norm2(x)
-        x = swish(x)
-        x = self.conv2(x)
-        if self.in_channels != self.out_channels:
-            x_in = self.conv_out(x_in)
-
-        return x + x_in
-
-
-class Fuse_sft_block(nn.Module):
-    def __init__(self, in_ch, out_ch):
-        super().__init__()
-        self.encode_enc = ResBlock(2 * in_ch, out_ch)
-
-        self.scale = nn.Sequential(
-            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, True),
-            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
-        )
-
-        self.shift = nn.Sequential(
-            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, True),
-            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
-        )
-
-    def forward(self, enc_feat, dec_feat, w=1):
-        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
-        scale = self.scale(enc_feat)
-        shift = self.shift(enc_feat)
-        residual = w * (dec_feat * scale + shift)
-        out = dec_feat + residual
-        return out
-
-
-class CodeFormer(VQAutoEncoder):
-    def __init__(self, state_dict):
-        dim_embd = 512
-        n_head = 8
-        n_layers = 9
-        codebook_size = 1024
-        latent_size = 256
-        connect_list = ["32", "64", "128", "256"]
-        fix_modules = ["quantize", "generator"]
-
-        # This is just a guess as I only have one model to look at
-        position_emb = state_dict["position_emb"]
-        dim_embd = position_emb.shape[1]
-        latent_size = position_emb.shape[0]
-
-        try:
-            n_layers = len(
-                set([x.split(".")[1] for x in state_dict.keys() if "ft_layers" in x])
-            )
-        except:
-            pass
-
-        codebook_size = state_dict["quantize.embedding.weight"].shape[0]
-
-        # This is also just another guess
-        n_head_exp = (
-            state_dict["ft_layers.0.self_attn.in_proj_weight"].shape[0] // dim_embd
-        )
-        n_head = 2**n_head_exp
-
-        in_nc = state_dict["encoder.blocks.0.weight"].shape[1]
-
-        self.model_arch = "CodeFormer"
-        self.sub_type = "Face SR"
-        self.scale = 8
-        self.in_nc = in_nc
-        self.out_nc = in_nc
-
-        self.state = state_dict
-
-        self.supports_fp16 = False
-        self.supports_bf16 = True
-        self.min_size_restriction = 16
-
-        super(CodeFormer, self).__init__(
-            512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size
-        )
-
-        if fix_modules is not None:
-            for module in fix_modules:
-                for param in getattr(self, module).parameters():
-                    param.requires_grad = False
-
-        self.connect_list = connect_list
-        self.n_layers = n_layers
-        self.dim_embd = dim_embd
-        self.dim_mlp = dim_embd * 2
-
-        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))  # type: ignore
-        self.feat_emb = nn.Linear(256, self.dim_embd)
-
-        # transformer
-        self.ft_layers = nn.Sequential(
-            *[
-                TransformerSALayer(
-                    embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0
-                )
-                for _ in range(self.n_layers)
-            ]
-        )
-
-        # logits_predict head
-        self.idx_pred_layer = nn.Sequential(
-            nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)
-        )
-
-        self.channels = {
-            "16": 512,
-            "32": 256,
-            "64": 256,
-            "128": 128,
-            "256": 128,
-            "512": 64,
-        }
-
-        # after second residual block for > 16, before attn layer for ==16
-        self.fuse_encoder_block = {
-            "512": 2,
-            "256": 5,
-            "128": 8,
-            "64": 11,
-            "32": 14,
-            "16": 18,
-        }
-        # after first residual block for > 16, before attn layer for ==16
-        self.fuse_generator_block = {
-            "16": 6,
-            "32": 9,
-            "64": 12,
-            "128": 15,
-            "256": 18,
-            "512": 21,
-        }
-
-        # fuse_convs_dict
-        self.fuse_convs_dict = nn.ModuleDict()
-        for f_size in self.connect_list:
-            in_ch = self.channels[f_size]
-            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
-
-        self.load_state_dict(state_dict)
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self, x, weight=0.5, **kwargs):
-        detach_16 = True
-        code_only = False
-        adain = True
-        # ################### Encoder #####################
-        enc_feat_dict = {}
-        out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
-        for i, block in enumerate(self.encoder.blocks):
-            x = block(x)
-            if i in out_list:
-                enc_feat_dict[str(x.shape[-1])] = x.clone()
-
-        lq_feat = x
-        # ################# Transformer ###################
-        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
-        pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1)
-        # BCHW -> BC(HW) -> (HW)BC
-        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1))
-        query_emb = feat_emb
-        # Transformer encoder
-        for layer in self.ft_layers:
-            query_emb = layer(query_emb, query_pos=pos_emb)
-
-        # output logits
-        logits = self.idx_pred_layer(query_emb)  # (hw)bn
-        logits = logits.permute(1, 0, 2)  # (hw)bn -> b(hw)n
-
-        if code_only:  # for training stage II
-            # logits doesn't need softmax before cross_entropy loss
-            return logits, lq_feat
-
-        # ################# Quantization ###################
-        # if self.training:
-        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
-        #     # b(hw)c -> bc(hw) -> bchw
-        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
-        # ------------
-        soft_one_hot = F.softmax(logits, dim=2)
-        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
-        quant_feat = self.quantize.get_codebook_feat(
-            top_idx, shape=[x.shape[0], 16, 16, 256]  # type: ignore
-        )
-        # preserve gradients
-        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
-
-        if detach_16:
-            quant_feat = quant_feat.detach()  # for training stage III
-        if adain:
-            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
-
-        # ################## Generator ####################
-        x = quant_feat
-        fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
-
-        for i, block in enumerate(self.generator.blocks):
-            x = block(x)
-            if i in fuse_list:  # fuse after i-th block
-                f_size = str(x.shape[-1])
-                if weight > 0:
-                    x = self.fuse_convs_dict[f_size](
-                        enc_feat_dict[f_size].detach(), x, weight
-                    )
-        out = x
-        # logits doesn't need softmax before cross_entropy loss
-        # return out, logits, lq_feat
-        return out, logits
diff --git a/comfy_extras/chainner_models/architecture/face/fused_act.py b/comfy_extras/chainner_models/architecture/face/fused_act.py
deleted file mode 100644
index 7ed526547..000000000
--- a/comfy_extras/chainner_models/architecture/face/fused_act.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# pylint: skip-file
-# type: ignore
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
-
-import torch
-from torch import nn
-from torch.autograd import Function
-
-fused_act_ext = None
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-    @staticmethod
-    def forward(ctx, grad_output, out, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused_act_ext.fused_bias_act(
-            grad_output, empty, out, 3, 1, negative_slope, scale
-        )
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        grad_bias = grad_input.sum(dim).detach()
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        (out,) = ctx.saved_tensors
-        gradgrad_out = fused_act_ext.fused_bias_act(
-            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
-        )
-
-        return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-        out = fused_act_ext.fused_bias_act(
-            input, bias, empty, 3, 0, negative_slope, scale
-        )
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        (out,) = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
-            grad_output, out, ctx.negative_slope, ctx.scale
-        )
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
-    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py b/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py
deleted file mode 100644
index b6e820e00..000000000
--- a/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py
+++ /dev/null
@@ -1,389 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-
-from .gfpganv1_arch import ResUpBlock
-from .stylegan2_bilinear_arch import (
-    ConvLayer,
-    EqualConv2d,
-    EqualLinear,
-    ResBlock,
-    ScaledLeakyReLU,
-    StyleGAN2GeneratorBilinear,
-)
-
-
-class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-    It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for
-    deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        lr_mlp=0.01,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(StyleGAN2GeneratorBilinearSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-        )
-        self.sft_half = sft_half
-
-    def forward(
-        self,
-        styles,
-        conditions,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2GeneratorBilinearSFT.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class GFPGANBilinear(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-    It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for
-    deployment. It can be easily converted to the clean version: GFPGANv1Clean.
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        channel_multiplier=1,
-        decoder_load_path=None,
-        fix_decoder=True,
-        # for stylegan decoder
-        num_mlp=8,
-        lr_mlp=0.01,
-        input_is_latent=False,
-        different_w=False,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(GFPGANBilinear, self).__init__()
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-        self.min_size_restriction = 512
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            "4": int(512 * unet_narrow),
-            "8": int(512 * unet_narrow),
-            "16": int(512 * unet_narrow),
-            "32": int(512 * unet_narrow),
-            "64": int(256 * channel_multiplier * unet_narrow),
-            "128": int(128 * channel_multiplier * unet_narrow),
-            "256": int(64 * channel_multiplier * unet_narrow),
-            "512": int(32 * channel_multiplier * unet_narrow),
-            "1024": int(16 * channel_multiplier * unet_narrow),
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2 ** (int(math.log(out_size, 2)))
-
-        self.conv_body_first = ConvLayer(
-            3, channels[f"{first_out_size}"], 1, bias=True, activate=True
-        )
-
-        # downsample
-        in_channels = channels[f"{first_out_size}"]
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f"{2**(i - 1)}"]
-            self.conv_body_down.append(ResBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        self.final_conv = ConvLayer(
-            in_channels, channels["4"], 3, bias=True, activate=True
-        )
-
-        # upsample
-        in_channels = channels["4"]
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(
-                EqualConv2d(
-                    channels[f"{2**i}"],
-                    3,
-                    1,
-                    stride=1,
-                    padding=0,
-                    bias=True,
-                    bias_init_val=0,
-                )
-            )
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = EqualLinear(
-            channels["4"] * 4 * 4,
-            linear_out_channel,
-            bias=True,
-            bias_init_val=0,
-            lr_mul=1,
-            activation=None,
-        )
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-            sft_half=sft_half,
-        )
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(
-                    decoder_load_path, map_location=lambda storage, loc: storage
-                )["params_ema"]
-            )
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    EqualConv2d(
-                        out_channels,
-                        out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(
-                        out_channels,
-                        sft_out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=1,
-                    ),
-                )
-            )
-            self.condition_shift.append(
-                nn.Sequential(
-                    EqualConv2d(
-                        out_channels,
-                        out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(
-                        out_channels,
-                        sft_out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                )
-            )
-
-    def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
-        """Forward function for GFPGANBilinear.
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = self.conv_body_first(x)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-
-        feat = self.final_conv(feat)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder(
-            [style_code],
-            conditions,
-            return_latents=return_latents,
-            input_is_latent=self.input_is_latent,
-            randomize_noise=randomize_noise,
-        )
-
-        return image, out_rgbs
diff --git a/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py b/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py
deleted file mode 100644
index 72d72fc86..000000000
--- a/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py
+++ /dev/null
@@ -1,566 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .fused_act import FusedLeakyReLU
-from .stylegan2_arch import (
-    ConvLayer,
-    EqualConv2d,
-    EqualLinear,
-    ResBlock,
-    ScaledLeakyReLU,
-    StyleGAN2Generator,
-)
-
-
-class StyleGAN2GeneratorSFT(StyleGAN2Generator):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        resample_kernel=(1, 3, 3, 1),
-        lr_mlp=0.01,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(StyleGAN2GeneratorSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-        )
-        self.sft_half = sft_half
-
-    def forward(
-        self,
-        styles,
-        conditions,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2GeneratorSFT.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ConvUpLayer(nn.Module):
-    """Convolutional upsampling layer. It uses bilinear upsampler + Conv.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input. Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        bias=True,
-        bias_init_val=0,
-        activate=True,
-    ):
-        super(ConvUpLayer, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        # self.scale is used to scale the convolution weights, which is related to the common initializations.
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channels, in_channels, kernel_size, kernel_size)
-        )
-
-        if bias and not activate:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-        # activation
-        if activate:
-            if bias:
-                self.activation = FusedLeakyReLU(out_channels)
-            else:
-                self.activation = ScaledLeakyReLU(0.2)
-        else:
-            self.activation = None
-
-    def forward(self, x):
-        # bilinear upsample
-        out = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False)
-        # conv
-        out = F.conv2d(
-            out,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-        # activation
-        if self.activation is not None:
-            out = self.activation(out)
-        return out
-
-
-class ResUpBlock(nn.Module):
-    """Residual block with upsampling.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-    """
-
-    def __init__(self, in_channels, out_channels):
-        super(ResUpBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvUpLayer(
-            in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True
-        )
-        self.skip = ConvUpLayer(
-            in_channels, out_channels, 1, bias=False, activate=False
-        )
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
-
-
-class GFPGANv1(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        channel_multiplier=1,
-        resample_kernel=(1, 3, 3, 1),
-        decoder_load_path=None,
-        fix_decoder=True,
-        # for stylegan decoder
-        num_mlp=8,
-        lr_mlp=0.01,
-        input_is_latent=False,
-        different_w=False,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(GFPGANv1, self).__init__()
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            "4": int(512 * unet_narrow),
-            "8": int(512 * unet_narrow),
-            "16": int(512 * unet_narrow),
-            "32": int(512 * unet_narrow),
-            "64": int(256 * channel_multiplier * unet_narrow),
-            "128": int(128 * channel_multiplier * unet_narrow),
-            "256": int(64 * channel_multiplier * unet_narrow),
-            "512": int(32 * channel_multiplier * unet_narrow),
-            "1024": int(16 * channel_multiplier * unet_narrow),
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2 ** (int(math.log(out_size, 2)))
-
-        self.conv_body_first = ConvLayer(
-            3, channels[f"{first_out_size}"], 1, bias=True, activate=True
-        )
-
-        # downsample
-        in_channels = channels[f"{first_out_size}"]
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f"{2**(i - 1)}"]
-            self.conv_body_down.append(
-                ResBlock(in_channels, out_channels, resample_kernel)
-            )
-            in_channels = out_channels
-
-        self.final_conv = ConvLayer(
-            in_channels, channels["4"], 3, bias=True, activate=True
-        )
-
-        # upsample
-        in_channels = channels["4"]
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(
-                EqualConv2d(
-                    channels[f"{2**i}"],
-                    3,
-                    1,
-                    stride=1,
-                    padding=0,
-                    bias=True,
-                    bias_init_val=0,
-                )
-            )
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = EqualLinear(
-            channels["4"] * 4 * 4,
-            linear_out_channel,
-            bias=True,
-            bias_init_val=0,
-            lr_mul=1,
-            activation=None,
-        )
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-            sft_half=sft_half,
-        )
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(
-                    decoder_load_path, map_location=lambda storage, loc: storage
-                )["params_ema"]
-            )
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    EqualConv2d(
-                        out_channels,
-                        out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(
-                        out_channels,
-                        sft_out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=1,
-                    ),
-                )
-            )
-            self.condition_shift.append(
-                nn.Sequential(
-                    EqualConv2d(
-                        out_channels,
-                        out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(
-                        out_channels,
-                        sft_out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                )
-            )
-
-    def forward(
-        self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs
-    ):
-        """Forward function for GFPGANv1.
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = self.conv_body_first(x)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-
-        feat = self.final_conv(feat)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder(
-            [style_code],
-            conditions,
-            return_latents=return_latents,
-            input_is_latent=self.input_is_latent,
-            randomize_noise=randomize_noise,
-        )
-
-        return image, out_rgbs
-
-
-class FacialComponentDiscriminator(nn.Module):
-    """Facial component (eyes, mouth, noise) discriminator used in GFPGAN."""
-
-    def __init__(self):
-        super(FacialComponentDiscriminator, self).__init__()
-        # It now uses a VGG-style architectrue with fixed model size
-        self.conv1 = ConvLayer(
-            3,
-            64,
-            3,
-            downsample=False,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.conv2 = ConvLayer(
-            64,
-            128,
-            3,
-            downsample=True,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.conv3 = ConvLayer(
-            128,
-            128,
-            3,
-            downsample=False,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.conv4 = ConvLayer(
-            128,
-            256,
-            3,
-            downsample=True,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.conv5 = ConvLayer(
-            256,
-            256,
-            3,
-            downsample=False,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False)
-
-    def forward(self, x, return_feats=False, **kwargs):
-        """Forward function for FacialComponentDiscriminator.
-        Args:
-            x (Tensor): Input images.
-            return_feats (bool): Whether to return intermediate features. Default: False.
-        """
-        feat = self.conv1(x)
-        feat = self.conv3(self.conv2(feat))
-        rlt_feats = []
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        feat = self.conv5(self.conv4(feat))
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        out = self.final_conv(feat)
-
-        if return_feats:
-            return out, rlt_feats
-        else:
-            return out, None
diff --git a/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py b/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py
deleted file mode 100644
index 16470d634..000000000
--- a/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .stylegan2_clean_arch import StyleGAN2GeneratorClean
-
-
-class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(StyleGAN2GeneratorCSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            narrow=narrow,
-        )
-        self.sft_half = sft_half
-
-    def forward(
-        self,
-        styles,
-        conditions,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2GeneratorCSFT.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ResBlock(nn.Module):
-    """Residual block with bilinear upsampling/downsampling.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.
-    """
-
-    def __init__(self, in_channels, out_channels, mode="down"):
-        super(ResBlock, self).__init__()
-
-        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
-        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
-        self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
-        if mode == "down":
-            self.scale_factor = 0.5
-        elif mode == "up":
-            self.scale_factor = 2
-
-    def forward(self, x):
-        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
-        # upsample/downsample
-        out = F.interpolate(
-            out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
-        )
-        out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
-        # skip
-        x = F.interpolate(
-            x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
-        )
-        skip = self.skip(x)
-        out = out + skip
-        return out
-
-
-class GFPGANv1Clean(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        state_dict,
-    ):
-        super(GFPGANv1Clean, self).__init__()
-
-        out_size = 512
-        num_style_feat = 512
-        channel_multiplier = 2
-        decoder_load_path = None
-        fix_decoder = False
-        num_mlp = 8
-        input_is_latent = True
-        different_w = True
-        narrow = 1
-        sft_half = True
-
-        self.model_arch = "GFPGAN"
-        self.sub_type = "Face SR"
-        self.scale = 8
-        self.in_nc = 3
-        self.out_nc = 3
-        self.state = state_dict
-
-        self.supports_fp16 = False
-        self.supports_bf16 = True
-        self.min_size_restriction = 512
-
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            "4": int(512 * unet_narrow),
-            "8": int(512 * unet_narrow),
-            "16": int(512 * unet_narrow),
-            "32": int(512 * unet_narrow),
-            "64": int(256 * channel_multiplier * unet_narrow),
-            "128": int(128 * channel_multiplier * unet_narrow),
-            "256": int(64 * channel_multiplier * unet_narrow),
-            "512": int(32 * channel_multiplier * unet_narrow),
-            "1024": int(16 * channel_multiplier * unet_narrow),
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2 ** (int(math.log(out_size, 2)))
-
-        self.conv_body_first = nn.Conv2d(3, channels[f"{first_out_size}"], 1)
-
-        # downsample
-        in_channels = channels[f"{first_out_size}"]
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f"{2**(i - 1)}"]
-            self.conv_body_down.append(ResBlock(in_channels, out_channels, mode="down"))
-            in_channels = out_channels
-
-        self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1)
-
-        # upsample
-        in_channels = channels["4"]
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.conv_body_up.append(ResBlock(in_channels, out_channels, mode="up"))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1))
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = nn.Linear(channels["4"] * 4 * 4, linear_out_channel)
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorCSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            narrow=narrow,
-            sft_half=sft_half,
-        )
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(
-                    decoder_load_path, map_location=lambda storage, loc: storage
-                )["params_ema"]
-            )
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    nn.Conv2d(out_channels, out_channels, 3, 1, 1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),
-                )
-            )
-            self.condition_shift.append(
-                nn.Sequential(
-                    nn.Conv2d(out_channels, out_channels, 3, 1, 1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),
-                )
-            )
-        self.load_state_dict(state_dict)
-
-    def forward(
-        self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs
-    ):
-        """Forward function for GFPGANv1Clean.
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-        feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder(
-            [style_code],
-            conditions,
-            return_latents=return_latents,
-            input_is_latent=self.input_is_latent,
-            randomize_noise=randomize_noise,
-        )
-
-        return image, out_rgbs
diff --git a/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py b/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py
deleted file mode 100644
index 449226029..000000000
--- a/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py
+++ /dev/null
@@ -1,776 +0,0 @@
-# pylint: skip-file
-# type: ignore
-"""Modified from https://github.com/wzhouxiff/RestoreFormer
-"""
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class VectorQuantizer(nn.Module):
-    """
-    see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
-    ____________________________________________
-    Discretization bottleneck part of the VQ-VAE.
-    Inputs:
-    - n_e : number of embeddings
-    - e_dim : dimension of embedding
-    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-    _____________________________________________
-    """
-
-    def __init__(self, n_e, e_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.n_e = n_e
-        self.e_dim = e_dim
-        self.beta = beta
-
-        self.embedding = nn.Embedding(self.n_e, self.e_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
-    def forward(self, z):
-        """
-        Inputs the output of the encoder network z and maps it to a discrete
-        one-hot vector that is the index of the closest embedding vector e_j
-        z (continuous) -> z_q (discrete)
-        z.shape = (batch, channel, height, width)
-        quantization pipeline:
-            1. get encoder input (B,C,H,W)
-            2. flatten input to (B*H*W,C)
-        """
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.e_dim)
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
-        d = (
-            torch.sum(z_flattened**2, dim=1, keepdim=True)
-            + torch.sum(self.embedding.weight**2, dim=1)
-            - 2 * torch.matmul(z_flattened, self.embedding.weight.t())
-        )
-
-        # could possible replace this here
-        # #\start...
-        # find closest encodings
-
-        min_value, min_encoding_indices = torch.min(d, dim=1)
-
-        min_encoding_indices = min_encoding_indices.unsqueeze(1)
-
-        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # dtype min encodings: torch.float32
-        # min_encodings shape: torch.Size([2048, 512])
-        # min_encoding_indices.shape: torch.Size([2048, 1])
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        # .........\end
-
-        # with:
-        # .........\start
-        # min_encoding_indices = torch.argmin(d, dim=1)
-        # z_q = self.embedding(min_encoding_indices)
-        # ......\end......... (TODO)
-
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
-            (z_q - z.detach()) ** 2
-        )
-
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d)
-
-    def get_codebook_entry(self, indices, shape):
-        # shape specifying (batch, height, width, channel)
-        # TODO: check for more easy handling with nn.Embedding
-        min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
-        min_encodings.scatter_(1, indices[:, None], 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:
-            z_q = z_q.view(shape)
-
-            # reshape back to match original input shape
-            z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-# pytorch_diffusion + derived encoder decoder
-def nonlinearity(x):
-    # swish
-    return x * torch.sigmoid(x)
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(
-        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
-    )
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(
-                in_channels, in_channels, kernel_size=3, stride=1, padding=1
-            )
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(
-                in_channels, in_channels, kernel_size=3, stride=2, padding=0
-            )
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0, 1, 0, 1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(
-        self,
-        *,
-        in_channels,
-        out_channels=None,
-        conv_shortcut=False,
-        dropout,
-        temb_channels=512
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(
-            in_channels, out_channels, kernel_size=3, stride=1, padding=1
-        )
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(
-            out_channels, out_channels, kernel_size=3, stride=1, padding=1
-        )
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(
-                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
-                )
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(
-                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
-                )
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x + h
-
-
-class MultiHeadAttnBlock(nn.Module):
-    def __init__(self, in_channels, head_size=1):
-        super().__init__()
-        self.in_channels = in_channels
-        self.head_size = head_size
-        self.att_size = in_channels // head_size
-        assert (
-            in_channels % head_size == 0
-        ), "The size of head should be divided by the number of channels."
-
-        self.norm1 = Normalize(in_channels)
-        self.norm2 = Normalize(in_channels)
-
-        self.q = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.num = 0
-
-    def forward(self, x, y=None):
-        h_ = x
-        h_ = self.norm1(h_)
-        if y is None:
-            y = h_
-        else:
-            y = self.norm2(y)
-
-        q = self.q(y)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, self.head_size, self.att_size, h * w)
-        q = q.permute(0, 3, 1, 2)  # b, hw, head, att
-
-        k = k.reshape(b, self.head_size, self.att_size, h * w)
-        k = k.permute(0, 3, 1, 2)
-
-        v = v.reshape(b, self.head_size, self.att_size, h * w)
-        v = v.permute(0, 3, 1, 2)
-
-        q = q.transpose(1, 2)
-        v = v.transpose(1, 2)
-        k = k.transpose(1, 2).transpose(2, 3)
-
-        scale = int(self.att_size) ** (-0.5)
-        q.mul_(scale)
-        w_ = torch.matmul(q, k)
-        w_ = F.softmax(w_, dim=3)
-
-        w_ = w_.matmul(v)
-
-        w_ = w_.transpose(1, 2).contiguous()  # [b, h*w, head, att]
-        w_ = w_.view(b, h, w, -1)
-        w_ = w_.permute(0, 3, 1, 2)
-
-        w_ = self.proj_out(w_)
-
-        return x + w_
-
-
-class MultiHeadEncoder(nn.Module):
-    def __init__(
-        self,
-        ch,
-        out_ch,
-        ch_mult=(1, 2, 4, 8),
-        num_res_blocks=2,
-        attn_resolutions=(16,),
-        dropout=0.0,
-        resamp_with_conv=True,
-        in_channels=3,
-        resolution=512,
-        z_channels=256,
-        double_z=True,
-        enable_mid=True,
-        head_size=1,
-        **ignore_kwargs
-    ):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.enable_mid = enable_mid
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(
-            in_channels, self.ch, kernel_size=3, stride=1, padding=1
-        )
-
-        curr_res = resolution
-        in_ch_mult = (1,) + tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch * in_ch_mult[i_level]
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(
-                    ResnetBlock(
-                        in_channels=block_in,
-                        out_channels=block_out,
-                        temb_channels=self.temb_ch,
-                        dropout=dropout,
-                    )
-                )
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(MultiHeadAttnBlock(block_in, head_size))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions - 1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        if self.enable_mid:
-            self.mid = nn.Module()
-            self.mid.block_1 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
-            self.mid.block_2 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(
-            block_in,
-            2 * z_channels if double_z else z_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-        )
-
-    def forward(self, x):
-        hs = {}
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        h = self.conv_in(x)
-        hs["in"] = h
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h, temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-
-            if i_level != self.num_resolutions - 1:
-                # hs.append(h)
-                hs["block_" + str(i_level)] = h
-                h = self.down[i_level].downsample(h)
-
-        # middle
-        # h = hs[-1]
-        if self.enable_mid:
-            h = self.mid.block_1(h, temb)
-            hs["block_" + str(i_level) + "_atten"] = h
-            h = self.mid.attn_1(h)
-            h = self.mid.block_2(h, temb)
-            hs["mid_atten"] = h
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        # hs.append(h)
-        hs["out"] = h
-
-        return hs
-
-
-class MultiHeadDecoder(nn.Module):
-    def __init__(
-        self,
-        ch,
-        out_ch,
-        ch_mult=(1, 2, 4, 8),
-        num_res_blocks=2,
-        attn_resolutions=(16,),
-        dropout=0.0,
-        resamp_with_conv=True,
-        in_channels=3,
-        resolution=512,
-        z_channels=256,
-        give_pre_end=False,
-        enable_mid=True,
-        head_size=1,
-        **ignorekwargs
-    ):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.enable_mid = enable_mid
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        block_in = ch * ch_mult[self.num_resolutions - 1]
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.z_shape = (1, z_channels, curr_res, curr_res)
-        print(
-            "Working with z of shape {} = {} dimensions.".format(
-                self.z_shape, np.prod(self.z_shape)
-            )
-        )
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(
-            z_channels, block_in, kernel_size=3, stride=1, padding=1
-        )
-
-        # middle
-        if self.enable_mid:
-            self.mid = nn.Module()
-            self.mid.block_1 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
-            self.mid.block_2 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                block.append(
-                    ResnetBlock(
-                        in_channels=block_in,
-                        out_channels=block_out,
-                        temb_channels=self.temb_ch,
-                        dropout=dropout,
-                    )
-                )
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(MultiHeadAttnBlock(block_in, head_size))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(
-            block_in, out_ch, kernel_size=3, stride=1, padding=1
-        )
-
-    def forward(self, z):
-        # assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        if self.enable_mid:
-            h = self.mid.block_1(h, temb)
-            h = self.mid.attn_1(h)
-            h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class MultiHeadDecoderTransformer(nn.Module):
-    def __init__(
-        self,
-        ch,
-        out_ch,
-        ch_mult=(1, 2, 4, 8),
-        num_res_blocks=2,
-        attn_resolutions=(16,),
-        dropout=0.0,
-        resamp_with_conv=True,
-        in_channels=3,
-        resolution=512,
-        z_channels=256,
-        give_pre_end=False,
-        enable_mid=True,
-        head_size=1,
-        **ignorekwargs
-    ):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.enable_mid = enable_mid
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        block_in = ch * ch_mult[self.num_resolutions - 1]
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.z_shape = (1, z_channels, curr_res, curr_res)
-        print(
-            "Working with z of shape {} = {} dimensions.".format(
-                self.z_shape, np.prod(self.z_shape)
-            )
-        )
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(
-            z_channels, block_in, kernel_size=3, stride=1, padding=1
-        )
-
-        # middle
-        if self.enable_mid:
-            self.mid = nn.Module()
-            self.mid.block_1 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
-            self.mid.block_2 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                block.append(
-                    ResnetBlock(
-                        in_channels=block_in,
-                        out_channels=block_out,
-                        temb_channels=self.temb_ch,
-                        dropout=dropout,
-                    )
-                )
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(MultiHeadAttnBlock(block_in, head_size))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(
-            block_in, out_ch, kernel_size=3, stride=1, padding=1
-        )
-
-    def forward(self, z, hs):
-        # assert z.shape[1:] == self.z_shape[1:]
-        # self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        if self.enable_mid:
-            h = self.mid.block_1(h, temb)
-            h = self.mid.attn_1(h, hs["mid_atten"])
-            h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](
-                        h, hs["block_" + str(i_level) + "_atten"]
-                    )
-                    # hfeature = h.clone()
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class RestoreFormer(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-    ):
-        super(RestoreFormer, self).__init__()
-
-        n_embed = 1024
-        embed_dim = 256
-        ch = 64
-        out_ch = 3
-        ch_mult = (1, 2, 2, 4, 4, 8)
-        num_res_blocks = 2
-        attn_resolutions = (16,)
-        dropout = 0.0
-        in_channels = 3
-        resolution = 512
-        z_channels = 256
-        double_z = False
-        enable_mid = True
-        fix_decoder = False
-        fix_codebook = True
-        fix_encoder = False
-        head_size = 8
-
-        self.model_arch = "RestoreFormer"
-        self.sub_type = "Face SR"
-        self.scale = 8
-        self.in_nc = 3
-        self.out_nc = out_ch
-        self.state = state_dict
-
-        self.supports_fp16 = False
-        self.supports_bf16 = True
-        self.min_size_restriction = 16
-
-        self.encoder = MultiHeadEncoder(
-            ch=ch,
-            out_ch=out_ch,
-            ch_mult=ch_mult,
-            num_res_blocks=num_res_blocks,
-            attn_resolutions=attn_resolutions,
-            dropout=dropout,
-            in_channels=in_channels,
-            resolution=resolution,
-            z_channels=z_channels,
-            double_z=double_z,
-            enable_mid=enable_mid,
-            head_size=head_size,
-        )
-        self.decoder = MultiHeadDecoderTransformer(
-            ch=ch,
-            out_ch=out_ch,
-            ch_mult=ch_mult,
-            num_res_blocks=num_res_blocks,
-            attn_resolutions=attn_resolutions,
-            dropout=dropout,
-            in_channels=in_channels,
-            resolution=resolution,
-            z_channels=z_channels,
-            enable_mid=enable_mid,
-            head_size=head_size,
-        )
-
-        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25)
-
-        self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1)
-
-        if fix_decoder:
-            for _, param in self.decoder.named_parameters():
-                param.requires_grad = False
-            for _, param in self.post_quant_conv.named_parameters():
-                param.requires_grad = False
-            for _, param in self.quantize.named_parameters():
-                param.requires_grad = False
-        elif fix_codebook:
-            for _, param in self.quantize.named_parameters():
-                param.requires_grad = False
-
-        if fix_encoder:
-            for _, param in self.encoder.named_parameters():
-                param.requires_grad = False
-
-        self.load_state_dict(state_dict)
-
-    def encode(self, x):
-        hs = self.encoder(x)
-        h = self.quant_conv(hs["out"])
-        quant, emb_loss, info = self.quantize(h)
-        return quant, emb_loss, info, hs
-
-    def decode(self, quant, hs):
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant, hs)
-
-        return dec
-
-    def forward(self, input, **kwargs):
-        quant, diff, info, hs = self.encode(input)
-        dec = self.decode(quant, hs)
-
-        return dec, None
diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py
deleted file mode 100644
index 1eb0e9f15..000000000
--- a/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py
+++ /dev/null
@@ -1,865 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
-
-
-class NormStyleCode(nn.Module):
-    def forward(self, x):
-        """Normalize the style codes.
-
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_resample_kernel(k):
-    """Make resampling kernel for UpFirDn.
-
-    Args:
-        k (list[int]): A list indicating the 1D resample kernel magnitude.
-
-    Returns:
-        Tensor: 2D resampled kernel.
-    """
-    k = torch.tensor(k, dtype=torch.float32)
-    if k.ndim == 1:
-        k = k[None, :] * k[:, None]  # to 2D kernel, outer product
-    # normalize
-    k /= k.sum()
-    return k
-
-
-class UpFirDnUpsample(nn.Module):
-    """Upsample, FIR filter, and downsample (upsampole version).
-
-    References:
-    1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html  # noqa: E501
-    2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html  # noqa: E501
-
-    Args:
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude.
-        factor (int): Upsampling scale factor. Default: 2.
-    """
-
-    def __init__(self, resample_kernel, factor=2):
-        super(UpFirDnUpsample, self).__init__()
-        self.kernel = make_resample_kernel(resample_kernel) * (factor**2)
-        self.factor = factor
-
-        pad = self.kernel.shape[0] - factor
-        self.pad = ((pad + 1) // 2 + factor - 1, pad // 2)
-
-    def forward(self, x):
-        out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad)
-        return out
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}(factor={self.factor})"
-
-
-class UpFirDnDownsample(nn.Module):
-    """Upsample, FIR filter, and downsample (downsampole version).
-
-    Args:
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude.
-        factor (int): Downsampling scale factor. Default: 2.
-    """
-
-    def __init__(self, resample_kernel, factor=2):
-        super(UpFirDnDownsample, self).__init__()
-        self.kernel = make_resample_kernel(resample_kernel)
-        self.factor = factor
-
-        pad = self.kernel.shape[0] - factor
-        self.pad = ((pad + 1) // 2, pad // 2)
-
-    def forward(self, x):
-        out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad)
-        return out
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}(factor={self.factor})"
-
-
-class UpFirDnSmooth(nn.Module):
-    """Upsample, FIR filter, and downsample (smooth version).
-
-    Args:
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude.
-        upsample_factor (int): Upsampling scale factor. Default: 1.
-        downsample_factor (int): Downsampling scale factor. Default: 1.
-        kernel_size (int): Kernel size: Default: 1.
-    """
-
-    def __init__(
-        self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1
-    ):
-        super(UpFirDnSmooth, self).__init__()
-        self.upsample_factor = upsample_factor
-        self.downsample_factor = downsample_factor
-        self.kernel = make_resample_kernel(resample_kernel)
-        if upsample_factor > 1:
-            self.kernel = self.kernel * (upsample_factor**2)
-
-        if upsample_factor > 1:
-            pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1)
-            self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1)
-        elif downsample_factor > 1:
-            pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1)
-            self.pad = ((pad + 1) // 2, pad // 2)
-        else:
-            raise NotImplementedError
-
-    def forward(self, x):
-        out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad)
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(upsample_factor={self.upsample_factor}"
-            f", downsample_factor={self.downsample_factor})"
-        )
-
-
-class EqualLinear(nn.Module):
-    """Equalized Linear as StyleGAN2.
-
-    Args:
-        in_channels (int): Size of each sample.
-        out_channels (int): Size of each output sample.
-        bias (bool): If set to ``False``, the layer will not learn an additive
-            bias. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        lr_mul (float): Learning rate multiplier. Default: 1.
-        activation (None | str): The activation after ``linear`` operation.
-            Supported: 'fused_lrelu', None. Default: None.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        bias=True,
-        bias_init_val=0,
-        lr_mul=1,
-        activation=None,
-    ):
-        super(EqualLinear, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.lr_mul = lr_mul
-        self.activation = activation
-        if self.activation not in ["fused_lrelu", None]:
-            raise ValueError(
-                f"Wrong activation value in EqualLinear: {activation}"
-                "Supported ones are: ['fused_lrelu', None]."
-            )
-        self.scale = (1 / math.sqrt(in_channels)) * lr_mul
-
-        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul))
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x):
-        if self.bias is None:
-            bias = None
-        else:
-            bias = self.bias * self.lr_mul
-        if self.activation == "fused_lrelu":
-            out = F.linear(x, self.weight * self.scale)
-            out = fused_leaky_relu(out, bias)
-        else:
-            out = F.linear(x, self.weight * self.scale, bias=bias)
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, bias={self.bias is not None})"
-        )
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-
-    There is no bias in ModulatedConv2d.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer.
-            Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
-            Default: None.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude. Default: (1, 3, 3, 1).
-        eps (float): A value added to the denominator for numerical stability.
-            Default: 1e-8.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        resample_kernel=(1, 3, 3, 1),
-        eps=1e-8,
-    ):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-
-        if self.sample_mode == "upsample":
-            self.smooth = UpFirDnSmooth(
-                resample_kernel,
-                upsample_factor=2,
-                downsample_factor=1,
-                kernel_size=kernel_size,
-            )
-        elif self.sample_mode == "downsample":
-            self.smooth = UpFirDnSmooth(
-                resample_kernel,
-                upsample_factor=1,
-                downsample_factor=2,
-                kernel_size=kernel_size,
-            )
-        elif self.sample_mode is None:
-            pass
-        else:
-            raise ValueError(
-                f"Wrong sample mode {self.sample_mode}, "
-                "supported ones are ['upsample', 'downsample', None]."
-            )
-
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-        # modulation inside each modulated conv
-        self.modulation = EqualLinear(
-            num_style_feat,
-            in_channels,
-            bias=True,
-            bias_init_val=1,
-            lr_mul=1,
-            activation=None,
-        )
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)
-        )
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.scale * self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(
-            b * self.out_channels, c, self.kernel_size, self.kernel_size
-        )
-
-        if self.sample_mode == "upsample":
-            x = x.view(1, b * c, h, w)
-            weight = weight.view(
-                b, self.out_channels, c, self.kernel_size, self.kernel_size
-            )
-            weight = weight.transpose(1, 2).reshape(
-                b * c, self.out_channels, self.kernel_size, self.kernel_size
-            )
-            out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b)
-            out = out.view(b, self.out_channels, *out.shape[2:4])
-            out = self.smooth(out)
-        elif self.sample_mode == "downsample":
-            x = self.smooth(x)
-            x = x.view(1, b * c, *x.shape[2:4])
-            out = F.conv2d(x, weight, padding=0, stride=2, groups=b)
-            out = out.view(b, self.out_channels, *out.shape[2:4])
-        else:
-            x = x.view(1, b * c, h, w)
-            # weight: (b*c_out, c_in, k, k), groups=b
-            out = F.conv2d(x, weight, padding=self.padding, groups=b)
-            out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size}, "
-            f"demodulate={self.demodulate}, sample_mode={self.sample_mode})"
-        )
-
-
-class StyleConv(nn.Module):
-    """Style conv.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
-            Default: None.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude. Default: (1, 3, 3, 1).
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        resample_kernel=(1, 3, 3, 1),
-    ):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            num_style_feat,
-            demodulate=demodulate,
-            sample_mode=sample_mode,
-            resample_kernel=resample_kernel,
-        )
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.activate = FusedLeakyReLU(out_channels)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style)
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # activation (with bias)
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB from features.
-
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude. Default: (1, 3, 3, 1).
-    """
-
-    def __init__(
-        self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)
-    ):
-        super(ToRGB, self).__init__()
-        if upsample:
-            self.upsample = UpFirDnUpsample(resample_kernel, factor=2)
-        else:
-            self.upsample = None
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            3,
-            kernel_size=1,
-            num_style_feat=num_style_feat,
-            demodulate=False,
-            sample_mode=None,
-        )
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = self.upsample(skip)
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-class StyleGAN2Generator(nn.Module):
-    """StyleGAN2 Generator.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of
-            StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude. A cross production will be applied to extent 1D resample
-            kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        resample_kernel=(1, 3, 3, 1),
-        lr_mlp=0.01,
-        narrow=1,
-    ):
-        super(StyleGAN2Generator, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.append(
-                EqualLinear(
-                    num_style_feat,
-                    num_style_feat,
-                    bias=True,
-                    bias_init_val=0,
-                    lr_mul=lr_mlp,
-                    activation="fused_lrelu",
-                )
-            )
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-
-        channels = {
-            "4": int(512 * narrow),
-            "8": int(512 * narrow),
-            "16": int(512 * narrow),
-            "32": int(512 * narrow),
-            "64": int(256 * channel_multiplier * narrow),
-            "128": int(128 * channel_multiplier * narrow),
-            "256": int(64 * channel_multiplier * narrow),
-            "512": int(32 * channel_multiplier * narrow),
-            "1024": int(16 * channel_multiplier * narrow),
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels["4"], size=4)
-        self.style_conv1 = StyleConv(
-            channels["4"],
-            channels["4"],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None,
-            resample_kernel=resample_kernel,
-        )
-        self.to_rgb1 = ToRGB(
-            channels["4"],
-            num_style_feat,
-            upsample=False,
-            resample_kernel=resample_kernel,
-        )
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels["4"]
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2 ** ((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode="upsample",
-                    resample_kernel=resample_kernel,
-                )
-            )
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None,
-                    resample_kernel=resample_kernel,
-                )
-            )
-            self.to_rgbs.append(
-                ToRGB(
-                    out_channels,
-                    num_style_feat,
-                    upsample=True,
-                    resample_kernel=resample_kernel,
-                )
-            )
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(
-            num_latent, self.num_style_feat, device=self.constant_input.weight.device
-        )
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(
-        self,
-        styles,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2Generator.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style.
-                Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is
-                False. Default: True.
-            truncation (float): TODO. Default: 1.
-            truncation_latent (Tensor | None): TODO. Default: None.
-            inject_index (int | None): The injection index for mixing noise.
-                Default: None.
-            return_latents (bool): Whether to return style latents.
-                Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latent with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ScaledLeakyReLU(nn.Module):
-    """Scaled LeakyReLU.
-
-    Args:
-        negative_slope (float): Negative slope. Default: 0.2.
-    """
-
-    def __init__(self, negative_slope=0.2):
-        super(ScaledLeakyReLU, self).__init__()
-        self.negative_slope = negative_slope
-
-    def forward(self, x):
-        out = F.leaky_relu(x, negative_slope=self.negative_slope)
-        return out * math.sqrt(2)
-
-
-class EqualConv2d(nn.Module):
-    """Equalized Linear as StyleGAN2.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input.
-            Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output.
-            Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        bias=True,
-        bias_init_val=0,
-    ):
-        super(EqualConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channels, in_channels, kernel_size, kernel_size)
-        )
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x):
-        out = F.conv2d(
-            x,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size},"
-            f" stride={self.stride}, padding={self.padding}, "
-            f"bias={self.bias is not None})"
-        )
-
-
-class ConvLayer(nn.Sequential):
-    """Conv Layer used in StyleGAN2 Discriminator.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Kernel size.
-        downsample (bool): Whether downsample by a factor of 2.
-            Default: False.
-        resample_kernel (list[int]): A list indicating the 1D resample
-            kernel magnitude. A cross production will be applied to
-            extent 1D resample kernel to 2D resample kernel.
-            Default: (1, 3, 3, 1).
-        bias (bool): Whether with bias. Default: True.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        downsample=False,
-        resample_kernel=(1, 3, 3, 1),
-        bias=True,
-        activate=True,
-    ):
-        layers = []
-        # downsample
-        if downsample:
-            layers.append(
-                UpFirDnSmooth(
-                    resample_kernel,
-                    upsample_factor=1,
-                    downsample_factor=2,
-                    kernel_size=kernel_size,
-                )
-            )
-            stride = 2
-            self.padding = 0
-        else:
-            stride = 1
-            self.padding = kernel_size // 2
-        # conv
-        layers.append(
-            EqualConv2d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                stride=stride,
-                padding=self.padding,
-                bias=bias and not activate,
-            )
-        )
-        # activation
-        if activate:
-            if bias:
-                layers.append(FusedLeakyReLU(out_channels))
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super(ConvLayer, self).__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    """Residual block used in StyleGAN2 Discriminator.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        resample_kernel (list[int]): A list indicating the 1D resample
-            kernel magnitude. A cross production will be applied to
-            extent 1D resample kernel to 2D resample kernel.
-            Default: (1, 3, 3, 1).
-    """
-
-    def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)):
-        super(ResBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvLayer(
-            in_channels,
-            out_channels,
-            3,
-            downsample=True,
-            resample_kernel=resample_kernel,
-            bias=True,
-            activate=True,
-        )
-        self.skip = ConvLayer(
-            in_channels,
-            out_channels,
-            1,
-            downsample=True,
-            resample_kernel=resample_kernel,
-            bias=False,
-            activate=False,
-        )
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py
deleted file mode 100644
index 601f8cc4b..000000000
--- a/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py
+++ /dev/null
@@ -1,709 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-
-
-class NormStyleCode(nn.Module):
-    def forward(self, x):
-        """Normalize the style codes.
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-class EqualLinear(nn.Module):
-    """Equalized Linear as StyleGAN2.
-    Args:
-        in_channels (int): Size of each sample.
-        out_channels (int): Size of each output sample.
-        bias (bool): If set to ``False``, the layer will not learn an additive
-            bias. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        lr_mul (float): Learning rate multiplier. Default: 1.
-        activation (None | str): The activation after ``linear`` operation.
-            Supported: 'fused_lrelu', None. Default: None.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        bias=True,
-        bias_init_val=0,
-        lr_mul=1,
-        activation=None,
-    ):
-        super(EqualLinear, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.lr_mul = lr_mul
-        self.activation = activation
-        if self.activation not in ["fused_lrelu", None]:
-            raise ValueError(
-                f"Wrong activation value in EqualLinear: {activation}"
-                "Supported ones are: ['fused_lrelu', None]."
-            )
-        self.scale = (1 / math.sqrt(in_channels)) * lr_mul
-
-        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul))
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x):
-        if self.bias is None:
-            bias = None
-        else:
-            bias = self.bias * self.lr_mul
-        if self.activation == "fused_lrelu":
-            out = F.linear(x, self.weight * self.scale)
-            out = fused_leaky_relu(out, bias)
-        else:
-            out = F.linear(x, self.weight * self.scale, bias=bias)
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, bias={self.bias is not None})"
-        )
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-    There is no bias in ModulatedConv2d.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer.
-            Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
-            Default: None.
-        eps (float): A value added to the denominator for numerical stability.
-            Default: 1e-8.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        eps=1e-8,
-        interpolation_mode="bilinear",
-    ):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-        self.interpolation_mode = interpolation_mode
-        if self.interpolation_mode == "nearest":
-            self.align_corners = None
-        else:
-            self.align_corners = False
-
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-        # modulation inside each modulated conv
-        self.modulation = EqualLinear(
-            num_style_feat,
-            in_channels,
-            bias=True,
-            bias_init_val=1,
-            lr_mul=1,
-            activation=None,
-        )
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)
-        )
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.scale * self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(
-            b * self.out_channels, c, self.kernel_size, self.kernel_size
-        )
-
-        if self.sample_mode == "upsample":
-            x = F.interpolate(
-                x,
-                scale_factor=2,
-                mode=self.interpolation_mode,
-                align_corners=self.align_corners,
-            )
-        elif self.sample_mode == "downsample":
-            x = F.interpolate(
-                x,
-                scale_factor=0.5,
-                mode=self.interpolation_mode,
-                align_corners=self.align_corners,
-            )
-
-        b, c, h, w = x.shape
-        x = x.view(1, b * c, h, w)
-        # weight: (b*c_out, c_in, k, k), groups=b
-        out = F.conv2d(x, weight, padding=self.padding, groups=b)
-        out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size}, "
-            f"demodulate={self.demodulate}, sample_mode={self.sample_mode})"
-        )
-
-
-class StyleConv(nn.Module):
-    """Style conv.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
-            Default: None.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        interpolation_mode="bilinear",
-    ):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            num_style_feat,
-            demodulate=demodulate,
-            sample_mode=sample_mode,
-            interpolation_mode=interpolation_mode,
-        )
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.activate = FusedLeakyReLU(out_channels)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style)
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # activation (with bias)
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB from features.
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-    """
-
-    def __init__(
-        self, in_channels, num_style_feat, upsample=True, interpolation_mode="bilinear"
-    ):
-        super(ToRGB, self).__init__()
-        self.upsample = upsample
-        self.interpolation_mode = interpolation_mode
-        if self.interpolation_mode == "nearest":
-            self.align_corners = None
-        else:
-            self.align_corners = False
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            3,
-            kernel_size=1,
-            num_style_feat=num_style_feat,
-            demodulate=False,
-            sample_mode=None,
-            interpolation_mode=interpolation_mode,
-        )
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = F.interpolate(
-                    skip,
-                    scale_factor=2,
-                    mode=self.interpolation_mode,
-                    align_corners=self.align_corners,
-                )
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-class StyleGAN2GeneratorBilinear(nn.Module):
-    """StyleGAN2 Generator.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of
-            StyleGAN2. Default: 2.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        lr_mlp=0.01,
-        narrow=1,
-        interpolation_mode="bilinear",
-    ):
-        super(StyleGAN2GeneratorBilinear, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.append(
-                EqualLinear(
-                    num_style_feat,
-                    num_style_feat,
-                    bias=True,
-                    bias_init_val=0,
-                    lr_mul=lr_mlp,
-                    activation="fused_lrelu",
-                )
-            )
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-
-        channels = {
-            "4": int(512 * narrow),
-            "8": int(512 * narrow),
-            "16": int(512 * narrow),
-            "32": int(512 * narrow),
-            "64": int(256 * channel_multiplier * narrow),
-            "128": int(128 * channel_multiplier * narrow),
-            "256": int(64 * channel_multiplier * narrow),
-            "512": int(32 * channel_multiplier * narrow),
-            "1024": int(16 * channel_multiplier * narrow),
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels["4"], size=4)
-        self.style_conv1 = StyleConv(
-            channels["4"],
-            channels["4"],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None,
-            interpolation_mode=interpolation_mode,
-        )
-        self.to_rgb1 = ToRGB(
-            channels["4"],
-            num_style_feat,
-            upsample=False,
-            interpolation_mode=interpolation_mode,
-        )
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels["4"]
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2 ** ((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode="upsample",
-                    interpolation_mode=interpolation_mode,
-                )
-            )
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None,
-                    interpolation_mode=interpolation_mode,
-                )
-            )
-            self.to_rgbs.append(
-                ToRGB(
-                    out_channels,
-                    num_style_feat,
-                    upsample=True,
-                    interpolation_mode=interpolation_mode,
-                )
-            )
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(
-            num_latent, self.num_style_feat, device=self.constant_input.weight.device
-        )
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(
-        self,
-        styles,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2Generator.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style.
-                Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is
-                False. Default: True.
-            truncation (float): TODO. Default: 1.
-            truncation_latent (Tensor | None): TODO. Default: None.
-            inject_index (int | None): The injection index for mixing noise.
-                Default: None.
-            return_latents (bool): Whether to return style latents.
-                Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latent with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ScaledLeakyReLU(nn.Module):
-    """Scaled LeakyReLU.
-    Args:
-        negative_slope (float): Negative slope. Default: 0.2.
-    """
-
-    def __init__(self, negative_slope=0.2):
-        super(ScaledLeakyReLU, self).__init__()
-        self.negative_slope = negative_slope
-
-    def forward(self, x):
-        out = F.leaky_relu(x, negative_slope=self.negative_slope)
-        return out * math.sqrt(2)
-
-
-class EqualConv2d(nn.Module):
-    """Equalized Linear as StyleGAN2.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input.
-            Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output.
-            Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        bias=True,
-        bias_init_val=0,
-    ):
-        super(EqualConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channels, in_channels, kernel_size, kernel_size)
-        )
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x):
-        out = F.conv2d(
-            x,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size},"
-            f" stride={self.stride}, padding={self.padding}, "
-            f"bias={self.bias is not None})"
-        )
-
-
-class ConvLayer(nn.Sequential):
-    """Conv Layer used in StyleGAN2 Discriminator.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Kernel size.
-        downsample (bool): Whether downsample by a factor of 2.
-            Default: False.
-        bias (bool): Whether with bias. Default: True.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        downsample=False,
-        bias=True,
-        activate=True,
-        interpolation_mode="bilinear",
-    ):
-        layers = []
-        self.interpolation_mode = interpolation_mode
-        # downsample
-        if downsample:
-            if self.interpolation_mode == "nearest":
-                self.align_corners = None
-            else:
-                self.align_corners = False
-
-            layers.append(
-                torch.nn.Upsample(
-                    scale_factor=0.5,
-                    mode=interpolation_mode,
-                    align_corners=self.align_corners,
-                )
-            )
-        stride = 1
-        self.padding = kernel_size // 2
-        # conv
-        layers.append(
-            EqualConv2d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                stride=stride,
-                padding=self.padding,
-                bias=bias and not activate,
-            )
-        )
-        # activation
-        if activate:
-            if bias:
-                layers.append(FusedLeakyReLU(out_channels))
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super(ConvLayer, self).__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    """Residual block used in StyleGAN2 Discriminator.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-    """
-
-    def __init__(self, in_channels, out_channels, interpolation_mode="bilinear"):
-        super(ResBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvLayer(
-            in_channels,
-            out_channels,
-            3,
-            downsample=True,
-            interpolation_mode=interpolation_mode,
-            bias=True,
-            activate=True,
-        )
-        self.skip = ConvLayer(
-            in_channels,
-            out_channels,
-            1,
-            downsample=True,
-            interpolation_mode=interpolation_mode,
-            bias=False,
-            activate=False,
-        )
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py
deleted file mode 100644
index c48de9af6..000000000
--- a/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py
+++ /dev/null
@@ -1,453 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn import init
-from torch.nn.modules.batchnorm import _BatchNorm
-
-
-@torch.no_grad()
-def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
-    """Initialize network weights.
-    Args:
-        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
-        scale (float): Scale initialized weights, especially for residual
-            blocks. Default: 1.
-        bias_fill (float): The value to fill bias. Default: 0
-        kwargs (dict): Other arguments for initialization function.
-    """
-    if not isinstance(module_list, list):
-        module_list = [module_list]
-    for module in module_list:
-        for m in module.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, _BatchNorm):
-                init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-
-
-class NormStyleCode(nn.Module):
-    def forward(self, x):
-        """Normalize the style codes.
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-    There is no bias in ModulatedConv2d.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-        eps (float): A value added to the denominator for numerical stability. Default: 1e-8.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        eps=1e-8,
-    ):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-
-        # modulation inside each modulated conv
-        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
-        # initialization
-        default_init_weights(
-            self.modulation,
-            scale=1,
-            bias_fill=1,
-            a=0,
-            mode="fan_in",
-            nonlinearity="linear",
-        )
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)
-            / math.sqrt(in_channels * kernel_size**2)
-        )
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(
-            b * self.out_channels, c, self.kernel_size, self.kernel_size
-        )
-
-        # upsample or downsample if necessary
-        if self.sample_mode == "upsample":
-            x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False)
-        elif self.sample_mode == "downsample":
-            x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False)
-
-        b, c, h, w = x.shape
-        x = x.view(1, b * c, h, w)
-        # weight: (b*c_out, c_in, k, k), groups=b
-        out = F.conv2d(x, weight, padding=self.padding, groups=b)
-        out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})"
-        )
-
-
-class StyleConv(nn.Module):
-    """Style conv used in StyleGAN2.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-    ):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            num_style_feat,
-            demodulate=demodulate,
-            sample_mode=sample_mode,
-        )
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
-        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style) * 2**0.5  # for conversion
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # add bias
-        out = out + self.bias
-        # activation
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB (image space) from features.
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-    """
-
-    def __init__(self, in_channels, num_style_feat, upsample=True):
-        super(ToRGB, self).__init__()
-        self.upsample = upsample
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            3,
-            kernel_size=1,
-            num_style_feat=num_style_feat,
-            demodulate=False,
-            sample_mode=None,
-        )
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = F.interpolate(
-                    skip, scale_factor=2, mode="bilinear", align_corners=False
-                )
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-class StyleGAN2GeneratorClean(nn.Module):
-    """Clean version of StyleGAN2 Generator.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(
-        self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1
-    ):
-        super(StyleGAN2GeneratorClean, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.extend(
-                [
-                    nn.Linear(num_style_feat, num_style_feat, bias=True),
-                    nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                ]
-            )
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-        # initialization
-        default_init_weights(
-            self.style_mlp,
-            scale=1,
-            bias_fill=0,
-            a=0.2,
-            mode="fan_in",
-            nonlinearity="leaky_relu",
-        )
-
-        # channel list
-        channels = {
-            "4": int(512 * narrow),
-            "8": int(512 * narrow),
-            "16": int(512 * narrow),
-            "32": int(512 * narrow),
-            "64": int(256 * channel_multiplier * narrow),
-            "128": int(128 * channel_multiplier * narrow),
-            "256": int(64 * channel_multiplier * narrow),
-            "512": int(32 * channel_multiplier * narrow),
-            "1024": int(16 * channel_multiplier * narrow),
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels["4"], size=4)
-        self.style_conv1 = StyleConv(
-            channels["4"],
-            channels["4"],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None,
-        )
-        self.to_rgb1 = ToRGB(channels["4"], num_style_feat, upsample=False)
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels["4"]
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2 ** ((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode="upsample",
-                )
-            )
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None,
-                )
-            )
-            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(
-            num_latent, self.num_style_feat, device=self.constant_input.weight.device
-        )
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(
-        self,
-        styles,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2GeneratorClean.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
diff --git a/comfy_extras/chainner_models/architecture/face/upfirdn2d.py b/comfy_extras/chainner_models/architecture/face/upfirdn2d.py
deleted file mode 100644
index 4ea454151..000000000
--- a/comfy_extras/chainner_models/architecture/face/upfirdn2d.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# pylint: skip-file
-# type: ignore
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501
-
-import os
-
-import torch
-from torch.autograd import Function
-from torch.nn import functional as F
-
-upfirdn2d_ext = None
-
-
-class UpFirDn2dBackward(Function):
-    @staticmethod
-    def forward(
-        ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
-    ):
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_ext.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            down_x,
-            down_y,
-            up_x,
-            up_y,
-            g_pad_x0,
-            g_pad_x1,
-            g_pad_y0,
-            g_pad_y1,
-        )
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        (kernel,) = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_ext.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            ctx.up_x,
-            ctx.up_y,
-            ctx.down_x,
-            ctx.down_y,
-            ctx.pad_x0,
-            ctx.pad_x1,
-            ctx.pad_y0,
-            ctx.pad_y1,
-        )
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
-        #                                  ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(
-            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
-        )
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        _, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_ext.upfirdn2d(
-            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-        )
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    if input.device.type == "cpu":
-        out = upfirdn2d_native(
-            input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
-        )
-    else:
-        out = UpFirDn2d.apply(
-            input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
-        )
-
-    return out
-
-
-def upfirdn2d_native(
-    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
-    )
-    out = out[
-        :,
-        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
-        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
-        :,
-    ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
-    )
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/comfy_extras/chainner_models/architecture/timm/LICENSE b/comfy_extras/chainner_models/architecture/timm/LICENSE
deleted file mode 100644
index b4e9438bd..000000000
--- a/comfy_extras/chainner_models/architecture/timm/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
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-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
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-      direction or management of such entity, whether by contract or
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-      outstanding shares, or (iii) beneficial ownership of such entity.
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-      transformation or translation of a Source form, including but
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-   5. Submission of Contributions. Unless You explicitly state otherwise,
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-   6. Trademarks. This License does not grant permission to use the trade
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-      except as required for reasonable and customary use in describing the
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-   7. Disclaimer of Warranty. Unless required by applicable law or
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
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-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "{}"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
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-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
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-
-   Copyright 2019 Ross Wightman
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
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diff --git a/comfy_extras/chainner_models/architecture/timm/drop.py b/comfy_extras/chainner_models/architecture/timm/drop.py
deleted file mode 100644
index 14f0da914..000000000
--- a/comfy_extras/chainner_models/architecture/timm/drop.py
+++ /dev/null
@@ -1,223 +0,0 @@
-""" DropBlock, DropPath
-
-PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
-
-Papers:
-DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
-
-Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
-
-Code:
-DropBlock impl inspired by two Tensorflow impl that I liked:
- - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
- - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def drop_block_2d(
-    x,
-    drop_prob: float = 0.1,
-    block_size: int = 7,
-    gamma_scale: float = 1.0,
-    with_noise: bool = False,
-    inplace: bool = False,
-    batchwise: bool = False,
-):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-
-    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
-    runs with success, but needs further validation and possibly optimization for lower runtime impact.
-    """
-    _, C, H, W = x.shape
-    total_size = W * H
-    clipped_block_size = min(block_size, min(W, H))
-    # seed_drop_rate, the gamma parameter
-    gamma = (
-        gamma_scale
-        * drop_prob
-        * total_size
-        / clipped_block_size**2
-        / ((W - block_size + 1) * (H - block_size + 1))
-    )
-
-    # Forces the block to be inside the feature map.
-    w_i, h_i = torch.meshgrid(
-        torch.arange(W).to(x.device), torch.arange(H).to(x.device)
-    )
-    valid_block = (
-        (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)
-    ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
-    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
-
-    if batchwise:
-        # one mask for whole batch, quite a bit faster
-        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
-    else:
-        uniform_noise = torch.rand_like(x)
-    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
-    block_mask = -F.max_pool2d(
-        -block_mask,
-        kernel_size=clipped_block_size,  # block_size,
-        stride=1,
-        padding=clipped_block_size // 2,
-    )
-
-    if with_noise:
-        normal_noise = (
-            torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)
-            if batchwise
-            else torch.randn_like(x)
-        )
-        if inplace:
-            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
-        else:
-            x = x * block_mask + normal_noise * (1 - block_mask)
-    else:
-        normalize_scale = (
-            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)
-        ).to(x.dtype)
-        if inplace:
-            x.mul_(block_mask * normalize_scale)
-        else:
-            x = x * block_mask * normalize_scale
-    return x
-
-
-def drop_block_fast_2d(
-    x: torch.Tensor,
-    drop_prob: float = 0.1,
-    block_size: int = 7,
-    gamma_scale: float = 1.0,
-    with_noise: bool = False,
-    inplace: bool = False,
-):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-
-    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
-    block mask at edges.
-    """
-    _, _, H, W = x.shape
-    total_size = W * H
-    clipped_block_size = min(block_size, min(W, H))
-    gamma = (
-        gamma_scale
-        * drop_prob
-        * total_size
-        / clipped_block_size**2
-        / ((W - block_size + 1) * (H - block_size + 1))
-    )
-
-    block_mask = torch.empty_like(x).bernoulli_(gamma)
-    block_mask = F.max_pool2d(
-        block_mask.to(x.dtype),
-        kernel_size=clipped_block_size,
-        stride=1,
-        padding=clipped_block_size // 2,
-    )
-
-    if with_noise:
-        normal_noise = torch.empty_like(x).normal_()
-        if inplace:
-            x.mul_(1.0 - block_mask).add_(normal_noise * block_mask)
-        else:
-            x = x * (1.0 - block_mask) + normal_noise * block_mask
-    else:
-        block_mask = 1 - block_mask
-        normalize_scale = (
-            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6)
-        ).to(dtype=x.dtype)
-        if inplace:
-            x.mul_(block_mask * normalize_scale)
-        else:
-            x = x * block_mask * normalize_scale
-    return x
-
-
-class DropBlock2d(nn.Module):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf"""
-
-    def __init__(
-        self,
-        drop_prob: float = 0.1,
-        block_size: int = 7,
-        gamma_scale: float = 1.0,
-        with_noise: bool = False,
-        inplace: bool = False,
-        batchwise: bool = False,
-        fast: bool = True,
-    ):
-        super(DropBlock2d, self).__init__()
-        self.drop_prob = drop_prob
-        self.gamma_scale = gamma_scale
-        self.block_size = block_size
-        self.with_noise = with_noise
-        self.inplace = inplace
-        self.batchwise = batchwise
-        self.fast = fast  # FIXME finish comparisons of fast vs not
-
-    def forward(self, x):
-        if not self.training or not self.drop_prob:
-            return x
-        if self.fast:
-            return drop_block_fast_2d(
-                x,
-                self.drop_prob,
-                self.block_size,
-                self.gamma_scale,
-                self.with_noise,
-                self.inplace,
-            )
-        else:
-            return drop_block_2d(
-                x,
-                self.drop_prob,
-                self.block_size,
-                self.gamma_scale,
-                self.with_noise,
-                self.inplace,
-                self.batchwise,
-            )
-
-
-def drop_path(
-    x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
-):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-
-    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
-    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
-    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
-    'survival rate' as the argument.
-
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (
-        x.ndim - 1
-    )  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
-    if keep_prob > 0.0 and scale_by_keep:
-        random_tensor.div_(keep_prob)
-    return x * random_tensor
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""
-
-    def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-        self.scale_by_keep = scale_by_keep
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
-
-    def extra_repr(self):
-        return f"drop_prob={round(self.drop_prob,3):0.3f}"
diff --git a/comfy_extras/chainner_models/architecture/timm/helpers.py b/comfy_extras/chainner_models/architecture/timm/helpers.py
deleted file mode 100644
index cdafee070..000000000
--- a/comfy_extras/chainner_models/architecture/timm/helpers.py
+++ /dev/null
@@ -1,31 +0,0 @@
-""" Layer/Module Helpers
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import collections.abc
-from itertools import repeat
-
-
-# From PyTorch internals
-def _ntuple(n):
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
-            return x
-        return tuple(repeat(x, n))
-
-    return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def make_divisible(v, divisor=8, min_value=None, round_limit=0.9):
-    min_value = min_value or divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_v < round_limit * v:
-        new_v += divisor
-    return new_v
diff --git a/comfy_extras/chainner_models/architecture/timm/weight_init.py b/comfy_extras/chainner_models/architecture/timm/weight_init.py
deleted file mode 100644
index b01697746..000000000
--- a/comfy_extras/chainner_models/architecture/timm/weight_init.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import math
-import warnings
-
-import torch
-from torch.nn.init import _calculate_fan_in_and_fan_out
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    # Cut & paste from PyTorch official master until it's in a few official releases - RW
-    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-            "The distribution of values may be incorrect.",
-            stacklevel=2,
-        )
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        l = norm_cdf((a - mean) / std)
-        u = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [l, u], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * l - 1, 2 * u - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.0))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(
-    tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0
-) -> torch.Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
-    applied while sampling the normal with mean/std applied, therefore a, b args
-    should be adjusted to match the range of mean, std args.
-
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-def trunc_normal_tf_(
-    tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0
-) -> torch.Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
-    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
-    and the result is subsquently scaled and shifted by the mean and std args.
-
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    _no_grad_trunc_normal_(tensor, 0, 1.0, a, b)
-    with torch.no_grad():
-        tensor.mul_(std).add_(mean)
-    return tensor
-
-
-def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
-    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
-    if mode == "fan_in":
-        denom = fan_in
-    elif mode == "fan_out":
-        denom = fan_out
-    elif mode == "fan_avg":
-        denom = (fan_in + fan_out) / 2
-
-    variance = scale / denom  # type: ignore
-
-    if distribution == "truncated_normal":
-        # constant is stddev of standard normal truncated to (-2, 2)
-        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
-    elif distribution == "normal":
-        tensor.normal_(std=math.sqrt(variance))
-    elif distribution == "uniform":
-        bound = math.sqrt(3 * variance)
-        # pylint: disable=invalid-unary-operand-type
-        tensor.uniform_(-bound, bound)
-    else:
-        raise ValueError(f"invalid distribution {distribution}")
-
-
-def lecun_normal_(tensor):
-    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py
deleted file mode 100644
index e000871c1..000000000
--- a/comfy_extras/chainner_models/model_loading.py
+++ /dev/null
@@ -1,99 +0,0 @@
-import logging as logger
-
-from .architecture.DAT import DAT
-from .architecture.face.codeformer import CodeFormer
-from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean
-from .architecture.face.restoreformer_arch import RestoreFormer
-from .architecture.HAT import HAT
-from .architecture.LaMa import LaMa
-from .architecture.OmniSR.OmniSR import OmniSR
-from .architecture.RRDB import RRDBNet as ESRGAN
-from .architecture.SCUNet import SCUNet
-from .architecture.SPSR import SPSRNet as SPSR
-from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
-from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
-from .architecture.Swin2SR import Swin2SR
-from .architecture.SwinIR import SwinIR
-from .types import PyTorchModel
-
-
-class UnsupportedModel(Exception):
-    pass
-
-
-def load_state_dict(state_dict) -> PyTorchModel:
-    logger.debug(f"Loading state dict into pytorch model arch")
-
-    state_dict_keys = list(state_dict.keys())
-
-    if "params_ema" in state_dict_keys:
-        state_dict = state_dict["params_ema"]
-    elif "params-ema" in state_dict_keys:
-        state_dict = state_dict["params-ema"]
-    elif "params" in state_dict_keys:
-        state_dict = state_dict["params"]
-
-    state_dict_keys = list(state_dict.keys())
-    # SRVGGNet Real-ESRGAN (v2)
-    if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys:
-        model = RealESRGANv2(state_dict)
-    # SPSR (ESRGAN with lots of extra layers)
-    elif "f_HR_conv1.0.weight" in state_dict:
-        model = SPSR(state_dict)
-    # Swift-SRGAN
-    elif (
-        "model" in state_dict_keys
-        and "initial.cnn.depthwise.weight" in state_dict["model"].keys()
-    ):
-        model = SwiftSRGAN(state_dict)
-    # SwinIR, Swin2SR, HAT
-    elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys:
-        if (
-            "layers.0.residual_group.blocks.0.conv_block.cab.0.weight"
-            in state_dict_keys
-        ):
-            model = HAT(state_dict)
-        elif "patch_embed.proj.weight" in state_dict_keys:
-            model = Swin2SR(state_dict)
-        else:
-            model = SwinIR(state_dict)
-    # GFPGAN
-    elif (
-        "toRGB.0.weight" in state_dict_keys
-        and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys
-    ):
-        model = GFPGANv1Clean(state_dict)
-    # RestoreFormer
-    elif (
-        "encoder.conv_in.weight" in state_dict_keys
-        and "encoder.down.0.block.0.norm1.weight" in state_dict_keys
-    ):
-        model = RestoreFormer(state_dict)
-    elif (
-        "encoder.blocks.0.weight" in state_dict_keys
-        and "quantize.embedding.weight" in state_dict_keys
-    ):
-        model = CodeFormer(state_dict)
-    # LaMa
-    elif (
-        "model.model.1.bn_l.running_mean" in state_dict_keys
-        or "generator.model.1.bn_l.running_mean" in state_dict_keys
-    ):
-        model = LaMa(state_dict)
-    # Omni-SR
-    elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys:
-        model = OmniSR(state_dict)
-    # SCUNet
-    elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys:
-        model = SCUNet(state_dict)
-    # DAT
-    elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys:
-        model = DAT(state_dict)
-    # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1
-    else:
-        try:
-            model = ESRGAN(state_dict)
-        except:
-            # pylint: disable=raise-missing-from
-            raise UnsupportedModel
-    return model
diff --git a/comfy_extras/chainner_models/types.py b/comfy_extras/chainner_models/types.py
deleted file mode 100644
index 193333b9e..000000000
--- a/comfy_extras/chainner_models/types.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from typing import Union
-
-from .architecture.DAT import DAT
-from .architecture.face.codeformer import CodeFormer
-from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean
-from .architecture.face.restoreformer_arch import RestoreFormer
-from .architecture.HAT import HAT
-from .architecture.LaMa import LaMa
-from .architecture.OmniSR.OmniSR import OmniSR
-from .architecture.RRDB import RRDBNet as ESRGAN
-from .architecture.SCUNet import SCUNet
-from .architecture.SPSR import SPSRNet as SPSR
-from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
-from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
-from .architecture.Swin2SR import Swin2SR
-from .architecture.SwinIR import SwinIR
-
-PyTorchSRModels = (
-    RealESRGANv2,
-    SPSR,
-    SwiftSRGAN,
-    ESRGAN,
-    SwinIR,
-    Swin2SR,
-    HAT,
-    OmniSR,
-    SCUNet,
-    DAT,
-)
-PyTorchSRModel = Union[
-    RealESRGANv2,
-    SPSR,
-    SwiftSRGAN,
-    ESRGAN,
-    SwinIR,
-    Swin2SR,
-    HAT,
-    OmniSR,
-    SCUNet,
-    DAT,
-]
-
-
-def is_pytorch_sr_model(model: object):
-    return isinstance(model, PyTorchSRModels)
-
-
-PyTorchFaceModels = (GFPGANv1Clean, RestoreFormer, CodeFormer)
-PyTorchFaceModel = Union[GFPGANv1Clean, RestoreFormer, CodeFormer]
-
-
-def is_pytorch_face_model(model: object):
-    return isinstance(model, PyTorchFaceModels)
-
-
-PyTorchInpaintModels = (LaMa,)
-PyTorchInpaintModel = Union[LaMa]
-
-
-def is_pytorch_inpaint_model(model: object):
-    return isinstance(model, PyTorchInpaintModels)
-
-
-PyTorchModels = (*PyTorchSRModels, *PyTorchFaceModels, *PyTorchInpaintModels)
-PyTorchModel = Union[PyTorchSRModel, PyTorchFaceModel, PyTorchInpaintModel]
-
-
-def is_pytorch_model(model: object):
-    return isinstance(model, PyTorchModels)
diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py
index 52c95df23..f0bbba766 100644
--- a/comfy_extras/nodes_upscale_model.py
+++ b/comfy_extras/nodes_upscale_model.py
@@ -1,5 +1,5 @@
 import os
-from comfy_extras.chainner_models import model_loading
+from spandrel import ModelLoader, ImageModelDescriptor
 from comfy import model_management
 import torch
 import comfy.utils
@@ -20,7 +20,11 @@ class UpscaleModelLoader:
         sd = comfy.utils.load_torch_file(model_path, safe_load=True)
         if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
             sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""})
-        out = model_loading.load_state_dict(sd).eval()
+        out = ModelLoader().load_from_state_dict(sd).eval()
+
+        if not isinstance(out, ImageModelDescriptor):
+            raise Exception("Upscale model must be a single-image model.")
+
         return (out, )
 
 
@@ -61,7 +65,7 @@ class ImageUpscaleWithModel:
                 if tile < 128:
                     raise e
 
-        upscale_model.cpu()
+        upscale_model.to("cpu")
         s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
         return (s,)
 
diff --git a/requirements.txt b/requirements.txt
index e7d8c0e9c..a1a29218c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,3 +11,4 @@ scipy
 tqdm
 psutil
 kornia>=0.7.1
+spandrel==0.3.1
\ No newline at end of file

From 9a151b7defb6bacb5ce67209e728b14922ad7454 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 26 May 2024 13:44:47 -0400
Subject: [PATCH 043/315] Fix issue and unpin spandrel package.

---
 comfy_extras/nodes_upscale_model.py | 2 +-
 requirements.txt                    | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py
index f0bbba766..03f294465 100644
--- a/comfy_extras/nodes_upscale_model.py
+++ b/comfy_extras/nodes_upscale_model.py
@@ -42,7 +42,7 @@ class ImageUpscaleWithModel:
     def upscale(self, upscale_model, image):
         device = model_management.get_torch_device()
 
-        memory_required = model_management.module_size(upscale_model)
+        memory_required = model_management.module_size(upscale_model.model)
         memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate
         memory_required += image.nelement() * image.element_size()
         model_management.free_memory(memory_required, device)
diff --git a/requirements.txt b/requirements.txt
index a1a29218c..906b96eda 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,5 +10,7 @@ Pillow
 scipy
 tqdm
 psutil
+
+#non essential dependencies:
 kornia>=0.7.1
-spandrel==0.3.1
\ No newline at end of file
+spandrel

From 16a493a19042227baadd939fc095305716ae58db Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 26 May 2024 15:37:24 -0400
Subject: [PATCH 044/315] Keep compatibility with some custom nodes.

---
 comfy_extras/chainner_models/model_loading.py | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 comfy_extras/chainner_models/model_loading.py

diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py
new file mode 100644
index 000000000..d48bc238c
--- /dev/null
+++ b/comfy_extras/chainner_models/model_loading.py
@@ -0,0 +1,5 @@
+from spandrel import ModelLoader
+
+def load_state_dict(state_dict):
+    print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
+    return ModelLoader().load_from_state_dict(state_dict).eval()

From f6a203951f061759a3d6a9847b865dd9c7d77a38 Mon Sep 17 00:00:00 2001
From: "Regis Gaughan, III" <regis.gaughan@gmail.com>
Date: Mon, 27 May 2024 14:05:51 -0400
Subject: [PATCH 045/315] Extend core snapToGrid to LiteGraph Groups. (#3393)

Extends the core Comfy.SnapToGrid behavior for nodes to apply to LiteGraph's LGraphGroup with the same behavior. Also, pulls out redundant rounding code into util function.
---
 web/extensions/core/snapToGrid.js | 96 ++++++++++++++++++++++++++++---
 1 file changed, 89 insertions(+), 7 deletions(-)

diff --git a/web/extensions/core/snapToGrid.js b/web/extensions/core/snapToGrid.js
index dc534d6ed..aac017748 100644
--- a/web/extensions/core/snapToGrid.js
+++ b/web/extensions/core/snapToGrid.js
@@ -2,6 +2,13 @@ import { app } from "../../scripts/app.js";
 
 // Shift + drag/resize to snap to grid
 
+/** Rounds a Vector2 in-place to the current CANVAS_GRID_SIZE. */
+function roundVectorToGrid(vec) {
+	vec[0] = LiteGraph.CANVAS_GRID_SIZE * Math.round(vec[0] / LiteGraph.CANVAS_GRID_SIZE);
+	vec[1] = LiteGraph.CANVAS_GRID_SIZE * Math.round(vec[1] / LiteGraph.CANVAS_GRID_SIZE);
+	return vec;
+}
+
 app.registerExtension({
 	name: "Comfy.SnapToGrid",
 	init() {
@@ -43,10 +50,7 @@ app.registerExtension({
 			const onResize = node.onResize;
 			node.onResize = function () {
 				if (app.shiftDown) {
-					const w = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.size[0] / LiteGraph.CANVAS_GRID_SIZE);
-					const h = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.size[1] / LiteGraph.CANVAS_GRID_SIZE);
-					node.size[0] = w;
-					node.size[1] = h;
+					roundVectorToGrid(node.size);
 				}
 				return onResize?.apply(this, arguments);
 			};
@@ -57,9 +61,7 @@ app.registerExtension({
 		const origDrawNode = LGraphCanvas.prototype.drawNode;
 		LGraphCanvas.prototype.drawNode = function (node, ctx) {
 			if (app.shiftDown && this.node_dragged && node.id in this.selected_nodes) {
-				const x = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[0] / LiteGraph.CANVAS_GRID_SIZE);
-				const y = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[1] / LiteGraph.CANVAS_GRID_SIZE);
-
+				const [x, y] = roundVectorToGrid([...node.pos]);
 				const shiftX = x - node.pos[0];
 				let shiftY = y - node.pos[1];
 
@@ -85,5 +87,85 @@ app.registerExtension({
 
 			return origDrawNode.apply(this, arguments);
 		};
+
+		
+		
+		/**
+  		 * The currently moving, selected group only. Set after the `selected_group` has actually started
+		 * moving.
+   		 */
+		let selectedAndMovingGroup = null;
+
+		/**
+  		 * Handles moving a group; tracking when a group has been moved (to show the ghost in `drawGroups`
+     		 * below) as well as handle the last move call from LiteGraph's `processMouseUp`.
+		 */
+		const groupMove = LGraphGroup.prototype.move;
+		LGraphGroup.prototype.move = function(deltax, deltay, ignore_nodes) {
+			const v = groupMove.apply(this, arguments);
+			// When we've started moving, set `selectedAndMovingGroup` as LiteGraph sets `selected_group`
+			// too eagerly and we don't want to behave like we're moving until we get a delta.
+			if (!selectedAndMovingGroup && app.canvas.selected_group === this && (deltax || deltay)) {
+				selectedAndMovingGroup = this;
+			}
+
+			// LiteGraph will call group.move both on mouse-move as well as mouse-up though we only want
+			// to snap on a mouse-up which we can determine by checking if `app.canvas.last_mouse_dragging`
+			// has been set to `false`. Essentially, this check here is the equivilant to calling an
+			// `LGraphGroup.prototype.onNodeMoved` if it had existed.
+			if (app.canvas.last_mouse_dragging === false && app.shiftDown) {
+				// After moving a group (while app.shiftDown), snap all the child nodes and, finally,
+				//  align the group itself.
+				this.recomputeInsideNodes();
+				for (const node of this._nodes) {
+					node.alignToGrid();
+				}
+				LGraphNode.prototype.alignToGrid.apply(this);
+			}
+			return v;
+		};
+
+		/**
+  		 * Handles drawing a group when, snapping the size when one is actively being resized tracking and/or
+     		 * drawing a ghost box when one is actively being moved. This mimics the node snapping behavior for
+		 * both.
+		 */
+		const drawGroups = LGraphCanvas.prototype.drawGroups;
+		LGraphCanvas.prototype.drawGroups = function (canvas, ctx) {
+			if (this.selected_group && app.shiftDown) {
+				if (this.selected_group_resizing) {
+					roundVectorToGrid(this.selected_group.size);
+				} else if (selectedAndMovingGroup) {
+					const [x, y] = roundVectorToGrid([...selectedAndMovingGroup.pos]);
+					const f = ctx.fillStyle;
+					const s = ctx.strokeStyle;
+					ctx.fillStyle = "rgba(100, 100, 100, 0.33)";
+					ctx.strokeStyle = "rgba(100, 100, 100, 0.66)";
+					ctx.rect(x, y, ...selectedAndMovingGroup.size);
+					ctx.fill();
+					ctx.stroke();
+					ctx.fillStyle = f;
+					ctx.strokeStyle = s;
+				}
+			} else if (!this.selected_group) {
+				selectedAndMovingGroup = null;
+			}
+			return drawGroups.apply(this, arguments);
+		};
+
+
+		/** Handles adding a group in a snapping-enabled state. */
+		const onGroupAdd = LGraphCanvas.onGroupAdd;
+		LGraphCanvas.onGroupAdd = function() {
+			const v = onGroupAdd.apply(app.canvas, arguments);
+			if (app.shiftDown) {
+				const lastGroup = app.graph._groups[app.graph._groups.length - 1];
+				if (lastGroup) {
+					roundVectorToGrid(lastGroup.pos);
+					roundVectorToGrid(lastGroup.size);
+				}
+			}
+			return v;
+		};
 	},
 });

From 34030fed925ca6e11863a0c2f85d84303378e312 Mon Sep 17 00:00:00 2001
From: luke zhang <zhz961101@gmail.com>
Date: Tue, 28 May 2024 02:26:07 +0800
Subject: [PATCH 046/315] improve dom widget performance (#3584)

---
 web/scripts/domWidget.js | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/web/scripts/domWidget.js b/web/scripts/domWidget.js
index d5eeebdbd..b7f437ad2 100644
--- a/web/scripts/domWidget.js
+++ b/web/scripts/domWidget.js
@@ -11,9 +11,10 @@ function intersect(a, b) {
 	else return null;
 }
 
-function getClipPath(node, element, elRect) {
+function getClipPath(node, element) {
 	const selectedNode = Object.values(app.canvas.selected_nodes)[0];
 	if (selectedNode && selectedNode !== node) {
+		const elRect = element.getBoundingClientRect();
 		const MARGIN = 7;
 		const scale = app.canvas.ds.scale;
 
@@ -269,7 +270,7 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) {
 			});
 
 			if (enableDomClipping) {
-				element.style.clipPath = getClipPath(node, element, elRect);
+				element.style.clipPath = getClipPath(node, element);
 				element.style.willChange = "clip-path";
 			}
 

From 0920e0e5febc852cfc5496ac2ba6b44d12b5b35c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 27 May 2024 19:03:56 -0400
Subject: [PATCH 047/315] Remove some unused imports.

---
 comfy/ldm/cascade/stage_b.py    | 1 -
 comfy/ldm/cascade/stage_c.py    | 1 -
 comfy/ldm/models/autoencoder.py | 2 --
 comfy/ldm/modules/attention.py  | 2 +-
 comfy/model_management.py       | 1 -
 comfy/sd.py                     | 1 -
 comfy/sd2_clip.py               | 1 -
 comfy_extras/nodes_canny.py     | 7 +------
 comfy_extras/nodes_sag.py       | 1 -
 comfy_extras/nodes_sdupscale.py | 1 -
 10 files changed, 2 insertions(+), 16 deletions(-)

diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py
index 6d2c22231..7c3d8feab 100644
--- a/comfy/ldm/cascade/stage_b.py
+++ b/comfy/ldm/cascade/stage_b.py
@@ -17,7 +17,6 @@
 """
 
 import math
-import numpy as np
 import torch
 from torch import nn
 from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py
index 67c1e52b6..c85da1f01 100644
--- a/comfy/ldm/cascade/stage_c.py
+++ b/comfy/ldm/cascade/stage_c.py
@@ -18,7 +18,6 @@
 
 import torch
 from torch import nn
-import numpy as np
 import math
 from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
 # from .controlnet import ControlNetDeliverer
diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py
index b91ec3249..f5f4de288 100644
--- a/comfy/ldm/models/autoencoder.py
+++ b/comfy/ldm/models/autoencoder.py
@@ -1,6 +1,4 @@
 import torch
-# import pytorch_lightning as pl
-import torch.nn.functional as F
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Tuple, Union
 
diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 93c944589..da9f7aab7 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -3,7 +3,7 @@ import torch
 import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange, repeat
-from typing import Optional, Any
+from typing import Optional
 import logging
 
 from .diffusionmodules.util import AlphaBlender, timestep_embedding
diff --git a/comfy/model_management.py b/comfy/model_management.py
index ef36a2c48..5c1afd3d6 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -2,7 +2,6 @@ import psutil
 import logging
 from enum import Enum
 from comfy.cli_args import args
-import comfy.utils
 import torch
 import sys
 import platform
diff --git a/comfy/sd.py b/comfy/sd.py
index 8044c184f..343d2a02c 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -14,7 +14,6 @@ import comfy.utils
 from . import clip_vision
 from . import gligen
 from . import diffusers_convert
-from . import model_base
 from . import model_detection
 
 from . import sd1_clip
diff --git a/comfy/sd2_clip.py b/comfy/sd2_clip.py
index 9c878d54a..d14b44544 100644
--- a/comfy/sd2_clip.py
+++ b/comfy/sd2_clip.py
@@ -1,5 +1,4 @@
 from comfy import sd1_clip
-import torch
 import os
 
 class SD2ClipHModel(sd1_clip.SDClipModel):
diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py
index fab2ab7ac..d85e6b856 100644
--- a/comfy_extras/nodes_canny.py
+++ b/comfy_extras/nodes_canny.py
@@ -1,10 +1,5 @@
-import math
-
-import torch
-import torch.nn.functional as F
-import comfy.model_management
-
 from kornia.filters import canny
+import comfy.model_management
 
 
 class Canny:
diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py
index 8d786db57..010e99744 100644
--- a/comfy_extras/nodes_sag.py
+++ b/comfy_extras/nodes_sag.py
@@ -4,7 +4,6 @@ import torch.nn.functional as F
 import math
 
 from einops import rearrange, repeat
-import os
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.samplers
 
diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py
index 28c1cb0f1..bba67e8dd 100644
--- a/comfy_extras/nodes_sdupscale.py
+++ b/comfy_extras/nodes_sdupscale.py
@@ -1,5 +1,4 @@
 import torch
-import nodes
 import comfy.utils
 
 class SD_4XUpscale_Conditioning:

From b26da2245fbc65e003889539fc352bf14370cded Mon Sep 17 00:00:00 2001
From: JettHu <35261585+JettHu@users.noreply.github.com>
Date: Tue, 28 May 2024 07:30:35 +0800
Subject: [PATCH 048/315] Fix UnetParams annotation typo (#3589)

---
 comfy/types.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/types.py b/comfy/types.py
index a8a3d29fd..70cf4b158 100644
--- a/comfy/types.py
+++ b/comfy/types.py
@@ -25,7 +25,7 @@ class UnetParams(TypedDict):
     timestep: torch.Tensor
     c: UnetApplyConds
     # List of [0, 1], [0], [1], ...
-    # 0 means unconditional, 1 means conditional
+    # 0 means conditional, 1 means conditional unconditional
     cond_or_uncond: List[int]
 
 

From 91542d4f8b99dfb65a3f5c56cc24fea91d93858a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 28 May 2024 01:37:40 -0400
Subject: [PATCH 049/315] Import spandrel_extra_arches if present.

I will not add this dependency to the default ones because models in the
spandrel_extra_arches package are non commercial and therefore not
compatible with free software licenses like the one ComfyUI uses.

If you don't mind this you can install it manually yourself.
---
 comfy_extras/nodes_upscale_model.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py
index 03f294465..bca79ef2e 100644
--- a/comfy_extras/nodes_upscale_model.py
+++ b/comfy_extras/nodes_upscale_model.py
@@ -1,10 +1,19 @@
 import os
+import logging
 from spandrel import ModelLoader, ImageModelDescriptor
 from comfy import model_management
 import torch
 import comfy.utils
 import folder_paths
 
+try:
+    from spandrel_extra_arches import EXTRA_REGISTRY
+    from spandrel import MAIN_REGISTRY
+    MAIN_REGISTRY.add(*EXTRA_REGISTRY)
+    logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.")
+except:
+    pass
+
 class UpscaleModelLoader:
     @classmethod
     def INPUT_TYPES(s):

From 71ec5b144ee2c46ee12f1035782d6cb4bc84cca9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 29 May 2024 00:19:30 -0400
Subject: [PATCH 050/315] Update commands to install nightly pytorch in readme.

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index cf32014b2..de0c062ae 100644
--- a/README.md
+++ b/README.md
@@ -106,7 +106,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
 
 This is the command to install the nightly with ROCm 6.0 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.0```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.1```
 
 ### NVIDIA
 
@@ -116,7 +116,7 @@ Nvidia users should install stable pytorch using this command:
 
 This is the command to install pytorch nightly instead which might have performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124```
 
 #### Troubleshooting
 

From bf3e334d468876bbf69d022e7a6770b0c52dd615 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 30 May 2024 11:07:38 -0400
Subject: [PATCH 051/315] Disable non_blocking when --deterministic or
 directml.

---
 comfy/model_management.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 5c1afd3d6..b353e50bf 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -629,6 +629,10 @@ def supports_dtype(device, dtype): #TODO
 def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
+    if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
+        return False
+    if directml_enabled:
+        return False
     return True
 
 def device_should_use_non_blocking(device):

From 04b308229ee59b5aebc0c78ea416e0b3ac22c146 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 31 May 2024 11:18:37 -0400
Subject: [PATCH 052/315] Small refactor of preview code.

---
 latent_preview.py | 23 ++++++++++-------------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/latent_preview.py b/latent_preview.py
index b258fcf20..54aa233f2 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -11,6 +11,13 @@ import logging
 
 MAX_PREVIEW_RESOLUTION = 512
 
+def preview_to_image(latent_image):
+        latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
+                            .mul(0xFF)  # to 0..255
+                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+
+        return Image.fromarray(latents_ubyte.numpy())
+
 class LatentPreviewer:
     def decode_latent_to_preview(self, x0):
         pass
@@ -24,12 +31,8 @@ class TAESDPreviewerImpl(LatentPreviewer):
         self.taesd = taesd
 
     def decode_latent_to_preview(self, x0):
-        x_sample = self.taesd.decode(x0[:1])[0].detach()
-        x_sample = 255. * torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-        x_sample = np.moveaxis(x_sample.to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(x_sample.device)).numpy(), 0, 2)
-
-        preview_image = Image.fromarray(x_sample)
-        return preview_image
+        x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2)
+        return preview_to_image(x_sample)
 
 
 class Latent2RGBPreviewer(LatentPreviewer):
@@ -39,13 +42,7 @@ class Latent2RGBPreviewer(LatentPreviewer):
     def decode_latent_to_preview(self, x0):
         self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
         latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
-
-        latents_ubyte = (((latent_image + 1) / 2)
-                            .clamp(0, 1)  # change scale from -1..1 to 0..1
-                            .mul(0xFF)  # to 0..255
-                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
-
-        return Image.fromarray(latents_ubyte.numpy())
+        return preview_to_image(latent_image)
 
 
 def get_previewer(device, latent_format):

From e2c585f3be4f8f59211b26ea28d175ea63629a78 Mon Sep 17 00:00:00 2001
From: Peter Crabtree <peter.crabtree@gmail.com>
Date: Sat, 1 Jun 2024 12:36:08 -0400
Subject: [PATCH 053/315] Fix to allow use of PerpNegGuider with
 cfg_function_post hooks (like PAG) (#3618)

---
 .gitignore                    |  3 ++-
 comfy_extras/nodes_perpneg.py | 30 ++++++++++++++++++++++++++++--
 2 files changed, 30 insertions(+), 3 deletions(-)

diff --git a/.gitignore b/.gitignore
index 9f0389241..afad81489 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,4 +15,5 @@ venv/
 !/web/extensions/logging.js.example
 !/web/extensions/core/
 /tests-ui/data/object_info.json
-/user/
\ No newline at end of file
+/user/
+comfyui*.log
diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py
index 306cf9cd0..546276aa1 100644
--- a/comfy_extras/nodes_perpneg.py
+++ b/comfy_extras/nodes_perpneg.py
@@ -61,12 +61,38 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider):
         self.neg_scale = neg_scale
 
     def predict_noise(self, x, timestep, model_options={}, seed=None):
+        # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg
+        # but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly
+        
         positive_cond = self.conds.get("positive", None)
         negative_cond = self.conds.get("negative", None)
         empty_cond = self.conds.get("empty_negative_prompt", None)
 
-        out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, positive_cond, empty_cond], x, timestep, model_options)
-        return perp_neg(x, out[1], out[0], out[2], self.neg_scale, self.cfg)
+        (noise_pred_pos, noise_pred_neg, noise_pred_empty) = \
+            comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options)
+        cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg)
+
+        # normally this would be done in cfg_function, but we skipped 
+        # that for efficiency: we can compute the noise predictions in
+        # a single call to calc_cond_batch() (rather than two)
+        # so we replicate the hook here
+        for fn in model_options.get("sampler_post_cfg_function", []):
+            args = {
+                "denoised": cfg_result,
+                "cond": positive_cond,
+                "uncond": negative_cond,
+                "model": self.inner_model,
+                "uncond_denoised": noise_pred_neg,
+                "cond_denoised": noise_pred_pos,
+                "sigma": timestep,
+                "model_options": model_options,
+                "input": x,
+                # not in the original call in samplers.py:cfg_function, but made available for future hooks
+                "empty_cond": empty_cond,
+                "empty_cond_denoised": noise_pred_empty,}
+            cfg_result = fn(args)
+
+        return cfg_result
 
 class PerpNegGuider:
     @classmethod

From b249862080d4c046bd7f2680898c2f348c792a12 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 1 Jun 2024 12:47:31 -0400
Subject: [PATCH 054/315] Add an annoying print to a function I want to remove.

---
 .gitignore                | 3 +--
 comfy/model_management.py | 1 +
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.gitignore b/.gitignore
index afad81489..9f0389241 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,5 +15,4 @@ venv/
 !/web/extensions/logging.js.example
 !/web/extensions/core/
 /tests-ui/data/object_info.json
-/user/
-comfyui*.log
+/user/
\ No newline at end of file
diff --git a/comfy/model_management.py b/comfy/model_management.py
index b353e50bf..3b9fad362 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -879,6 +879,7 @@ def unload_all_models():
 
 
 def resolve_lowvram_weight(weight, model, key): #TODO: remove
+    print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
     return weight
 
 #TODO: might be cleaner to put this somewhere else

From 809cc85a8e092ae416ca2652a4b73671b8d3c72b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 2 Jun 2024 19:21:53 -0400
Subject: [PATCH 055/315] Remove useless code.

---
 comfy/model_patcher.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 2e746d8a9..84592f931 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -76,9 +76,7 @@ class ModelPatcher:
     def model_size(self):
         if self.size > 0:
             return self.size
-        model_sd = self.model.state_dict()
         self.size = comfy.model_management.module_size(self.model)
-        self.model_keys = set(model_sd.keys())
         return self.size
 
     def clone(self):
@@ -90,7 +88,6 @@ class ModelPatcher:
 
         n.object_patches = self.object_patches.copy()
         n.model_options = copy.deepcopy(self.model_options)
-        n.model_keys = self.model_keys
         n.backup = self.backup
         n.object_patches_backup = self.object_patches_backup
         return n
@@ -210,8 +207,9 @@ class ModelPatcher:
 
     def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
         p = set()
+        model_sd = self.model.state_dict()
         for k in patches:
-            if k in self.model_keys:
+            if k in model_sd:
                 p.add(k)
                 current_patches = self.patches.get(k, [])
                 current_patches.append((strength_patch, patches[k], strength_model))

From cb8d0ebccc93d3df6e00da1a57718a86d3dde300 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 3 Jun 2024 19:48:27 -0400
Subject: [PATCH 056/315] Don't load the view coordinates when loading a
 workflow from the history.

I think this makes things slightly less annoying for some users.
---
 web/scripts/app.js | 4 ++--
 web/scripts/ui.js  | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index 4dc011b9f..f96d197a8 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -1800,7 +1800,7 @@ export class ComfyApp {
 	 * @param {*} graphData A serialized graph object
 	 * @param { boolean } clean If the graph state, e.g. images, should be cleared
 	 */
-	async loadGraphData(graphData, clean = true) {
+	async loadGraphData(graphData, clean = true, restore_view = true) {
 		if (clean !== false) {
 			this.clean();
 		}
@@ -1836,7 +1836,7 @@ export class ComfyApp {
 
 		try {
 			this.graph.configure(graphData);
-			if (this.enableWorkflowViewRestore.value && graphData.extra?.ds) {
+			if (restore_view && this.enableWorkflowViewRestore.value && graphData.extra?.ds) {
 				this.canvas.ds.offset = graphData.extra.ds.offset;
 				this.canvas.ds.scale = graphData.extra.ds.scale;
 			}
diff --git a/web/scripts/ui.js b/web/scripts/ui.js
index 36fed3238..72e43d357 100644
--- a/web/scripts/ui.js
+++ b/web/scripts/ui.js
@@ -228,7 +228,7 @@ class ComfyList {
 							$el("button", {
 								textContent: "Load",
 								onclick: async () => {
-									await app.loadGraphData(item.prompt[3].extra_pnginfo.workflow);
+									await app.loadGraphData(item.prompt[3].extra_pnginfo.workflow, true, false);
 									if (item.outputs) {
 										app.nodeOutputs = item.outputs;
 									}

From 20447e9ec92b7e7e3544a6fd2932c31c90333991 Mon Sep 17 00:00:00 2001
From: Denys Smirnov <dennwc@protonmail.com>
Date: Tue, 4 Jun 2024 23:37:11 +0300
Subject: [PATCH 057/315] Fix alpha in PorterDuffImageComposite. (#3411)

There were two bugs in PorterDuffImageComposite.

The first one is the fact that it uses the mask input directly as alpha, missing the conversion (`1-a`). The fix is similar to c16f5744.

The second one is that all color composition formulas assume alpha premultiplied values, while the input is not premultiplied.

This change fixes both of these issue.
---
 comfy_extras/nodes_compositing.py | 19 ++++++++++++++++---
 1 file changed, 16 insertions(+), 3 deletions(-)

diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py
index 181b36ed6..48fe5e3dd 100644
--- a/comfy_extras/nodes_compositing.py
+++ b/comfy_extras/nodes_compositing.py
@@ -28,6 +28,14 @@ class PorterDuffMode(Enum):
 
 
 def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):
+    # convert mask to alpha
+    src_alpha = 1 - src_alpha
+    dst_alpha = 1 - dst_alpha
+    # premultiply alpha
+    src_image = src_image * src_alpha
+    dst_image = dst_image * dst_alpha
+
+    # composite ops below assume alpha-premultiplied images
     if mode == PorterDuffMode.ADD:
         out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
         out_image = torch.clamp(src_image + dst_image, 0, 1)
@@ -35,7 +43,7 @@ def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_
         out_alpha = torch.zeros_like(dst_alpha)
         out_image = torch.zeros_like(dst_image)
     elif mode == PorterDuffMode.DARKEN:
-        out_alpha = src_alpha + dst_alpha  - src_alpha * dst_alpha
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
         out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
     elif mode == PorterDuffMode.DST:
         out_alpha = dst_alpha
@@ -84,8 +92,13 @@ def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_
         out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
         out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
     else:
-        out_alpha = None
-        out_image = None
+        return None, None
+
+    # back to non-premultiplied alpha
+    out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image))
+    out_image = torch.clamp(out_image, 0, 1)
+    # convert alpha to mask
+    out_alpha = 1 - out_alpha
     return out_image, out_alpha
 
 

From b1fd26fe9e55163f780bf9e5f56bf9bf5f035c93 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 4 Jun 2024 17:44:14 -0400
Subject: [PATCH 058/315] pytorch xpu should be flash or mem efficient
 attention?

---
 comfy/model_management.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 3b9fad362..a5142d305 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -693,6 +693,8 @@ def pytorch_attention_flash_attention():
         #TODO: more reliable way of checking for flash attention?
         if is_nvidia(): #pytorch flash attention only works on Nvidia
             return True
+        if is_intel_xpu():
+            return True
     return False
 
 def force_upcast_attention_dtype():

From 104fcea0c8672b138a9bdd1ae00603c9240867c1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 5 Jun 2024 19:14:56 -0400
Subject: [PATCH 059/315] Add function to get the list of currently loaded
 models.

---
 comfy/model_management.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index a5142d305..57aa8bca2 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -276,6 +276,7 @@ class LoadedModel:
         self.device = model.load_device
         self.weights_loaded = False
         self.real_model = None
+        self.currently_used = True
 
     def model_memory(self):
         return self.model.model_size()
@@ -365,6 +366,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
         if shift_model.device == device:
             if shift_model not in keep_loaded:
                 can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
+                shift_model.currently_used = False
 
     for x in sorted(can_unload):
         i = x[-1]
@@ -410,6 +412,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
                 current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True)
                 loaded = None
             else:
+                loaded.currently_used = True
                 models_already_loaded.append(loaded)
 
         if loaded is None:
@@ -466,6 +469,16 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
 def load_model_gpu(model):
     return load_models_gpu([model])
 
+def loaded_models(only_currently_used=False):
+    output = []
+    for m in current_loaded_models:
+        if only_currently_used:
+            if not m.currently_used:
+                continue
+
+        output.append(m.model)
+    return output
+
 def cleanup_models(keep_clone_weights_loaded=False):
     to_delete = []
     for i in range(len(current_loaded_models)):

From 0dccb4617de61b81763321f01ae527dbe3b01202 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 6 Jun 2024 14:49:45 -0400
Subject: [PATCH 060/315] Remove some unnecessary arguments.

---
 nodes.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/nodes.py b/nodes.py
index 34821ca3f..f454ff8cd 100644
--- a/nodes.py
+++ b/nodes.py
@@ -496,7 +496,7 @@ class CheckpointLoader:
 
     CATEGORY = "advanced/loaders"
 
-    def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
+    def load_checkpoint(self, config_name, ckpt_name):
         config_path = folder_paths.get_full_path("configs", config_name)
         ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
         return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
@@ -511,7 +511,7 @@ class CheckpointLoaderSimple:
 
     CATEGORY = "loaders"
 
-    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+    def load_checkpoint(self, ckpt_name):
         ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return out[:3]

From 56333d48508f95bdef23870cad3239ba0ebdb8a9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 7 Jun 2024 03:05:23 -0400
Subject: [PATCH 061/315] Use the end token for the text encoder attention
 mask.

---
 comfy/sd1_clip.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index ff6db0d20..e7ebf046d 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -168,11 +168,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         attention_mask = None
         if self.enable_attention_masks:
             attention_mask = torch.zeros_like(tokens)
-            max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1
+            end_token = self.special_tokens.get("end", -1)
             for x in range(attention_mask.shape[0]):
                 for y in range(attention_mask.shape[1]):
                     attention_mask[x, y] = 1
-                    if tokens[x, y] == max_token:
+                    if tokens[x, y] == end_token:
                         break
 
         outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)

From 6cd8ffc465ed363b078249b081ea3f975e77cf15 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 8 Jun 2024 02:16:55 -0400
Subject: [PATCH 062/315] Reshape the empty latent image to the right amount of
 channels if needed.

---
 comfy/latent_formats.py              |  2 ++
 comfy/sample.py                      |  6 ++++++
 comfy/utils.py                       | 10 +++++-----
 comfy_extras/nodes_custom_sampler.py |  2 ++
 nodes.py                             |  2 ++
 5 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 4ca466d9a..69192bc62 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -2,6 +2,7 @@ import torch
 
 class LatentFormat:
     scale_factor = 1.0
+    latent_channels = 4
     latent_rgb_factors = None
     taesd_decoder_name = None
 
@@ -72,6 +73,7 @@ class SD_X4(LatentFormat):
         ]
 
 class SC_Prior(LatentFormat):
+    latent_channels = 16
     def __init__(self):
         self.scale_factor = 1.0
         self.latent_rgb_factors = [
diff --git a/comfy/sample.py b/comfy/sample.py
index e51bd67d6..98dcaca7f 100644
--- a/comfy/sample.py
+++ b/comfy/sample.py
@@ -24,6 +24,12 @@ def prepare_noise(latent_image, seed, noise_inds=None):
     noises = torch.cat(noises, axis=0)
     return noises
 
+def fix_empty_latent_channels(model, latent_image):
+    latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels
+    if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
+        latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
+    return latent_image
+
 def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
     logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed")
     return model, positive, negative, noise_mask, []
diff --git a/comfy/utils.py b/comfy/utils.py
index ab47b8f28..884404cce 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -249,11 +249,11 @@ def unet_to_diffusers(unet_config):
 
     return diffusers_unet_map
 
-def repeat_to_batch_size(tensor, batch_size):
-    if tensor.shape[0] > batch_size:
-        return tensor[:batch_size]
-    elif tensor.shape[0] < batch_size:
-        return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size]
+def repeat_to_batch_size(tensor, batch_size, dim=0):
+    if tensor.shape[dim] > batch_size:
+        return tensor.narrow(dim, 0, batch_size)
+    elif tensor.shape[dim] < batch_size:
+        return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size)
     return tensor
 
 def resize_to_batch_size(tensor, batch_size):
diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
index 47f08bf60..45ef8cf40 100644
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -380,6 +380,7 @@ class SamplerCustom:
     def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image):
         latent = latent_image
         latent_image = latent["samples"]
+        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
         if not add_noise:
             noise = Noise_EmptyNoise().generate_noise(latent)
         else:
@@ -538,6 +539,7 @@ class SamplerCustomAdvanced:
     def sample(self, noise, guider, sampler, sigmas, latent_image):
         latent = latent_image
         latent_image = latent["samples"]
+        latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
 
         noise_mask = None
         if "noise_mask" in latent:
diff --git a/nodes.py b/nodes.py
index f454ff8cd..b744b53f0 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1299,6 +1299,8 @@ class SetLatentNoiseMask:
 
 def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
     latent_image = latent["samples"]
+    latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+
     if disable_noise:
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
     else:

From 742d5720d1b128c78266bfd7156fb578d664a95a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 9 Jun 2024 16:41:04 -0400
Subject: [PATCH 063/315] Support zeroing out text embeddings with the
 attention mask.

---
 comfy/sd1_clip.py | 13 +++++++++----
 1 file changed, 9 insertions(+), 4 deletions(-)

diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index e7ebf046d..2729f14d8 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -68,7 +68,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     ]
     def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
-                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, return_projected_pooled=True):  # clip-vit-base-patch32
+                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
+                 return_projected_pooled=True):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
 
@@ -90,6 +91,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
         self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
         self.enable_attention_masks = enable_attention_masks
+        self.zero_out_masked = zero_out_masked
 
         self.layer_norm_hidden_state = layer_norm_hidden_state
         self.return_projected_pooled = return_projected_pooled
@@ -179,9 +181,12 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.transformer.set_input_embeddings(backup_embeds)
 
         if self.layer == "last":
-            z = outputs[0]
+            z = outputs[0].float()
         else:
-            z = outputs[1]
+            z = outputs[1].float()
+
+        if self.zero_out_masked and attention_mask is not None:
+            z *= attention_mask.unsqueeze(-1).float()
 
         pooled_output = None
         if len(outputs) >= 3:
@@ -190,7 +195,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             elif outputs[2] is not None:
                 pooled_output = outputs[2].float()
 
-        return z.float(), pooled_output
+        return z, pooled_output
 
     def encode(self, tokens):
         return self(tokens)

From a5e6a632f9f16e5b3c72c428820bce67b05446bf Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 10 Jun 2024 01:05:53 -0400
Subject: [PATCH 064/315] Support sampling non 2D latents.

---
 comfy/samplers.py | 93 ++++++++++++++++++++++++++++++++---------------
 1 file changed, 63 insertions(+), 30 deletions(-)

diff --git a/comfy/samplers.py b/comfy/samplers.py
index 29962a916..656e0a28f 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -8,7 +8,8 @@ import logging
 import comfy.sampler_helpers
 
 def get_area_and_mult(conds, x_in, timestep_in):
-    area = (x_in.shape[2], x_in.shape[3], 0, 0)
+    dims = tuple(x_in.shape[2:])
+    area = None
     strength = 1.0
 
     if 'timestep_start' in conds:
@@ -20,11 +21,16 @@ def get_area_and_mult(conds, x_in, timestep_in):
         if timestep_in[0] < timestep_end:
             return None
     if 'area' in conds:
-        area = conds['area']
+        area = list(conds['area'])
     if 'strength' in conds:
         strength = conds['strength']
 
-    input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
+    input_x = x_in
+    if area is not None:
+        for i in range(len(dims)):
+            area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i])
+            input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i])
+
     if 'mask' in conds:
         # Scale the mask to the size of the input
         # The mask should have been resized as we began the sampling process
@@ -32,28 +38,30 @@ def get_area_and_mult(conds, x_in, timestep_in):
         if "mask_strength" in conds:
             mask_strength = conds["mask_strength"]
         mask = conds['mask']
-        assert(mask.shape[1] == x_in.shape[2])
-        assert(mask.shape[2] == x_in.shape[3])
-        mask = mask[:input_x.shape[0],area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
+        assert(mask.shape[1:] == x_in.shape[2:])
+
+        mask = mask[:input_x.shape[0]]
+        if area is not None:
+            for i in range(len(dims)):
+                mask = mask.narrow(i + 1, area[len(dims) + i], area[i])
+
+        mask = mask * mask_strength
         mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
     else:
         mask = torch.ones_like(input_x)
     mult = mask * strength
 
-    if 'mask' not in conds:
+    if 'mask' not in conds and area is not None:
         rr = 8
-        if area[2] != 0:
-            for t in range(rr):
-                mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
-        if (area[0] + area[2]) < x_in.shape[2]:
-            for t in range(rr):
-                mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
-        if area[3] != 0:
-            for t in range(rr):
-                mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
-        if (area[1] + area[3]) < x_in.shape[3]:
-            for t in range(rr):
-                mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
+        for i in range(len(dims)):
+            if area[len(dims) + i] != 0:
+                for t in range(rr):
+                    m = mult.narrow(i + 2, t, 1)
+                    m *= ((1.0/rr) * (t + 1))
+            if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]:
+                for t in range(rr):
+                    m = mult.narrow(i + 2, area[i] - 1 - t, 1)
+                    m *= ((1.0/rr) * (t + 1))
 
     conditioning = {}
     model_conds = conds["model_conds"]
@@ -219,8 +227,19 @@ def calc_cond_batch(model, conds, x_in, timestep, model_options):
 
         for o in range(batch_chunks):
             cond_index = cond_or_uncond[o]
-            out_conds[cond_index][:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
-            out_counts[cond_index][:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
+            a = area[o]
+            if a is None:
+                out_conds[cond_index] += output[o] * mult[o]
+                out_counts[cond_index] += mult[o]
+            else:
+                out_c = out_conds[cond_index]
+                out_cts = out_counts[cond_index]
+                dims = len(a) // 2
+                for i in range(dims):
+                    out_c = out_c.narrow(i + 2, a[i + dims], a[i])
+                    out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
+                out_c += output[o] * mult[o]
+                out_cts += mult[o]
 
     for i in range(len(out_conds)):
         out_conds[i] /= out_counts[i]
@@ -335,7 +354,7 @@ def get_mask_aabb(masks):
 
     return bounding_boxes, is_empty
 
-def resolve_areas_and_cond_masks(conditions, h, w, device):
+def resolve_areas_and_cond_masks_multidim(conditions, dims, device):
     # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
     # While we're doing this, we can also resolve the mask device and scaling for performance reasons
     for i in range(len(conditions)):
@@ -344,7 +363,14 @@ def resolve_areas_and_cond_masks(conditions, h, w, device):
             area = c['area']
             if area[0] == "percentage":
                 modified = c.copy()
-                area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w))
+                a = area[1:]
+                a_len = len(a) // 2
+                area = ()
+                for d in range(len(dims)):
+                    area += (max(1, round(a[d] * dims[d])),)
+                for d in range(len(dims)):
+                    area += (round(a[d + a_len] * dims[d]),)
+
                 modified['area'] = area
                 c = modified
                 conditions[i] = c
@@ -353,12 +379,12 @@ def resolve_areas_and_cond_masks(conditions, h, w, device):
             mask = c['mask']
             mask = mask.to(device=device)
             modified = c.copy()
-            if len(mask.shape) == 2:
+            if len(mask.shape) == len(dims):
                 mask = mask.unsqueeze(0)
-            if mask.shape[1] != h or mask.shape[2] != w:
-                mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1)
+            if mask.shape[1:] != dims:
+                mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1)
 
-            if modified.get("set_area_to_bounds", False):
+            if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2
                 bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
                 boxes, is_empty = get_mask_aabb(bounds)
                 if is_empty[0]:
@@ -375,7 +401,11 @@ def resolve_areas_and_cond_masks(conditions, h, w, device):
             modified['mask'] = mask
             conditions[i] = modified
 
-def create_cond_with_same_area_if_none(conds, c):
+def resolve_areas_and_cond_masks(conditions, h, w, device):
+    logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
+    return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)
+
+def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2
     if 'area' not in c:
         return
 
@@ -479,7 +509,10 @@ def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwar
         params = x.copy()
         params["device"] = device
         params["noise"] = noise
-        params["width"] = params.get("width", noise.shape[3] * 8)
+        default_width = None
+        if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model
+            default_width = noise.shape[3] * 8
+        params["width"] = params.get("width", default_width)
         params["height"] = params.get("height", noise.shape[2] * 8)
         params["prompt_type"] = params.get("prompt_type", prompt_type)
         for k in kwargs:
@@ -567,7 +600,7 @@ def ksampler(sampler_name, extra_options={}, inpaint_options={}):
 def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None):
     for k in conds:
         conds[k] = conds[k][:]
-        resolve_areas_and_cond_masks(conds[k], noise.shape[2], noise.shape[3], device)
+        resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device)
 
     for k in conds:
         calculate_start_end_timesteps(model, conds[k])

From 8c4a9befa7261b6fc78407ace90a57d21bfe631e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 10 Jun 2024 13:26:25 -0400
Subject: [PATCH 065/315] SD3 Support.

---
 comfy/latent_formats.py                     |     33 +-
 comfy/ldm/modules/diffusionmodules/mmdit.py |   1023 +
 comfy/model_base.py                         |     26 +
 comfy/model_detection.py                    |     42 +-
 comfy/model_sampling.py                     |     61 +
 comfy/sd.py                                 |      6 +-
 comfy/sd3_clip.py                           |     91 +
 comfy/supported_models.py                   |     25 +-
 comfy/t5.py                                 |    231 +
 comfy/t5_config_base.json                   |     21 +
 comfy/t5_config_xxl.json                    |     21 +
 comfy/t5_tokenizer/special_tokens_map.json  |    125 +
 comfy/t5_tokenizer/tokenizer.json           | 129428 +++++++++++++++++
 comfy/t5_tokenizer/tokenizer_config.json    |    939 +
 comfy_extras/nodes_model_advanced.py        |     27 +
 comfy_extras/nodes_sd3.py                   |     87 +
 nodes.py                                    |      1 +
 17 files changed, 132182 insertions(+), 5 deletions(-)
 create mode 100644 comfy/ldm/modules/diffusionmodules/mmdit.py
 create mode 100644 comfy/sd3_clip.py
 create mode 100644 comfy/t5.py
 create mode 100644 comfy/t5_config_base.json
 create mode 100644 comfy/t5_config_xxl.json
 create mode 100644 comfy/t5_tokenizer/special_tokens_map.json
 create mode 100644 comfy/t5_tokenizer/tokenizer.json
 create mode 100644 comfy/t5_tokenizer/tokenizer_config.json
 create mode 100644 comfy_extras/nodes_sd3.py

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 69192bc62..6a9a96206 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -25,8 +25,9 @@ class SD15(LatentFormat):
         self.taesd_decoder_name = "taesd_decoder"
 
 class SDXL(LatentFormat):
+    scale_factor = 0.13025
+
     def __init__(self):
-        self.scale_factor = 0.13025
         self.latent_rgb_factors = [
                     #   R        G        B
                     [ 0.3920,  0.4054,  0.4549],
@@ -104,3 +105,33 @@ class SC_B(LatentFormat):
             [-0.3087, -0.1535,  0.0366],
             [ 0.0290, -0.1574, -0.4078]
         ]
+
+class SD3(LatentFormat):
+    latent_channels = 16
+    def __init__(self):
+        self.scale_factor = 1.5305
+        self.shift_factor = 0.0609
+        self.latent_rgb_factors = [
+            [-0.0645,  0.0177,  0.1052],
+            [ 0.0028,  0.0312,  0.0650],
+            [ 0.1848,  0.0762,  0.0360],
+            [ 0.0944,  0.0360,  0.0889],
+            [ 0.0897,  0.0506, -0.0364],
+            [-0.0020,  0.1203,  0.0284],
+            [ 0.0855,  0.0118,  0.0283],
+            [-0.0539,  0.0658,  0.1047],
+            [-0.0057,  0.0116,  0.0700],
+            [-0.0412,  0.0281, -0.0039],
+            [ 0.1106,  0.1171,  0.1220],
+            [-0.0248,  0.0682, -0.0481],
+            [ 0.0815,  0.0846,  0.1207],
+            [-0.0120, -0.0055, -0.0867],
+            [-0.0749, -0.0634, -0.0456],
+            [-0.1418, -0.1457, -0.1259]
+        ]
+
+    def process_in(self, latent):
+        return (latent - self.shift_factor) * self.scale_factor
+
+    def process_out(self, latent):
+        return (latent / self.scale_factor) + self.shift_factor
diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
new file mode 100644
index 000000000..5e7afc8d3
--- /dev/null
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -0,0 +1,1023 @@
+import logging
+import math
+from typing import Dict, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+from .. import attention
+from einops import rearrange, repeat
+
+def default(x, y):
+    if x is not None:
+        return x
+    return y
+
+class Mlp(nn.Module):
+    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    def __init__(
+            self,
+            in_features,
+            hidden_features=None,
+            out_features=None,
+            act_layer=nn.GELU,
+            norm_layer=None,
+            bias=True,
+            drop=0.,
+            use_conv=False,
+            dtype=None,
+            device=None,
+            operations=None,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        drop_probs = drop
+        linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear
+
+        self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop_probs)
+        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
+        self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
+        self.drop2 = nn.Dropout(drop_probs)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = self.norm(x)
+        x = self.fc2(x)
+        x = self.drop2(x)
+        return x
+
+class PatchEmbed(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    dynamic_img_pad: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            img_size: Optional[int] = 224,
+            patch_size: int = 16,
+            in_chans: int = 3,
+            embed_dim: int = 768,
+            norm_layer = None,
+            flatten: bool = True,
+            bias: bool = True,
+            strict_img_size: bool = True,
+            dynamic_img_pad: bool = True,
+            dtype=None,
+            device=None,
+            operations=None,
+    ):
+        super().__init__()
+        self.patch_size = (patch_size, patch_size)
+        if img_size is not None:
+            self.img_size = (img_size, img_size)
+            self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
+            self.num_patches = self.grid_size[0] * self.grid_size[1]
+        else:
+            self.img_size = None
+            self.grid_size = None
+            self.num_patches = None
+
+        # flatten spatial dim and transpose to channels last, kept for bwd compat
+        self.flatten = flatten
+        self.strict_img_size = strict_img_size
+        self.dynamic_img_pad = dynamic_img_pad
+
+        self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # if self.img_size is not None:
+        #     if self.strict_img_size:
+        #         _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
+        #         _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
+        #     elif not self.dynamic_img_pad:
+        #         _assert(
+        #             H % self.patch_size[0] == 0,
+        #             f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
+        #         )
+        #         _assert(
+        #             W % self.patch_size[1] == 0,
+        #             f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
+        #         )
+        if self.dynamic_img_pad:
+            pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
+            pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
+            x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+        x = self.norm(x)
+        return x
+
+def modulate(x, shift, scale):
+    if shift is None:
+        shift = torch.zeros_like(scale)
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+#################################################################################
+#                   Sine/Cosine Positional Embedding Functions                  #
+#################################################################################
+
+
+def get_2d_sincos_pos_embed(
+    embed_dim,
+    grid_size,
+    cls_token=False,
+    extra_tokens=0,
+    scaling_factor=None,
+    offset=None,
+):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_h = np.arange(grid_size, dtype=np.float32)
+    grid_w = np.arange(grid_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+    if scaling_factor is not None:
+        grid = grid / scaling_factor
+    if offset is not None:
+        grid = grid - offset
+
+    grid = grid.reshape([2, 1, grid_size, grid_size])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate(
+            [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
+        )
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32):
+    omega = torch.arange(embed_dim // 2, device=device, dtype=dtype)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+    pos = pos.reshape(-1)  # (M,)
+    out = torch.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+    emb_sin = torch.sin(out)  # (M, D/2)
+    emb_cos = torch.cos(out)  # (M, D/2)
+    emb = torch.cat([emb_sin, emb_cos], dim=1)  # (M, D)
+    return emb
+
+def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32):
+    small = min(h, w)
+    val_h = (h / small) * val_magnitude
+    val_w = (w / small) * val_magnitude
+    grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij')
+    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
+    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
+    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
+    return emb
+
+
+#################################################################################
+#               Embedding Layers for Timesteps and Class Labels                 #
+#################################################################################
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32)
+            / half
+        ).to(device=t.device)
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        if torch.is_floating_point(t):
+            embedding = embedding.to(dtype=t.dtype)
+        return embedding
+
+    def forward(self, t, dtype, **kwargs):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class VectorEmbedder(nn.Module):
+    """
+    Embeds a flat vector of dimension input_dim
+    """
+
+    def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        emb = self.mlp(x)
+        return emb
+
+
+#################################################################################
+#                                 Core DiT Model                                #
+#################################################################################
+
+
+def split_qkv(qkv, head_dim):
+    qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
+    return qkv[0], qkv[1], qkv[2]
+
+def optimized_attention(qkv, num_heads):
+    return attention.optimized_attention(qkv[0], qkv[1], qkv[2], num_heads)
+
+class SelfAttention(nn.Module):
+    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_scale: Optional[float] = None,
+        proj_drop: float = 0.0,
+        attn_mode: str = "xformers",
+        pre_only: bool = False,
+        qk_norm: Optional[str] = None,
+        rmsnorm: bool = False,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        if not pre_only:
+            self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+            self.proj_drop = nn.Dropout(proj_drop)
+        assert attn_mode in self.ATTENTION_MODES
+        self.attn_mode = attn_mode
+        self.pre_only = pre_only
+
+        if qk_norm == "rms":
+            self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+            self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+        elif qk_norm == "ln":
+            self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+            self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+        elif qk_norm is None:
+            self.ln_q = nn.Identity()
+            self.ln_k = nn.Identity()
+        else:
+            raise ValueError(qk_norm)
+
+    def pre_attention(self, x: torch.Tensor) -> torch.Tensor:
+        B, L, C = x.shape
+        qkv = self.qkv(x)
+        q, k, v = split_qkv(qkv, self.head_dim)
+        q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
+        k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
+        return (q, k, v)
+
+    def post_attention(self, x: torch.Tensor) -> torch.Tensor:
+        assert not self.pre_only
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        qkv = self.pre_attention(x)
+        x = optimized_attention(
+            qkv, num_heads=self.num_heads
+        )
+        x = self.post_attention(x)
+        return x
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(
+        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+        """
+        super().__init__()
+        self.eps = eps
+        self.learnable_scale = elementwise_affine
+        if self.learnable_scale:
+            self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+        else:
+            self.register_parameter("weight", None)
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+        Args:
+            x (torch.Tensor): The input tensor.
+        Returns:
+            torch.Tensor: The normalized tensor.
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+        Args:
+            x (torch.Tensor): The input tensor.
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+        """
+        x = self._norm(x)
+        if self.learnable_scale:
+            return x * self.weight.to(device=x.device, dtype=x.dtype)
+        else:
+            return x
+
+
+class SwiGLUFeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float] = None,
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
+
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+        """
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
+
+
+class DismantledBlock(nn.Module):
+    """
+    A DiT block with gated adaptive layer norm (adaLN) conditioning.
+    """
+
+    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: str = "xformers",
+        qkv_bias: bool = False,
+        pre_only: bool = False,
+        rmsnorm: bool = False,
+        scale_mod_only: bool = False,
+        swiglu: bool = False,
+        qk_norm: Optional[str] = None,
+        dtype=None,
+        device=None,
+        operations=None,
+        **block_kwargs,
+    ):
+        super().__init__()
+        assert attn_mode in self.ATTENTION_MODES
+        if not rmsnorm:
+            self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        else:
+            self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.attn = SelfAttention(
+            dim=hidden_size,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            attn_mode=attn_mode,
+            pre_only=pre_only,
+            qk_norm=qk_norm,
+            rmsnorm=rmsnorm,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+        if not pre_only:
+            if not rmsnorm:
+                self.norm2 = operations.LayerNorm(
+                    hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
+                )
+            else:
+                self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        if not pre_only:
+            if not swiglu:
+                self.mlp = Mlp(
+                    in_features=hidden_size,
+                    hidden_features=mlp_hidden_dim,
+                    act_layer=lambda: nn.GELU(approximate="tanh"),
+                    drop=0,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations
+                )
+            else:
+                self.mlp = SwiGLUFeedForward(
+                    dim=hidden_size,
+                    hidden_dim=mlp_hidden_dim,
+                    multiple_of=256,
+                )
+        self.scale_mod_only = scale_mod_only
+        if not scale_mod_only:
+            n_mods = 6 if not pre_only else 2
+        else:
+            n_mods = 4 if not pre_only else 1
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+        self.pre_only = pre_only
+
+    def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        if not self.pre_only:
+            if not self.scale_mod_only:
+                (
+                    shift_msa,
+                    scale_msa,
+                    gate_msa,
+                    shift_mlp,
+                    scale_mlp,
+                    gate_mlp,
+                ) = self.adaLN_modulation(c).chunk(6, dim=1)
+            else:
+                shift_msa = None
+                shift_mlp = None
+                (
+                    scale_msa,
+                    gate_msa,
+                    scale_mlp,
+                    gate_mlp,
+                ) = self.adaLN_modulation(
+                    c
+                ).chunk(4, dim=1)
+            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
+            return qkv, (
+                x,
+                gate_msa,
+                shift_mlp,
+                scale_mlp,
+                gate_mlp,
+            )
+        else:
+            if not self.scale_mod_only:
+                (
+                    shift_msa,
+                    scale_msa,
+                ) = self.adaLN_modulation(
+                    c
+                ).chunk(2, dim=1)
+            else:
+                shift_msa = None
+                scale_msa = self.adaLN_modulation(c)
+            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
+            return qkv, None
+
+    def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
+        assert not self.pre_only
+        x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp)
+        )
+        return x
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        assert not self.pre_only
+        qkv, intermediates = self.pre_attention(x, c)
+        attn = optimized_attention(
+            qkv,
+            num_heads=self.attn.num_heads,
+        )
+        return self.post_attention(attn, *intermediates)
+
+
+def block_mixing(*args, use_checkpoint=True, **kwargs):
+    if use_checkpoint:
+        return torch.utils.checkpoint.checkpoint(
+            _block_mixing, *args, use_reentrant=False, **kwargs
+        )
+    else:
+        return _block_mixing(*args, **kwargs)
+
+
+def _block_mixing(context, x, context_block, x_block, c):
+    context_qkv, context_intermediates = context_block.pre_attention(context, c)
+
+    x_qkv, x_intermediates = x_block.pre_attention(x, c)
+
+    o = []
+    for t in range(3):
+        o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
+    qkv = tuple(o)
+
+    attn = optimized_attention(
+        qkv,
+        num_heads=x_block.attn.num_heads,
+    )
+    context_attn, x_attn = (
+        attn[:, : context_qkv[0].shape[1]],
+        attn[:, context_qkv[0].shape[1] :],
+    )
+
+    if not context_block.pre_only:
+        context = context_block.post_attention(context_attn, *context_intermediates)
+
+    else:
+        context = None
+    x = x_block.post_attention(x_attn, *x_intermediates)
+    return context, x
+
+
+class JointBlock(nn.Module):
+    """just a small wrapper to serve as a fsdp unit"""
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        pre_only = kwargs.pop("pre_only")
+        qk_norm = kwargs.pop("qk_norm", None)
+        self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
+        self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs)
+
+    def forward(self, *args, **kwargs):
+        return block_mixing(
+            *args, context_block=self.context_block, x_block=self.x_block, **kwargs
+        )
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        patch_size: int,
+        out_channels: int,
+        total_out_channels: Optional[int] = None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = (
+            operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+            if (total_out_channels is None)
+            else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
+        )
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+class SelfAttentionContext(nn.Module):
+    def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None):
+        super().__init__()
+        dim_head = dim // heads
+        inner_dim = dim
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device)
+
+        self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device)
+
+    def forward(self, x):
+        qkv = self.qkv(x)
+        q, k, v = split_qkv(qkv, self.dim_head)
+        x = optimized_attention((q.reshape(q.shape[0], q.shape[1], -1), k, v), self.heads)
+        return self.proj(x)
+
+class ContextProcessorBlock(nn.Module):
+    def __init__(self, context_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations)
+        self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x):
+        x += self.attn(self.norm1(x))
+        x += self.mlp(self.norm2(x))
+        return x
+
+class ContextProcessor(nn.Module):
+    def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)])
+        self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+    def forward(self, x):
+        for i, l in enumerate(self.layers):
+            x = l(x)
+        return self.norm(x)
+
+class MMDiT(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+
+    def __init__(
+        self,
+        input_size: int = 32,
+        patch_size: int = 2,
+        in_channels: int = 4,
+        depth: int = 28,
+        # hidden_size: Optional[int] = None,
+        # num_heads: Optional[int] = None,
+        mlp_ratio: float = 4.0,
+        learn_sigma: bool = False,
+        adm_in_channels: Optional[int] = None,
+        context_embedder_config: Optional[Dict] = None,
+        compile_core: bool = False,
+        use_checkpoint: bool = False,
+        register_length: int = 0,
+        attn_mode: str = "torch",
+        rmsnorm: bool = False,
+        scale_mod_only: bool = False,
+        swiglu: bool = False,
+        out_channels: Optional[int] = None,
+        pos_embed_scaling_factor: Optional[float] = None,
+        pos_embed_offset: Optional[float] = None,
+        pos_embed_max_size: Optional[int] = None,
+        num_patches = None,
+        qk_norm: Optional[str] = None,
+        qkv_bias: bool = True,
+        context_processor_layers = None,
+        context_size = 4096,
+        dtype = None, #TODO
+        device = None,
+        operations = None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        default_out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.out_channels = default(out_channels, default_out_channels)
+        self.patch_size = patch_size
+        self.pos_embed_scaling_factor = pos_embed_scaling_factor
+        self.pos_embed_offset = pos_embed_offset
+        self.pos_embed_max_size = pos_embed_max_size
+
+        # hidden_size = default(hidden_size, 64 * depth)
+        # num_heads = default(num_heads, hidden_size // 64)
+
+        # apply magic --> this defines a head_size of 64
+        self.hidden_size = 64 * depth
+        num_heads = depth
+
+        self.num_heads = num_heads
+
+        self.x_embedder = PatchEmbed(
+            input_size,
+            patch_size,
+            in_channels,
+            self.hidden_size,
+            bias=True,
+            strict_img_size=self.pos_embed_max_size is None,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+        self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+        self.y_embedder = None
+        if adm_in_channels is not None:
+            assert isinstance(adm_in_channels, int)
+            self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+        if context_processor_layers is not None:
+            self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations)
+        else:
+            self.context_processor = None
+
+        self.context_embedder = nn.Identity()
+        if context_embedder_config is not None:
+            if context_embedder_config["target"] == "torch.nn.Linear":
+                self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device)
+
+        self.register_length = register_length
+        if self.register_length > 0:
+            self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device))
+
+        # num_patches = self.x_embedder.num_patches
+        # Will use fixed sin-cos embedding:
+        # just use a buffer already
+        if num_patches is not None:
+            self.register_buffer(
+                "pos_embed",
+                torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device),
+            )
+        else:
+            self.pos_embed = None
+
+        self.use_checkpoint = use_checkpoint
+        self.joint_blocks = nn.ModuleList(
+            [
+                JointBlock(
+                    self.hidden_size,
+                    num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    attn_mode=attn_mode,
+                    pre_only=i == depth - 1,
+                    rmsnorm=rmsnorm,
+                    scale_mod_only=scale_mod_only,
+                    swiglu=swiglu,
+                    qk_norm=qk_norm,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations
+                )
+                for i in range(depth)
+            ]
+        )
+
+        self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+        # self.initialize_weights()
+
+        if compile_core:
+            assert False
+            self.forward_core_with_concat = torch.compile(self.forward_core_with_concat)
+
+    def initialize_weights(self):
+        # TODO: Init context_embedder?
+        # Initialize transformer layers:
+        def _basic_init(module):
+            if isinstance(module, nn.Linear):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+        # Initialize (and freeze) pos_embed by sin-cos embedding
+        if self.pos_embed is not None:
+            pos_embed_grid_size = (
+                int(self.x_embedder.num_patches**0.5)
+                if self.pos_embed_max_size is None
+                else self.pos_embed_max_size
+            )
+            pos_embed = get_2d_sincos_pos_embed(
+                self.pos_embed.shape[-1],
+                int(self.x_embedder.num_patches**0.5),
+                pos_embed_grid_size,
+                scaling_factor=self.pos_embed_scaling_factor,
+                offset=self.pos_embed_offset,
+            )
+
+
+            pos_embed = get_2d_sincos_pos_embed(
+                self.pos_embed.shape[-1],
+                int(self.pos_embed.shape[-2]**0.5),
+                scaling_factor=self.pos_embed_scaling_factor,
+            )
+            self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
+
+        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d)
+        w = self.x_embedder.proj.weight.data
+        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
+        nn.init.constant_(self.x_embedder.proj.bias, 0)
+
+        if hasattr(self, "y_embedder"):
+            nn.init.normal_(self.y_embedder.mlp[0].weight, std=0.02)
+            nn.init.normal_(self.y_embedder.mlp[2].weight, std=0.02)
+
+        # Initialize timestep embedding MLP:
+        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
+        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
+
+        # Zero-out adaLN modulation layers in DiT blocks:
+        for block in self.joint_blocks:
+            nn.init.constant_(block.x_block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.x_block.adaLN_modulation[-1].bias, 0)
+            nn.init.constant_(block.context_block.adaLN_modulation[-1].weight, 0)
+            nn.init.constant_(block.context_block.adaLN_modulation[-1].bias, 0)
+
+        # Zero-out output layers:
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
+        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
+        nn.init.constant_(self.final_layer.linear.weight, 0)
+        nn.init.constant_(self.final_layer.linear.bias, 0)
+
+    def cropped_pos_embed(self, hw, device=None):
+        p = self.x_embedder.patch_size[0]
+        h, w = hw
+        # patched size
+        h = (h + 1) // p
+        w = (w + 1) // p
+        if self.pos_embed is None:
+            return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device)
+        assert self.pos_embed_max_size is not None
+        assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
+        assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
+        top = (self.pos_embed_max_size - h) // 2
+        left = (self.pos_embed_max_size - w) // 2
+        spatial_pos_embed = rearrange(
+            self.pos_embed,
+            "1 (h w) c -> 1 h w c",
+            h=self.pos_embed_max_size,
+            w=self.pos_embed_max_size,
+        )
+        spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
+        spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
+        # print(spatial_pos_embed, top, left, h, w)
+        # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res
+        # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better
+        # # print(t)
+        # return t
+        return spatial_pos_embed
+
+    def unpatchify(self, x, hw=None):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        if hw is None:
+            h = w = int(x.shape[1] ** 0.5)
+        else:
+            h, w = hw
+            h = (h + 1) // p
+            w = (w + 1) // p
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum("nhwpqc->nchpwq", x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs
+
+    def forward_core_with_concat(
+        self,
+        x: torch.Tensor,
+        c_mod: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if self.register_length > 0:
+            context = torch.cat(
+                (
+                    repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
+                    default(context, torch.Tensor([]).type_as(x)),
+                ),
+                1,
+            )
+
+        # context is B, L', D
+        # x is B, L, D
+        for block in self.joint_blocks:
+            context, x = block(
+                context,
+                x,
+                c=c_mod,
+                use_checkpoint=self.use_checkpoint,
+            )
+
+        x = self.final_layer(x, c_mod)  # (N, T, patch_size ** 2 * out_channels)
+        return x
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        t: torch.Tensor,
+        y: Optional[torch.Tensor] = None,
+        context: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+
+        if self.context_processor is not None:
+            context = self.context_processor(context)
+
+        hw = x.shape[-2:]
+        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype)
+        c = self.t_embedder(t, dtype=x.dtype)  # (N, D)
+        if y is not None and self.y_embedder is not None:
+            y = self.y_embedder(y)  # (N, D)
+            c = c + y  # (N, D)
+
+        if context is not None:
+            context = self.context_embedder(context)
+
+        x = self.forward_core_with_concat(x, c, context)
+
+        x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
+        return x[:,:,:hw[-2],:hw[-1]]
+
+
+class OpenAISignatureMMDITWrapper(MMDiT):
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        y: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        return super().forward(x, timesteps, context=context, y=y)
+
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 841598b73..a26b442b1 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -5,11 +5,13 @@ from comfy.ldm.cascade.stage_c import StageC
 from comfy.ldm.cascade.stage_b import StageB
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
+from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
 import comfy.model_management
 import comfy.conds
 import comfy.ops
 from enum import Enum
 from . import utils
+import comfy.latent_formats
 
 class ModelType(Enum):
     EPS = 1
@@ -17,6 +19,7 @@ class ModelType(Enum):
     V_PREDICTION_EDM = 3
     STABLE_CASCADE = 4
     EDM = 5
+    FLOW = 6
 
 
 from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling
@@ -32,6 +35,9 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.V_PREDICTION_EDM:
         c = V_PREDICTION
         s = ModelSamplingContinuousEDM
+    elif model_type == ModelType.FLOW:
+        c = comfy.model_sampling.CONST
+        s = comfy.model_sampling.ModelSamplingDiscreteFlow
     elif model_type == ModelType.STABLE_CASCADE:
         c = EPS
         s = StableCascadeSampling
@@ -557,3 +563,23 @@ class StableCascade_B(BaseModel):
         out["effnet"] = comfy.conds.CONDRegular(prior)
         out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
         return out
+
+
+class SD3(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper)
+
+    def encode_adm(self, **kwargs):
+        return kwargs["pooled_output"]
+
+    def extra_conds(self, **kwargs):
+        out = {}
+        adm = self.encode_adm(**kwargs)
+        if adm is not None:
+            out['y'] = comfy.conds.CONDRegular(adm)
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
+
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 23358a2c0..dfe0ea995 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -1,5 +1,6 @@
 import comfy.supported_models
 import comfy.supported_models_base
+import math
 import logging
 
 def count_blocks(state_dict_keys, prefix_string):
@@ -26,12 +27,47 @@ def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
         context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
         use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
         time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict
-        return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack
+        time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict
+        return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
     return None
 
 def detect_unet_config(state_dict, key_prefix):
     state_dict_keys = list(state_dict.keys())
 
+    if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model
+        unet_config = {}
+        unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
+        patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
+        unet_config["patch_size"] = patch_size
+        unet_config["out_channels"] = state_dict['{}final_layer.linear.weight'.format(key_prefix)].shape[0] // (patch_size * patch_size)
+
+        unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
+        unet_config["input_size"] = None
+        y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)
+        if y_key in state_dict_keys:
+            unet_config["adm_in_channels"] = state_dict[y_key].shape[1]
+
+        context_key = '{}context_embedder.weight'.format(key_prefix)
+        if context_key in state_dict_keys:
+            in_features = state_dict[context_key].shape[1]
+            out_features = state_dict[context_key].shape[0]
+            unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}}
+        num_patches_key = '{}pos_embed'.format(key_prefix)
+        if num_patches_key in state_dict_keys:
+            num_patches = state_dict[num_patches_key].shape[1]
+            unet_config["num_patches"] = num_patches
+            unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches))
+
+        rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)
+        if rms_qk in state_dict_keys:
+            unet_config["qk_norm"] = "rms"
+
+        unet_config["pos_embed_scaling_factor"] = None #unused for inference
+        context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
+        if context_processor in state_dict_keys:
+            unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
+        return unet_config
+
     if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade
         unet_config = {}
         text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix)
@@ -58,7 +94,6 @@ def detect_unet_config(state_dict, key_prefix):
                 unet_config['nhead'] = [-1, 9, 18, 18]
                 unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]]
                 unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
-
         return unet_config
 
     unet_config = {
@@ -93,6 +128,7 @@ def detect_unet_config(state_dict, key_prefix):
     use_linear_in_transformer = False
 
     video_model = False
+    video_model_cross = False
 
     current_res = 1
     count = 0
@@ -136,6 +172,7 @@ def detect_unet_config(state_dict, key_prefix):
                         context_dim = out[1]
                         use_linear_in_transformer = out[2]
                         video_model = out[3]
+                        video_model_cross = out[4]
                 else:
                     transformer_depth.append(0)
 
@@ -176,6 +213,7 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config["video_kernel_size"] = [3, 1, 1]
         unet_config["use_temporal_resblock"] = True
         unet_config["use_temporal_attention"] = True
+        unet_config["disable_temporal_crossattention"] = not video_model_cross
     else:
         unet_config["use_temporal_resblock"] = False
         unet_config["use_temporal_attention"] = False
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index 37976b326..d6120a83e 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -33,6 +33,19 @@ class EDM(V_PREDICTION):
         sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
+class CONST:
+    def calculate_input(self, sigma, noise):
+        return noise
+
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input - model_output * sigma
+
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        return sigma * noise + (1.0 - sigma) * latent_image
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent / (1.0 - sigma)
 
 class ModelSamplingDiscrete(torch.nn.Module):
     def __init__(self, model_config=None):
@@ -104,6 +117,12 @@ class ModelSamplingDiscrete(torch.nn.Module):
         percent = 1.0 - percent
         return self.sigma(torch.tensor(percent * 999.0)).item()
 
+class ModelSamplingDiscreteEDM(ModelSamplingDiscrete):
+    def timestep(self, sigma):
+        return 0.25 * sigma.log()
+
+    def sigma(self, timestep):
+        return (timestep / 0.25).exp()
 
 class ModelSamplingContinuousEDM(torch.nn.Module):
     def __init__(self, model_config=None):
@@ -149,6 +168,48 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
         log_sigma_min = math.log(self.sigma_min)
         return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
 
+
+def time_snr_shift(alpha, t):
+    if alpha == 1.0:
+        return t
+    return alpha * t / (1 + (alpha - 1) * t)
+
+class ModelSamplingDiscreteFlow(torch.nn.Module):
+    def __init__(self, model_config=None):
+        super().__init__()
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(shift=sampling_settings.get("shift", 1.0))
+
+    def set_parameters(self, shift=1.0, timesteps=1000):
+        self.shift = shift
+        ts = self.sigma(torch.arange(1, timesteps + 1, 1))
+        self.register_buffer('sigmas', ts)
+
+    @property
+    def sigma_min(self):
+        return self.sigmas[0]
+
+    @property
+    def sigma_max(self):
+        return self.sigmas[-1]
+
+    def timestep(self, sigma):
+        return sigma * 1000
+
+    def sigma(self, timestep):
+        return time_snr_shift(self.shift, timestep / 1000)
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 1.0
+        if percent >= 1.0:
+            return 0.0
+        return 1.0 - percent
+
 class StableCascadeSampling(ModelSamplingDiscrete):
     def __init__(self, model_config=None):
         super().__init__()
diff --git a/comfy/sd.py b/comfy/sd.py
index 343d2a02c..cb147fa46 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -19,6 +19,7 @@ from . import model_detection
 from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
+from . import sd3_clip
 
 import comfy.model_patcher
 import comfy.lora
@@ -395,9 +396,12 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
         else:
             clip_target.clip = sd1_clip.SD1ClipModel
             clip_target.tokenizer = sd1_clip.SD1Tokenizer
-    else:
+    elif len(clip_data) == 2:
         clip_target.clip = sdxl_clip.SDXLClipModel
         clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+    elif len(clip_data) == 3:
+        clip_target.clip = sd3_clip.SD3ClipModel
+        clip_target.tokenizer = sd3_clip.SD3Tokenizer
 
     clip = CLIP(clip_target, embedding_directory=embedding_directory)
     for c in clip_data:
diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py
new file mode 100644
index 000000000..bbbf6affd
--- /dev/null
+++ b/comfy/sd3_clip.py
@@ -0,0 +1,91 @@
+from comfy import sd1_clip
+from comfy import sdxl_clip
+from transformers import T5TokenizerFast
+import comfy.t5
+import torch
+import os
+import comfy.model_management
+
+class T5XXLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5)
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
+
+class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+class SDT5XXLModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, **kwargs):
+        super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs)
+
+
+
+class SD3Tokenizer:
+    def __init__(self, embedding_directory=None):
+        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
+        self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        out = {}
+        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.clip_g.untokenize(token_weight_pair)
+
+class SD3ClipModel(torch.nn.Module):
+    def __init__(self, device="cpu", dtype=None):
+        super().__init__()
+        self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False)
+        self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype)
+        self.t5xxl = T5XXLModel(device=device, dtype=dtype)
+
+    def set_clip_options(self, options):
+        self.clip_l.set_clip_options(options)
+        self.clip_g.set_clip_options(options)
+        self.t5xxl.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.clip_g.reset_clip_options()
+        self.clip_l.reset_clip_options()
+        self.t5xxl.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_l = token_weight_pairs["l"]
+        token_weight_pairs_g = token_weight_pairs["g"]
+        token_weight_pars_t5 = token_weight_pairs["t5xxl"]
+        lg_out = None
+        if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
+            l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+            g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+            lg_out = torch.cat([l_out, g_out], dim=-1)
+            lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
+            out = lg_out
+            pooled = torch.cat((l_pooled, g_pooled), dim=-1)
+        else:
+            pooled = torch.zeros((1, 1280 + 768), device=comfy.model_management.intermediate_device())
+
+        t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
+        if lg_out is not None:
+            out = torch.cat([lg_out, t5_out], dim=-2)
+        else:
+            out = t5_out
+
+        return out, pooled
+
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+            return self.clip_g.load_sd(sd)
+        elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+            return self.clip_l.load_sd(sd)
+        else:
+            return self.t5xxl.load_sd(sd)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 6ca32e8ee..6bb76c96f 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -5,6 +5,7 @@ from . import utils
 from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
+from . import sd3_clip
 
 from . import supported_models_base
 from . import latent_formats
@@ -488,6 +489,28 @@ class SDXL_instructpix2pix(SDXL):
     def get_model(self, state_dict, prefix="", device=None):
         return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device)
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p]
+class SD3(supported_models_base.BASE):
+    unet_config = {
+        "in_channels": 16,
+        "pos_embed_scaling_factor": None,
+    }
+
+    sampling_settings = {
+        "shift": 3.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.SD3
+    text_encoder_key_prefix = ["text_encoders."] #TODO?
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.SD3(self, device=device)
+        return out
+
+    def clip_target(self):
+        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.SD3ClipModel) #TODO?
+
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3]
 
 models += [SVD_img2vid]
diff --git a/comfy/t5.py b/comfy/t5.py
new file mode 100644
index 000000000..06dfe4766
--- /dev/null
+++ b/comfy/t5.py
@@ -0,0 +1,231 @@
+import torch
+import math
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+class T5LayerNorm(torch.nn.Module):
+    def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+        self.variance_epsilon = eps
+
+    def forward(self, x):
+        variance = x.pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight.to(device=x.device, dtype=x.dtype) * x
+
+class T5DenseActDense(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, dtype, device, operations):
+        super().__init__()
+        self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x):
+        x = torch.nn.functional.relu(self.wi(x))
+        # x = self.dropout(x)
+        x = self.wo(x)
+        return x
+
+class T5DenseGatedActDense(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, dtype, device, operations):
+        super().__init__()
+        self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x):
+        hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
+        hidden_linear = self.wi_1(x)
+        x = hidden_gelu * hidden_linear
+        # x = self.dropout(x)
+        x = self.wo(x)
+        return x
+
+class T5LayerFF(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+        super().__init__()
+        if ff_activation == "gelu_pytorch_tanh":
+            self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations)
+        elif ff_activation == "relu":
+            self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations)
+
+        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x):
+        forwarded_states = self.layer_norm(x)
+        forwarded_states = self.DenseReluDense(forwarded_states)
+        # x = x + self.dropout(forwarded_states)
+        x += forwarded_states
+        return x
+
+class T5Attention(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+
+        # Mesh TensorFlow initialization to avoid scaling before softmax
+        self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
+        self.num_heads = num_heads
+
+        self.relative_attention_bias = None
+        if relative_attention_bias:
+            self.relative_attention_num_buckets = 32
+            self.relative_attention_max_distance = 128
+            self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
+
+    @staticmethod
+    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+        """
+        Adapted from Mesh Tensorflow:
+        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+        Translate relative position to a bucket number for relative attention. The relative position is defined as
+        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+        This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+        Args:
+            relative_position: an int32 Tensor
+            bidirectional: a boolean - whether the attention is bidirectional
+            num_buckets: an integer
+            max_distance: an integer
+
+        Returns:
+            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+        """
+        relative_buckets = 0
+        if bidirectional:
+            num_buckets //= 2
+            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+            relative_position = torch.abs(relative_position)
+        else:
+            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+        # now relative_position is in the range [0, inf)
+
+        # half of the buckets are for exact increments in positions
+        max_exact = num_buckets // 2
+        is_small = relative_position < max_exact
+
+        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+        relative_position_if_large = max_exact + (
+            torch.log(relative_position.float() / max_exact)
+            / math.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_position_if_large = torch.min(
+            relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+        )
+
+        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+        return relative_buckets
+
+    def compute_bias(self, query_length, key_length, device):
+        """Compute binned relative position bias"""
+        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+        relative_position = memory_position - context_position  # shape (query_length, key_length)
+        relative_position_bucket = self._relative_position_bucket(
+            relative_position,  # shape (query_length, key_length)
+            bidirectional=True,
+            num_buckets=self.relative_attention_num_buckets,
+            max_distance=self.relative_attention_max_distance,
+        )
+        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
+        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
+        return values
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        q = self.q(x)
+        k = self.k(x)
+        v = self.v(x)
+        if self.relative_attention_bias is not None:
+            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
+
+        if past_bias is not None:
+            if mask is not None:
+                mask = mask + past_bias
+            else:
+                mask = past_bias
+
+        out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
+        return self.o(out), past_bias
+
+class T5LayerSelfAttention(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+        self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)
+        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        normed_hidden_states = self.layer_norm(x)
+        output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)
+        # x = x + self.dropout(attention_output)
+        x += output
+        return x, past_bias
+
+class T5Block(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+        self.layer = torch.nn.ModuleList()
+        self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
+        self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations))
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
+        x = self.layer[-1](x)
+        return x, past_bias
+
+class T5Stack(torch.nn.Module):
+    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, num_heads, dtype, device, operations):
+        super().__init__()
+
+        self.block = torch.nn.ModuleList(
+            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
+        )
+        self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
+        mask = None
+        if attention_mask is not None:
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+
+        intermediate = None
+        optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
+        past_bias = None
+        for i, l in enumerate(self.block):
+            x, past_bias = l(x, mask, past_bias, optimized_attention)
+            if i == intermediate_output:
+                intermediate = x.clone()
+        x = self.final_layer_norm(x)
+        if intermediate is not None and final_layer_norm_intermediate:
+            intermediate = self.final_layer_norm(intermediate)
+        return x, intermediate
+
+class T5(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.num_layers = config_dict["num_layers"]
+        model_dim = config_dict["d_model"]
+
+        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations)
+        self.dtype = dtype
+        self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
+
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, embeddings):
+        self.shared = embeddings
+
+    def forward(self, input_ids, *args, **kwargs):
+        x = self.shared(input_ids)
+        return self.encoder(x, *args, **kwargs)
diff --git a/comfy/t5_config_base.json b/comfy/t5_config_base.json
new file mode 100644
index 000000000..facd85ef3
--- /dev/null
+++ b/comfy/t5_config_base.json
@@ -0,0 +1,21 @@
+{
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/t5_config_xxl.json b/comfy/t5_config_xxl.json
new file mode 100644
index 000000000..bf4feadcf
--- /dev/null
+++ b/comfy/t5_config_xxl.json
@@ -0,0 +1,21 @@
+{
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/t5_tokenizer/special_tokens_map.json b/comfy/t5_tokenizer/special_tokens_map.json
new file mode 100644
index 000000000..17ade346a
--- /dev/null
+++ b/comfy/t5_tokenizer/special_tokens_map.json
@@ -0,0 +1,125 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/comfy/t5_tokenizer/tokenizer.json b/comfy/t5_tokenizer/tokenizer.json
new file mode 100644
index 000000000..b11c92d71
--- /dev/null
+++ b/comfy/t5_tokenizer/tokenizer.json
@@ -0,0 +1,129428 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "<pad>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
+      "content": "</s>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2,
+      "content": "<unk>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32000,
+      "content": "<extra_id_99>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32001,
+      "content": "<extra_id_98>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32002,
+      "content": "<extra_id_97>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32003,
+      "content": "<extra_id_96>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32004,
+      "content": "<extra_id_95>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32005,
+      "content": "<extra_id_94>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32006,
+      "content": "<extra_id_93>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32007,
+      "content": "<extra_id_92>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32008,
+      "content": "<extra_id_91>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32009,
+      "content": "<extra_id_90>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32010,
+      "content": "<extra_id_89>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32011,
+      "content": "<extra_id_88>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32012,
+      "content": "<extra_id_87>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32013,
+      "content": "<extra_id_86>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32014,
+      "content": "<extra_id_85>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32015,
+      "content": "<extra_id_84>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32016,
+      "content": "<extra_id_83>",
+      "single_word": false,
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+        "<extra_id_7>",
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+      ],
+      [
+        "<extra_id_6>",
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+      ],
+      [
+        "<extra_id_5>",
+        0.0
+      ],
+      [
+        "<extra_id_4>",
+        0.0
+      ],
+      [
+        "<extra_id_3>",
+        0.0
+      ],
+      [
+        "<extra_id_2>",
+        0.0
+      ],
+      [
+        "<extra_id_1>",
+        0.0
+      ],
+      [
+        "<extra_id_0>",
+        0.0
+      ]
+    ],
+    "byte_fallback": false
+  }
+}
\ No newline at end of file
diff --git a/comfy/t5_tokenizer/tokenizer_config.json b/comfy/t5_tokenizer/tokenizer_config.json
new file mode 100644
index 000000000..02020eb6d
--- /dev/null
+++ b/comfy/t5_tokenizer/tokenizer_config.json
@@ -0,0 +1,939 @@
+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+    },
+    "32098": {
+      "content": "<extra_id_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32099": {
+      "content": "<extra_id_0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "legacy": false,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 21af4b733..64002a8db 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -132,6 +132,32 @@ class ModelSamplingStableCascade:
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )
 
+class ModelSamplingSD3:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "shift": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, shift):
+        m = model.clone()
+
+        sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
+        sampling_type = comfy.model_sampling.CONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift=shift)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
 class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
@@ -213,5 +239,6 @@ NODE_CLASS_MAPPINGS = {
     "ModelSamplingDiscrete": ModelSamplingDiscrete,
     "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
     "ModelSamplingStableCascade": ModelSamplingStableCascade,
+    "ModelSamplingSD3": ModelSamplingSD3,
     "RescaleCFG": RescaleCFG,
 }
diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py
new file mode 100644
index 000000000..80b8644a4
--- /dev/null
+++ b/comfy_extras/nodes_sd3.py
@@ -0,0 +1,87 @@
+import folder_paths
+import comfy.sd
+import comfy.model_management
+import nodes
+import torch
+
+class TripleCLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), )
+                             }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_clip(self, clip_name1, clip_name2, clip_name3):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return (clip,)
+
+class EmptySD3LatentImage:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/sd3"
+
+    def generate(self, width, height, batch_size=1):
+        latent = torch.ones([batch_size, 16, height // 8, width // 8], device=self.device) * 0.0609
+        return ({"samples":latent}, )
+
+class CLIPTextEncodeSD3:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "clip": ("CLIP", ),
+            "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "empty_padding": (["none", "empty_prompt"], )
+            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning"
+
+    def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding):
+        no_padding = empty_padding == "none"
+
+        tokens = clip.tokenize(clip_g)
+        if len(clip_g) == 0 and no_padding:
+            tokens["g"] = []
+
+        if len(clip_l) == 0 and no_padding:
+            tokens["l"] = []
+        else:
+            tokens["l"] = clip.tokenize(clip_l)["l"]
+
+        if len(t5xxl) == 0 and no_padding:
+            tokens["t5xxl"] =  []
+        else:
+            tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+        if len(tokens["l"]) != len(tokens["g"]):
+            empty = clip.tokenize("")
+            while len(tokens["l"]) < len(tokens["g"]):
+                tokens["l"] += empty["l"]
+            while len(tokens["l"]) > len(tokens["g"]):
+                tokens["g"] += empty["g"]
+        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+        return ([[cond, {"pooled_output": pooled}]], )
+
+
+NODE_CLASS_MAPPINGS = {
+    "TripleCLIPLoader": TripleCLIPLoader,
+    "EmptySD3LatentImage": EmptySD3LatentImage,
+    "CLIPTextEncodeSD3": CLIPTextEncodeSD3,
+}
diff --git a/nodes.py b/nodes.py
index b744b53f0..ef1f85613 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1964,6 +1964,7 @@ def init_custom_nodes():
         "nodes_attention_multiply.py",
         "nodes_advanced_samplers.py",
         "nodes_webcam.py",
+        "nodes_sd3.py",
     ]
 
     import_failed = []

From a82fae2375744a73d63268bc4e167649e3f026e0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 10 Jun 2024 16:00:03 -0400
Subject: [PATCH 066/315] Fix bug with cosxl edit model.

---
 comfy/conds.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/comfy/conds.py b/comfy/conds.py
index 23fa48872..660690af8 100644
--- a/comfy/conds.py
+++ b/comfy/conds.py
@@ -29,7 +29,12 @@ class CONDRegular:
 
 class CONDNoiseShape(CONDRegular):
     def process_cond(self, batch_size, device, area, **kwargs):
-        data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
+        data = self.cond
+        if area is not None:
+            dims = len(area) // 2
+            for i in range(dims):
+                data = data.narrow(i + 2, area[i + dims], area[i])
+
         return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
 
 

From 4134564dc15c5eb40a61e2da4c493cf6786e67d1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 11 Jun 2024 06:26:13 -0400
Subject: [PATCH 067/315] Require safetensors library to be at least 0.4.2 for
 fp8 support.

---
 requirements.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/requirements.txt b/requirements.txt
index 906b96eda..8f681f8fd 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,7 @@ torchsde
 torchvision
 einops
 transformers>=4.25.1
-safetensors>=0.3.0
+safetensors>=0.4.2
 aiohttp
 pyyaml
 Pillow

From 73ce178021338e2cea419a00ae61ec0a6630ef19 Mon Sep 17 00:00:00 2001
From: Dango233 <zhengyizhou@outlook.com>
Date: Tue, 11 Jun 2024 18:30:25 +0800
Subject: [PATCH 068/315] Remove redundancy in mmdit.py (#3685)

---
 comfy/ldm/modules/diffusionmodules/mmdit.py | 61 ---------------------
 1 file changed, 61 deletions(-)

diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
index 5e7afc8d3..be40ab940 100644
--- a/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -835,72 +835,11 @@ class MMDiT(nn.Module):
         )
 
         self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
-        # self.initialize_weights()
 
         if compile_core:
             assert False
             self.forward_core_with_concat = torch.compile(self.forward_core_with_concat)
 
-    def initialize_weights(self):
-        # TODO: Init context_embedder?
-        # Initialize transformer layers:
-        def _basic_init(module):
-            if isinstance(module, nn.Linear):
-                torch.nn.init.xavier_uniform_(module.weight)
-                if module.bias is not None:
-                    nn.init.constant_(module.bias, 0)
-
-        self.apply(_basic_init)
-
-        # Initialize (and freeze) pos_embed by sin-cos embedding
-        if self.pos_embed is not None:
-            pos_embed_grid_size = (
-                int(self.x_embedder.num_patches**0.5)
-                if self.pos_embed_max_size is None
-                else self.pos_embed_max_size
-            )
-            pos_embed = get_2d_sincos_pos_embed(
-                self.pos_embed.shape[-1],
-                int(self.x_embedder.num_patches**0.5),
-                pos_embed_grid_size,
-                scaling_factor=self.pos_embed_scaling_factor,
-                offset=self.pos_embed_offset,
-            )
-
-
-            pos_embed = get_2d_sincos_pos_embed(
-                self.pos_embed.shape[-1],
-                int(self.pos_embed.shape[-2]**0.5),
-                scaling_factor=self.pos_embed_scaling_factor,
-            )
-            self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0))
-
-        # Initialize patch_embed like nn.Linear (instead of nn.Conv2d)
-        w = self.x_embedder.proj.weight.data
-        nn.init.xavier_uniform_(w.view([w.shape[0], -1]))
-        nn.init.constant_(self.x_embedder.proj.bias, 0)
-
-        if hasattr(self, "y_embedder"):
-            nn.init.normal_(self.y_embedder.mlp[0].weight, std=0.02)
-            nn.init.normal_(self.y_embedder.mlp[2].weight, std=0.02)
-
-        # Initialize timestep embedding MLP:
-        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
-        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)
-
-        # Zero-out adaLN modulation layers in DiT blocks:
-        for block in self.joint_blocks:
-            nn.init.constant_(block.x_block.adaLN_modulation[-1].weight, 0)
-            nn.init.constant_(block.x_block.adaLN_modulation[-1].bias, 0)
-            nn.init.constant_(block.context_block.adaLN_modulation[-1].weight, 0)
-            nn.init.constant_(block.context_block.adaLN_modulation[-1].bias, 0)
-
-        # Zero-out output layers:
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].weight, 0)
-        nn.init.constant_(self.final_layer.adaLN_modulation[-1].bias, 0)
-        nn.init.constant_(self.final_layer.linear.weight, 0)
-        nn.init.constant_(self.final_layer.linear.bias, 0)
-
     def cropped_pos_embed(self, hw, device=None):
         p = self.x_embedder.patch_size[0]
         h, w = hw

From 9424522ead16a36199c50217ae09093ff8e3223d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 11 Jun 2024 07:20:26 -0400
Subject: [PATCH 069/315] Reuse code.

---
 comfy/model_base.py | 7 +------
 1 file changed, 1 insertion(+), 6 deletions(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index a26b442b1..28458bbab 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -573,13 +573,8 @@ class SD3(BaseModel):
         return kwargs["pooled_output"]
 
     def extra_conds(self, **kwargs):
-        out = {}
-        adm = self.encode_adm(**kwargs)
-        if adm is not None:
-            out['y'] = comfy.conds.CONDRegular(adm)
-
+        out = super().extra_conds(**kwargs)
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
-

From 1c34d338d7cc11513023080cd0adedbf9f997356 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 11 Jun 2024 07:37:22 -0400
Subject: [PATCH 070/315] Update EmptySD3LatentImage to use 1024 resolution by
 default.

---
 comfy_extras/nodes_sd3.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py
index 80b8644a4..d0303aec5 100644
--- a/comfy_extras/nodes_sd3.py
+++ b/comfy_extras/nodes_sd3.py
@@ -27,8 +27,8 @@ class EmptySD3LatentImage:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "width": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
-                              "height": ("INT", {"default": 512, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+        return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"

From 5889b7ca0ad7b3bf036999330b0c5371ce0da0f3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 11 Jun 2024 13:14:43 -0400
Subject: [PATCH 071/315] Support multiple text encoder configurations on SD3.

---
 comfy/sd.py               |  2 +-
 comfy/sd3_clip.py         | 89 ++++++++++++++++++++++++++++-----------
 comfy/supported_models.py | 35 ++++++++++-----
 3 files changed, 91 insertions(+), 35 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index cb147fa46..11764077a 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -482,7 +482,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         vae = VAE(sd=vae_sd)
 
     if output_clip:
-        clip_target = model_config.clip_target()
+        clip_target = model_config.clip_target(state_dict=sd)
         if clip_target is not None:
             clip_sd = model_config.process_clip_state_dict(sd)
             if len(clip_sd) > 0:
diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py
index bbbf6affd..595381fc0 100644
--- a/comfy/sd3_clip.py
+++ b/comfy/sd3_clip.py
@@ -5,6 +5,7 @@ import comfy.t5
 import torch
 import os
 import comfy.model_management
+import logging
 
 class T5XXLModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
@@ -43,42 +44,82 @@ class SD3Tokenizer:
         return self.clip_g.untokenize(token_weight_pair)
 
 class SD3ClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None):
+    def __init__(self, clip_l=True, clip_g=True, t5=True, device="cpu", dtype=None):
         super().__init__()
-        self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False)
-        self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype)
-        self.t5xxl = T5XXLModel(device=device, dtype=dtype)
+        if clip_l:
+            self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False)
+        else:
+            self.clip_l = None
+
+        if clip_g:
+            self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype)
+        else:
+            self.clip_g = None
+
+        if t5:
+            self.t5xxl = T5XXLModel(device=device, dtype=dtype)
+        else:
+            self.t5xxl = None
+
+        logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}".format(clip_l, clip_g, t5))
 
     def set_clip_options(self, options):
-        self.clip_l.set_clip_options(options)
-        self.clip_g.set_clip_options(options)
-        self.t5xxl.set_clip_options(options)
+        if self.clip_l is not None:
+            self.clip_l.set_clip_options(options)
+        if self.clip_g is not None:
+            self.clip_g.set_clip_options(options)
+        if self.t5xxl is not None:
+            self.t5xxl.set_clip_options(options)
 
     def reset_clip_options(self):
-        self.clip_g.reset_clip_options()
-        self.clip_l.reset_clip_options()
-        self.t5xxl.reset_clip_options()
+        if self.clip_l is not None:
+            self.clip_l.reset_clip_options()
+        if self.clip_g is not None:
+            self.clip_g.reset_clip_options()
+        if self.t5xxl is not None:
+            self.t5xxl.reset_clip_options()
 
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs_l = token_weight_pairs["l"]
         token_weight_pairs_g = token_weight_pairs["g"]
         token_weight_pars_t5 = token_weight_pairs["t5xxl"]
         lg_out = None
-        if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
-            l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
-            g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
-            lg_out = torch.cat([l_out, g_out], dim=-1)
-            lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
-            out = lg_out
-            pooled = torch.cat((l_pooled, g_pooled), dim=-1)
-        else:
-            pooled = torch.zeros((1, 1280 + 768), device=comfy.model_management.intermediate_device())
+        pooled = None
+        out = None
 
-        t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
-        if lg_out is not None:
-            out = torch.cat([lg_out, t5_out], dim=-2)
-        else:
-            out = t5_out
+        if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
+            if self.clip_l is not None:
+                lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+            else:
+                l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
+
+            if self.clip_g is not None:
+                g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+                if lg_out is not None:
+                    lg_out = torch.cat([lg_out, g_out], dim=-1)
+                else:
+                    lg_out = torch.nn.functional.pad(g_out, (768, 0))
+            else:
+                g_out = None
+                g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
+
+            if lg_out is not None:
+                lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
+                out = lg_out
+            pooled = torch.cat((l_pooled, g_pooled), dim=-1)
+
+        if self.t5xxl is not None:
+            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
+            if lg_out is not None:
+                out = torch.cat([lg_out, t5_out], dim=-2)
+            else:
+                out = t5_out
+
+        if out is None:
+            out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
+
+        if pooled is None:
+            pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
 
         return out, pooled
 
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 6bb76c96f..481ecaa62 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -54,7 +54,7 @@ class SD15(supported_models_base.BASE):
         replace_prefix = {"clip_l.": "cond_stage_model."}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
 
 class SD20(supported_models_base.BASE):
@@ -97,7 +97,7 @@ class SD20(supported_models_base.BASE):
         state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
         return state_dict
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
 
 class SD21UnclipL(SD20):
@@ -159,7 +159,7 @@ class SDXLRefiner(supported_models_base.BASE):
         state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
         return state_dict_g
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
 
 class SDXL(supported_models_base.BASE):
@@ -228,7 +228,7 @@ class SDXL(supported_models_base.BASE):
         state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
         return state_dict_g
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
 
 class SSD1B(SDXL):
@@ -299,7 +299,7 @@ class SVD_img2vid(supported_models_base.BASE):
         out = model_base.SVD_img2vid(self, device=device)
         return out
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return None
 
 class SV3D_u(SVD_img2vid):
@@ -365,7 +365,7 @@ class Stable_Zero123(supported_models_base.BASE):
         out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"])
         return out
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return None
 
 class SD_X4Upscaler(SD20):
@@ -439,7 +439,7 @@ class Stable_Cascade_C(supported_models_base.BASE):
         out = model_base.StableCascade_C(self, device=device)
         return out
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel)
 
 class Stable_Cascade_B(Stable_Cascade_C):
@@ -501,14 +501,29 @@ class SD3(supported_models_base.BASE):
 
     unet_extra_config = {}
     latent_format = latent_formats.SD3
-    text_encoder_key_prefix = ["text_encoders."] #TODO?
+    text_encoder_key_prefix = ["text_encoders."]
 
     def get_model(self, state_dict, prefix="", device=None):
         out = model_base.SD3(self, device=device)
         return out
 
-    def clip_target(self):
-        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.SD3ClipModel) #TODO?
+    def clip_target(self, state_dict={}):
+        clip_l = False
+        clip_g = False
+        t5 = False
+        pref = self.text_encoder_key_prefix[0]
+        if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
+            clip_l = True
+        if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
+            clip_g = True
+        if "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref) in state_dict:
+            t5 = True
+
+        class SD3ClipModel(sd3_clip.SD3ClipModel):
+            def __init__(self, device="cpu", dtype=None):
+                super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, device=device, dtype=dtype)
+
+        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, SD3ClipModel)
 
 
 models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3]

From 0e49211a110fff099ebafa927ee7b9416ff9feaa Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 11 Jun 2024 17:03:26 -0400
Subject: [PATCH 072/315] Load the SD3 T5xxl model in the same dtype stored in
 the checkpoint.

---
 comfy/model_management.py | 17 +++++++++++++++++
 comfy/sd.py               |  8 +++++++-
 comfy/sd1_clip.py         |  4 ++++
 comfy/sd3_clip.py         | 18 +++++++++++++++---
 comfy/sdxl_clip.py        |  1 +
 comfy/supported_models.py |  7 +++++--
 6 files changed, 49 insertions(+), 6 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 57aa8bca2..dbd0dbac6 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -639,6 +639,23 @@ def supports_dtype(device, dtype): #TODO
         return True
     return False
 
+def supports_cast(device, dtype): #TODO
+    if dtype == torch.float32:
+        return True
+    if dtype == torch.float16:
+        return True
+    if is_device_mps(device):
+        return False
+    if directml_enabled: #TODO: test this
+        return False
+    if dtype == torch.bfloat16:
+        return True
+    if dtype == torch.float8_e4m3fn:
+        return True
+    if dtype == torch.float8_e5m2:
+        return True
+    return False
+
 def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
diff --git a/comfy/sd.py b/comfy/sd.py
index 11764077a..a7b4dbcf2 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -98,13 +98,19 @@ class CLIP:
         load_device = model_management.text_encoder_device()
         offload_device = model_management.text_encoder_offload_device()
         params['device'] = offload_device
-        params['dtype'] = model_management.text_encoder_dtype(load_device)
+        dtype = model_management.text_encoder_dtype(load_device)
+        params['dtype'] = dtype
 
         self.cond_stage_model = clip(**(params))
 
+        for dt in self.cond_stage_model.dtypes:
+            if not model_management.supports_cast(load_device, dt):
+                load_device = offload_device
+
         self.tokenizer = tokenizer(embedding_directory=embedding_directory)
         self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
         self.layer_idx = None
+        logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device))
 
     def clone(self):
         n = CLIP(no_init=True)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 2729f14d8..911af0a7e 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -511,6 +511,10 @@ class SD1ClipModel(torch.nn.Module):
         self.clip = "clip_{}".format(self.clip_name)
         setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs))
 
+        self.dtypes = set()
+        if dtype is not None:
+            self.dtypes.add(dtype)
+
     def set_clip_options(self, options):
         getattr(self, self.clip).set_clip_options(options)
 
diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py
index 595381fc0..cbbbe53dd 100644
--- a/comfy/sd3_clip.py
+++ b/comfy/sd3_clip.py
@@ -44,24 +44,36 @@ class SD3Tokenizer:
         return self.clip_g.untokenize(token_weight_pair)
 
 class SD3ClipModel(torch.nn.Module):
-    def __init__(self, clip_l=True, clip_g=True, t5=True, device="cpu", dtype=None):
+    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None):
         super().__init__()
+        self.dtypes = set()
         if clip_l:
             self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False)
+            self.dtypes.add(dtype)
         else:
             self.clip_l = None
 
         if clip_g:
             self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype)
+            self.dtypes.add(dtype)
         else:
             self.clip_g = None
 
         if t5:
-            self.t5xxl = T5XXLModel(device=device, dtype=dtype)
+            if dtype_t5 is None:
+                dtype_t5 = dtype
+            elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype):
+                dtype_t5 = dtype
+
+            if not comfy.model_management.supports_cast(device, dtype_t5):
+                dtype_t5 = dtype
+
+            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
+            self.dtypes.add(dtype_t5)
         else:
             self.t5xxl = None
 
-        logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}".format(clip_l, clip_g, t5))
+        logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5))
 
     def set_clip_options(self, options):
         if self.clip_l is not None:
diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py
index e62d1ed86..1257cba1e 100644
--- a/comfy/sdxl_clip.py
+++ b/comfy/sdxl_clip.py
@@ -39,6 +39,7 @@ class SDXLClipModel(torch.nn.Module):
         super().__init__()
         self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False)
         self.clip_g = SDXLClipG(device=device, dtype=dtype)
+        self.dtypes = set([dtype])
 
     def set_clip_options(self, options):
         self.clip_l.set_clip_options(options)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 481ecaa62..a49df7a35 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -511,17 +511,20 @@ class SD3(supported_models_base.BASE):
         clip_l = False
         clip_g = False
         t5 = False
+        dtype_t5 = None
         pref = self.text_encoder_key_prefix[0]
         if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
             clip_l = True
         if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
             clip_g = True
-        if "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref) in state_dict:
+        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        if t5_key in state_dict:
             t5 = True
+            dtype_t5 = state_dict[t5_key].dtype
 
         class SD3ClipModel(sd3_clip.SD3ClipModel):
             def __init__(self, device="cpu", dtype=None):
-                super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, device=device, dtype=dtype)
+                super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype)
 
         return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, SD3ClipModel)
 

From 69c8d6d8a60ac6355d02a8a003d55e304fcc702d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 11 Jun 2024 23:27:39 -0400
Subject: [PATCH 073/315] Single and dual clip loader nodes support SD3.

You can use the CLIPLoader to use the t5xxl only or the DualCLIPLoader to
use CLIP-L and CLIP-G only for sd3.
---
 comfy/sd.py               | 13 +++++++++++--
 comfy/sd3_clip.py         |  6 ++++++
 comfy/supported_models.py |  6 +-----
 nodes.py                  | 17 +++++++++++++----
 4 files changed, 31 insertions(+), 11 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index a7b4dbcf2..3fd9e0e98 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -370,6 +370,7 @@ def load_style_model(ckpt_path):
 class CLIPType(Enum):
     STABLE_DIFFUSION = 1
     STABLE_CASCADE = 2
+    SD3 = 3
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
@@ -399,12 +400,20 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
         elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
             clip_target.clip = sd2_clip.SD2ClipModel
             clip_target.tokenizer = sd2_clip.SD2Tokenizer
+        elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
+            dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype
+            clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
+            clip_target.tokenizer = sd3_clip.SD3Tokenizer
         else:
             clip_target.clip = sd1_clip.SD1ClipModel
             clip_target.tokenizer = sd1_clip.SD1Tokenizer
     elif len(clip_data) == 2:
-        clip_target.clip = sdxl_clip.SDXLClipModel
-        clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+        if clip_type == CLIPType.SD3:
+            clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
+            clip_target.tokenizer = sd3_clip.SD3Tokenizer
+        else:
+            clip_target.clip = sdxl_clip.SDXLClipModel
+            clip_target.tokenizer = sdxl_clip.SDXLTokenizer
     elif len(clip_data) == 3:
         clip_target.clip = sd3_clip.SD3ClipModel
         clip_target.tokenizer = sd3_clip.SD3Tokenizer
diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py
index cbbbe53dd..0713eb285 100644
--- a/comfy/sd3_clip.py
+++ b/comfy/sd3_clip.py
@@ -142,3 +142,9 @@ class SD3ClipModel(torch.nn.Module):
             return self.clip_l.load_sd(sd)
         else:
             return self.t5xxl.load_sd(sd)
+
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None):
+    class SD3ClipModel_(SD3ClipModel):
+        def __init__(self, device="cpu", dtype=None):
+            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype)
+    return SD3ClipModel_
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index a49df7a35..c8ddf3e2c 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -522,11 +522,7 @@ class SD3(supported_models_base.BASE):
             t5 = True
             dtype_t5 = state_dict[t5_key].dtype
 
-        class SD3ClipModel(sd3_clip.SD3ClipModel):
-            def __init__(self, device="cpu", dtype=None):
-                super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype)
-
-        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, SD3ClipModel)
+        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
 
 
 models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3]
diff --git a/nodes.py b/nodes.py
index ef1f85613..6fbeb377e 100644
--- a/nodes.py
+++ b/nodes.py
@@ -818,7 +818,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
-                              "type": (["stable_diffusion", "stable_cascade"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -829,6 +829,8 @@ class CLIPLoader:
         clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         if type == "stable_cascade":
             clip_type = comfy.sd.CLIPType.STABLE_CASCADE
+        elif type == "sd3":
+            clip_type = comfy.sd.CLIPType.SD3
 
         clip_path = folder_paths.get_full_path("clip", clip_name)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
@@ -837,17 +839,24 @@ class CLIPLoader:
 class DualCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
+                              "clip_name2": (folder_paths.get_filename_list("clip"), ),
+                              "type": (["sdxl", "sd3"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
     CATEGORY = "advanced/loaders"
 
-    def load_clip(self, clip_name1, clip_name2):
+    def load_clip(self, clip_name1, clip_name2, type):
         clip_path1 = folder_paths.get_full_path("clip", clip_name1)
         clip_path2 = folder_paths.get_full_path("clip", clip_name2)
-        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        if type == "sdxl":
+            clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
+        elif type == "sd3":
+            clip_type = comfy.sd.CLIPType.SD3
+
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)
 
 class CLIPVisionLoader:

From 694e0b48e0f6d55ddfcbe44a5d2818774241077b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 12 Jun 2024 00:49:00 -0400
Subject: [PATCH 074/315] SD3 better memory usage estimation.

---
 comfy/model_base.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index 28458bbab..7ef034408 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -578,3 +578,15 @@ class SD3(BaseModel):
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
+
+    def memory_required(self, input_shape):
+        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
+            dtype = self.get_dtype()
+            if self.manual_cast_dtype is not None:
+                dtype = self.manual_cast_dtype
+            #TODO: this probably needs to be tweaked
+            area = input_shape[0] * input_shape[2] * input_shape[3]
+            return (area * comfy.model_management.dtype_size(dtype) * 0.012) * (1024 * 1024)
+        else:
+            area = input_shape[0] * input_shape[2] * input_shape[3]
+            return (area * 0.3) * (1024 * 1024)

From 32be358213bf14a342073eca6fc45623d07e6267 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 12 Jun 2024 01:02:07 -0400
Subject: [PATCH 075/315] Save SD3 modelspec.architecture in CheckpointSave
 node.

---
 comfy_extras/nodes_model_merging.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py
index bb15112f4..b0d149c60 100644
--- a/comfy_extras/nodes_model_merging.py
+++ b/comfy_extras/nodes_model_merging.py
@@ -183,6 +183,8 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
         metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
     elif isinstance(model.model, comfy.model_base.SVD_img2vid):
         metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1"
+    elif isinstance(model.model, comfy.model_base.SD3):
+        metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants
     else:
         enable_modelspec = False
 

From 1ddf512fdc69bf8dfb51eb858d3e5ba069570791 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 12 Jun 2024 01:07:58 -0400
Subject: [PATCH 076/315] Don't auto convert clip and vae weights to fp16 when
 saving checkpoint.

---
 comfy/model_base.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index 7ef034408..21f884ba2 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -207,9 +207,6 @@ class BaseModel(torch.nn.Module):
         unet_state_dict = self.diffusion_model.state_dict()
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
 
-        if self.get_dtype() == torch.float16:
-            extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds)
-
         if self.model_type == ModelType.V_PREDICTION:
             unet_state_dict["v_pred"] = torch.tensor([])
 

From c8b5e08dc39171babb5d43f160cc04271591743e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 12 Jun 2024 02:24:39 -0400
Subject: [PATCH 077/315] Default shift value on SD3 is 3.0

---
 comfy_extras/nodes_model_advanced.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 64002a8db..9bcd3c397 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -136,7 +136,7 @@ class ModelSamplingSD3:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "shift": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}),
                               }}
 
     RETURN_TYPES = ("MODEL",)

From 321e509e0a8a143095d29969ef9f1b32c9a93c9f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 12 Jun 2024 09:48:27 -0400
Subject: [PATCH 078/315] Add link to SD3 example page to README.

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index de0c062ae..a40dd07dd 100644
--- a/README.md
+++ b/README.md
@@ -11,7 +11,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
-- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/) and [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)
+- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) and [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram)

From 0eaa34ec5b22fd305159a78ea92b2ef00105ab18 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 12 Jun 2024 10:32:34 -0400
Subject: [PATCH 079/315] Fix regular empty latent image not working with SD3
 and custom sampler.

---
 comfy_extras/nodes_custom_sampler.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
index 45ef8cf40..69f1b9418 100644
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -380,7 +380,10 @@ class SamplerCustom:
     def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image):
         latent = latent_image
         latent_image = latent["samples"]
+        latent = latent.copy()
         latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+        latent["samples"] = latent_image
+
         if not add_noise:
             noise = Noise_EmptyNoise().generate_noise(latent)
         else:
@@ -539,7 +542,9 @@ class SamplerCustomAdvanced:
     def sample(self, noise, guider, sampler, sigmas, latent_image):
         latent = latent_image
         latent_image = latent["samples"]
+        latent = latent.copy()
         latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+        latent["samples"] = latent_image
 
         noise_mask = None
         if "noise_mask" in latent:

From 605e64f6d3da44235498bf9103d7aab1c95ef211 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 12 Jun 2024 10:39:33 -0400
Subject: [PATCH 080/315] Fix lowvram issue.

---
 comfy/ldm/modules/diffusionmodules/mmdit.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
index be40ab940..0cb6bd312 100644
--- a/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -934,7 +934,7 @@ class MMDiT(nn.Module):
             context = self.context_processor(context)
 
         hw = x.shape[-2:]
-        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype)
+        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
         c = self.t_embedder(t, dtype=x.dtype)  # (N, D)
         if y is not None and self.y_embedder is not None:
             y = self.y_embedder(y)  # (N, D)

From 37a08a41b3861e3b52dd69ff6b7fb00ba6b43758 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 13 Jun 2024 17:12:50 -0400
Subject: [PATCH 081/315] Support setting weight offsets in weight patcher.

---
 comfy/model_patcher.py | 24 ++++++++++++++++++++----
 1 file changed, 20 insertions(+), 4 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 84592f931..2f80ae2b4 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -209,11 +209,18 @@ class ModelPatcher:
         p = set()
         model_sd = self.model.state_dict()
         for k in patches:
-            if k in model_sd:
+            offset = None
+            if isinstance(k, str):
+                key = k
+            else:
+                offset = k[1]
+                key = k[0]
+
+            if key in model_sd:
                 p.add(k)
-                current_patches = self.patches.get(k, [])
-                current_patches.append((strength_patch, patches[k], strength_model))
-                self.patches[k] = current_patches
+                current_patches = self.patches.get(key, [])
+                current_patches.append((strength_patch, patches[k], strength_model, offset))
+                self.patches[key] = current_patches
 
         self.patches_uuid = uuid.uuid4()
         return list(p)
@@ -339,6 +346,12 @@ class ModelPatcher:
             strength = p[0]
             v = p[1]
             strength_model = p[2]
+            offset = p[3]
+
+            old_weight = None
+            if offset is not None:
+                old_weight = weight
+                weight = weight.narrow(offset[0], offset[1], offset[2])
 
             if strength_model != 1.0:
                 weight *= strength_model
@@ -488,6 +501,9 @@ class ModelPatcher:
             else:
                 logging.warning("patch type not recognized {} {}".format(patch_type, key))
 
+            if old_weight is not None:
+                weight = old_weight
+
         return weight
 
     def unpatch_model(self, device_to=None, unpatch_weights=True):

From ac151ac1698624bc4e321addb8c126069aced4b0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 13 Jun 2024 18:26:01 -0400
Subject: [PATCH 082/315] Support SD3 diffusers lora.

---
 comfy/lora.py | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/comfy/lora.py b/comfy/lora.py
index 096285bba..37254b03f 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -29,6 +29,7 @@ def load_lora(lora, to_load):
 
         regular_lora = "{}.lora_up.weight".format(x)
         diffusers_lora = "{}_lora.up.weight".format(x)
+        diffusers2_lora = "{}.lora_B.weight".format(x)
         transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
         A_name = None
 
@@ -40,6 +41,10 @@ def load_lora(lora, to_load):
             A_name = diffusers_lora
             B_name = "{}_lora.down.weight".format(x)
             mid_name = None
+        elif diffusers2_lora in lora.keys():
+            A_name = diffusers2_lora
+            B_name = "{}.lora_A.weight".format(x)
+            mid_name = None
         elif transformers_lora in lora.keys():
             A_name = transformers_lora
             B_name ="{}.lora_linear_layer.down.weight".format(x)
@@ -164,6 +169,7 @@ def load_lora(lora, to_load):
     for x in lora.keys():
         if x not in loaded_keys:
             logging.warning("lora key not loaded: {}".format(x))
+
     return patch_dict
 
 def model_lora_keys_clip(model, key_map={}):
@@ -217,7 +223,8 @@ def model_lora_keys_clip(model, key_map={}):
     return key_map
 
 def model_lora_keys_unet(model, key_map={}):
-    sdk = model.state_dict().keys()
+    sd = model.state_dict()
+    sdk = sd.keys()
 
     for k in sdk:
         if k.startswith("diffusion_model.") and k.endswith(".weight"):
@@ -238,4 +245,17 @@ def model_lora_keys_unet(model, key_map={}):
                 if diffusers_lora_key.endswith(".to_out.0"):
                     diffusers_lora_key = diffusers_lora_key[:-2]
                 key_map[diffusers_lora_key] = unet_key
+
+    if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
+        for i in range(model.model_config.unet_config.get("depth", 0)):
+            k = "transformer.transformer_blocks.{}.attn.".format(i)
+            qkv = "diffusion_model.joint_blocks.{}.x_block.attn.qkv.weight".format(i)
+            proj = "diffusion_model.joint_blocks.{}.x_block.attn.proj.weight".format(i)
+            if qkv in sd:
+                offset = sd[qkv].shape[0] // 3
+                key_map["{}to_q".format(k)] = (qkv, (0, 0, offset))
+                key_map["{}to_k".format(k)] = (qkv, (0, offset, offset))
+                key_map["{}to_v".format(k)] = (qkv, (0, offset * 2, offset))
+                key_map["{}to_out.0".format(k)] = proj
+
     return key_map

From 5eb98f00927ace00b6b3d01ed9c76b113fc4ec9f Mon Sep 17 00:00:00 2001
From: Simon Lui <502929+simonlui@users.noreply.github.com>
Date: Thu, 13 Jun 2024 15:51:14 -0700
Subject: [PATCH 083/315] Exempt IPEX from non_blocking previews fixing
 segmentation faults. (#3708)

---
 comfy/model_management.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index dbd0dbac6..dbb33d07f 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -659,6 +659,8 @@ def supports_cast(device, dtype): #TODO
 def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
+    if is_intel_xpu():
+        return False
     if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
         return False
     if directml_enabled:

From 0e06b370dbd1c6a8aaf13e9c9cede9ec8925ea86 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 14 Jun 2024 18:18:53 -0400
Subject: [PATCH 084/315] Print key names for easier debugging.

---
 comfy/model_patcher.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 2f80ae2b4..44b82795f 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -335,7 +335,7 @@ class ModelPatcher:
                     self.patch_weight_to_device(bias_key, device_to)
                     m.to(device_to)
                     mem_counter += comfy.model_management.module_size(m)
-                    logging.debug("lowvram: loaded module regularly {}".format(m))
+                    logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
 
         self.model_lowvram = True
         self.lowvram_patch_counter = patch_counter

From 0ec513d8774fc0b14fc3fdbb3f09745244532146 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 15 Jun 2024 01:08:12 -0400
Subject: [PATCH 085/315] Add a --force-channels-last to inference models in
 channel last mode.

---
 comfy/cli_args.py         | 1 +
 comfy/model_base.py       | 3 +++
 comfy/model_management.py | 6 ++++++
 3 files changed, 10 insertions(+)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index b8ac9bc69..fb0d37ce7 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -75,6 +75,7 @@ fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store
 fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
 fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
 
+parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
 
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 21f884ba2..daff6e0f5 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -66,6 +66,9 @@ class BaseModel(torch.nn.Module):
             else:
                 operations = comfy.ops.disable_weight_init
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
+            if comfy.model_management.force_channels_last():
+                self.diffusion_model.to(memory_format=torch.channels_last)
+                logging.debug("using channels last mode for diffusion model")
         self.model_type = model_type
         self.model_sampling = model_sampling(model_config, model_type)
 
diff --git a/comfy/model_management.py b/comfy/model_management.py
index dbb33d07f..8b8d3ff06 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -673,6 +673,12 @@ def device_should_use_non_blocking(device):
     return False
     # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others
 
+def force_channels_last():
+    if args.force_channels_last:
+        return True
+
+    #TODO
+    return False
 
 def cast_to_device(tensor, device, dtype, copy=False):
     device_supports_cast = False

From f2e844e0542ae98b6bfcd438fbc8d22e66f178c9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 15 Jun 2024 02:25:03 -0400
Subject: [PATCH 086/315] Optimize some unneeded if conditions in the sampling
 code.

---
 comfy/k_diffusion/sampling.py | 26 +++++++++++++++++++++-----
 1 file changed, 21 insertions(+), 5 deletions(-)

diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index f9b281894..5bb991e76 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -129,8 +129,13 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        sigma_hat = sigmas[i] * (gamma + 1)
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
             x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
@@ -170,7 +175,13 @@ def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         sigma_hat = sigmas[i] * (gamma + 1)
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
@@ -199,8 +210,13 @@ def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        sigma_hat = sigmas[i] * (gamma + 1)
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
             x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5

From 1281f933c1c38ac0491ff2f86cbcd2ec90743ce3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 15 Jun 2024 02:44:38 -0400
Subject: [PATCH 087/315] Small optimization.

---
 comfy/ldm/modules/diffusionmodules/mmdit.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
index 0cb6bd312..20d3a321a 100644
--- a/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -243,9 +243,9 @@ class TimestepEmbedder(nn.Module):
         half = dim // 2
         freqs = torch.exp(
             -math.log(max_period)
-            * torch.arange(start=0, end=half, dtype=torch.float32)
+            * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
             / half
-        ).to(device=t.device)
+        )
         args = t[:, None].float() * freqs[None]
         embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
         if dim % 2:

From bb1969cab7281199fca8c84b11ba2daa63a2e632 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 15 Jun 2024 12:14:56 -0400
Subject: [PATCH 088/315] Initial support for the stable audio open model.

---
 comfy/latent_formats.py              |   3 +
 comfy/ldm/audio/autoencoder.py       | 276 +++++++++
 comfy/ldm/audio/dit.py               | 888 +++++++++++++++++++++++++++
 comfy/ldm/audio/embedders.py         | 108 ++++
 comfy/ldm/modules/attention.py       | 140 +++--
 comfy/model_base.py                  |  43 +-
 comfy/model_detection.py             |  12 +
 comfy/model_sampling.py              |   8 +
 comfy/ops.py                         |  41 ++
 comfy/sa_t5.py                       |  22 +
 comfy/sd.py                          |  43 +-
 comfy/supported_models.py            |  31 +-
 comfy_extras/nodes_audio.py          | 128 ++++
 comfy_extras/nodes_model_advanced.py |  31 +
 nodes.py                             |   8 +-
 requirements.txt                     |   1 +
 16 files changed, 1719 insertions(+), 64 deletions(-)
 create mode 100644 comfy/ldm/audio/autoencoder.py
 create mode 100644 comfy/ldm/audio/dit.py
 create mode 100644 comfy/ldm/audio/embedders.py
 create mode 100644 comfy/sa_t5.py
 create mode 100644 comfy_extras/nodes_audio.py

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 6a9a96206..92f39d5c2 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -135,3 +135,6 @@ class SD3(LatentFormat):
 
     def process_out(self, latent):
         return (latent / self.scale_factor) + self.shift_factor
+
+class StableAudio1(LatentFormat):
+    latent_channels = 64
diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py
new file mode 100644
index 000000000..7363131e0
--- /dev/null
+++ b/comfy/ldm/audio/autoencoder.py
@@ -0,0 +1,276 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+import torch
+from torch import nn
+from typing import Literal, Dict, Any
+import math
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def vae_sample(mean, scale):
+        stdev = nn.functional.softplus(scale) + 1e-4
+        var = stdev * stdev
+        logvar = torch.log(var)
+        latents = torch.randn_like(mean) * stdev + mean
+
+        kl = (mean * mean + var - logvar - 1).sum(1).mean()
+
+        return latents, kl
+
+class VAEBottleneck(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.is_discrete = False
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        mean, scale = x.chunk(2, dim=1)
+
+        x, kl = vae_sample(mean, scale)
+
+        info["kl"] = kl
+
+        if return_info:
+            return x, info
+        else:
+            return x
+
+    def decode(self, x):
+        return x
+
+
+def snake_beta(x, alpha, beta):
+    return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)
+
+# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
+class SnakeBeta(nn.Module):
+
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
+        super(SnakeBeta, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale: # log scale alphas initialized to zeros
+            self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
+            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
+        else: # linear scale alphas initialized to ones
+            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
+            self.beta = nn.Parameter(torch.ones(in_features) * alpha)
+
+        # self.alpha.requires_grad = alpha_trainable
+        # self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = snake_beta(x, alpha, beta)
+
+        return x
+
+def WNConv1d(*args, **kwargs):
+    return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs))
+
+def WNConvTranspose1d(*args, **kwargs):
+    return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
+
+def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
+    if activation == "elu":
+        act = torch.nn.ELU()
+    elif activation == "snake":
+        act = SnakeBeta(channels)
+    elif activation == "none":
+        act = torch.nn.Identity()
+    else:
+        raise ValueError(f"Unknown activation {activation}")
+
+    if antialias:
+        act = Activation1d(act)
+
+    return act
+
+
+class ResidualUnit(nn.Module):
+    def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):
+        super().__init__()
+
+        self.dilation = dilation
+
+        padding = (dilation * (7-1)) // 2
+
+        self.layers = nn.Sequential(
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=7, dilation=dilation, padding=padding),
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+            WNConv1d(in_channels=out_channels, out_channels=out_channels,
+                      kernel_size=1)
+        )
+
+    def forward(self, x):
+        res = x
+
+        #x = checkpoint(self.layers, x)
+        x = self.layers(x)
+
+        return x + res
+
+class EncoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
+        super().__init__()
+
+        self.layers = nn.Sequential(
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=9, use_snake=use_snake),
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class DecoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):
+        super().__init__()
+
+        if use_nearest_upsample:
+            upsample_layer = nn.Sequential(
+                nn.Upsample(scale_factor=stride, mode="nearest"),
+                WNConv1d(in_channels=in_channels,
+                        out_channels=out_channels,
+                        kernel_size=2*stride,
+                        stride=1,
+                        bias=False,
+                        padding='same')
+            )
+        else:
+            upsample_layer = WNConvTranspose1d(in_channels=in_channels,
+                               out_channels=out_channels,
+                               kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))
+
+        self.layers = nn.Sequential(
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+            upsample_layer,
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=9, use_snake=use_snake),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class OobleckEncoder(nn.Module):
+    def __init__(self,
+                 in_channels=2,
+                 channels=128,
+                 latent_dim=32,
+                 c_mults = [1, 2, 4, 8],
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False
+        ):
+        super().__init__()
+
+        c_mults = [1] + c_mults
+
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
+        ]
+
+        for i in range(self.depth-1):
+            layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
+            WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class OobleckDecoder(nn.Module):
+    def __init__(self,
+                 out_channels=2,
+                 channels=128,
+                 latent_dim=32,
+                 c_mults = [1, 2, 4, 8],
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False,
+                 use_nearest_upsample=False,
+                 final_tanh=True):
+        super().__init__()
+
+        c_mults = [1] + c_mults
+
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
+        ]
+
+        for i in range(self.depth-1, 0, -1):
+            layers += [DecoderBlock(
+                in_channels=c_mults[i]*channels,
+                out_channels=c_mults[i-1]*channels,
+                stride=strides[i-1],
+                use_snake=use_snake,
+                antialias_activation=antialias_activation,
+                use_nearest_upsample=use_nearest_upsample
+                )
+            ]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
+            WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
+            nn.Tanh() if final_tanh else nn.Identity()
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class AudioOobleckVAE(nn.Module):
+    def __init__(self,
+                 in_channels=2,
+                 channels=128,
+                 latent_dim=64,
+                 c_mults = [1, 2, 4, 8, 16],
+                 strides = [2, 4, 4, 8, 8],
+                 use_snake=True,
+                 antialias_activation=False,
+                 use_nearest_upsample=False,
+                 final_tanh=False):
+        super().__init__()
+        self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation)
+        self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation,
+                                      use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh)
+        self.bottleneck = VAEBottleneck()
+
+    def encode(self, x):
+        return self.bottleneck.encode(self.encoder(x))
+
+    def decode(self, x):
+        return self.decoder(self.bottleneck.decode(x))
+
diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py
new file mode 100644
index 000000000..1c1112c5e
--- /dev/null
+++ b/comfy/ldm/audio/dit.py
@@ -0,0 +1,888 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+from comfy.ldm.modules.attention import optimized_attention
+import typing as tp
+
+import torch
+
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+import math
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.weight = nn.Parameter(torch.empty(
+            [out_features // 2, in_features], dtype=dtype, device=device))
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device)
+        return torch.cat([f.cos(), f.sin()], dim=-1)
+
+# norms
+class LayerNorm(nn.Module):
+    def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
+        """
+        bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
+        """
+        super().__init__()
+
+        self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+
+        if bias:
+            self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+        else:
+            self.beta = None
+
+    def forward(self, x):
+        beta = self.beta
+        if self.beta is not None:
+            beta = beta.to(dtype=x.dtype, device=x.device)
+        return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta)
+
+class GLU(nn.Module):
+    def __init__(
+        self,
+        dim_in,
+        dim_out,
+        activation,
+        use_conv = False,
+        conv_kernel_size = 3,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.act = activation
+        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device)
+        self.use_conv = use_conv
+
+    def forward(self, x):
+        if self.use_conv:
+            x = rearrange(x, 'b n d -> b d n')
+            x = self.proj(x)
+            x = rearrange(x, 'b d n -> b n d')
+        else:
+            x = self.proj(x)
+
+        x, gate = x.chunk(2, dim = -1)
+        return x * self.act(gate)
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.max_seq_len = max_seq_len
+        self.emb = nn.Embedding(max_seq_len, dim)
+
+    def forward(self, x, pos = None, seq_start_pos = None):
+        seq_len, device = x.shape[1], x.device
+        assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
+
+        if pos is None:
+            pos = torch.arange(seq_len, device = device)
+
+        if seq_start_pos is not None:
+            pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
+
+        pos_emb = self.emb(pos)
+        pos_emb = pos_emb * self.scale
+        return pos_emb
+
+class ScaledSinusoidalEmbedding(nn.Module):
+    def __init__(self, dim, theta = 10000):
+        super().__init__()
+        assert (dim % 2) == 0, 'dimension must be divisible by 2'
+        self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
+
+        half_dim = dim // 2
+        freq_seq = torch.arange(half_dim).float() / half_dim
+        inv_freq = theta ** -freq_seq
+        self.register_buffer('inv_freq', inv_freq, persistent = False)
+
+    def forward(self, x, pos = None, seq_start_pos = None):
+        seq_len, device = x.shape[1], x.device
+
+        if pos is None:
+            pos = torch.arange(seq_len, device = device)
+
+        if seq_start_pos is not None:
+            pos = pos - seq_start_pos[..., None]
+
+        emb = torch.einsum('i, j -> i j', pos, self.inv_freq)
+        emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
+        return emb * self.scale
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        use_xpos = False,
+        scale_base = 512,
+        interpolation_factor = 1.,
+        base = 10000,
+        base_rescale_factor = 1.
+    ):
+        super().__init__()
+        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+        # has some connection to NTK literature
+        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+        base *= base_rescale_factor ** (dim / (dim - 2))
+
+        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+        assert interpolation_factor >= 1.
+        self.interpolation_factor = interpolation_factor
+
+        if not use_xpos:
+            self.register_buffer('scale', None)
+            return
+
+        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
+
+        self.scale_base = scale_base
+        self.register_buffer('scale', scale)
+
+    def forward_from_seq_len(self, seq_len, device, dtype):
+        # device = self.inv_freq.device
+
+        t = torch.arange(seq_len, device=device, dtype=dtype)
+        return self.forward(t)
+
+    def forward(self, t):
+        # device = self.inv_freq.device
+        device = t.device
+        dtype = t.dtype
+
+        # t = t.to(torch.float32)
+
+        t = t / self.interpolation_factor
+
+        freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device))
+        freqs = torch.cat((freqs, freqs), dim = -1)
+
+        if self.scale is None:
+            return freqs, 1.
+
+        power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
+        scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1')
+        scale = torch.cat((scale, scale), dim = -1)
+
+        return freqs, scale
+
+def rotate_half(x):
+    x = rearrange(x, '... (j d) -> ... j d', j = 2)
+    x1, x2 = x.unbind(dim = -2)
+    return torch.cat((-x2, x1), dim = -1)
+
+def apply_rotary_pos_emb(t, freqs, scale = 1):
+    out_dtype = t.dtype
+
+    # cast to float32 if necessary for numerical stability
+    dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
+    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
+    freqs, t = freqs.to(dtype), t.to(dtype)
+    freqs = freqs[-seq_len:, :]
+
+    if t.ndim == 4 and freqs.ndim == 3:
+        freqs = rearrange(freqs, 'b n d -> b 1 n d')
+
+    # partial rotary embeddings, Wang et al. GPT-J
+    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
+    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
+
+    t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)
+
+    return torch.cat((t, t_unrotated), dim = -1)
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_out = None,
+        mult = 4,
+        no_bias = False,
+        glu = True,
+        use_conv = False,
+        conv_kernel_size = 3,
+        zero_init_output = True,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+
+        # Default to SwiGLU
+
+        activation = nn.SiLU()
+
+        dim_out = dim if dim_out is None else dim_out
+
+        if glu:
+            linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations)
+        else:
+            linear_in = nn.Sequential(
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device),
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                activation
+            )
+
+        linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
+
+        # # init last linear layer to 0
+        # if zero_init_output:
+        #     nn.init.zeros_(linear_out.weight)
+        #     if not no_bias:
+        #         nn.init.zeros_(linear_out.bias)
+
+
+        self.ff = nn.Sequential(
+            linear_in,
+            Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
+            linear_out,
+            Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+        )
+
+    def forward(self, x):
+        return self.ff(x)
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_heads = 64,
+        dim_context = None,
+        causal = False,
+        zero_init_output=True,
+        qk_norm = False,
+        natten_kernel_size = None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.causal = causal
+
+        dim_kv = dim_context if dim_context is not None else dim
+
+        self.num_heads = dim // dim_heads
+        self.kv_heads = dim_kv // dim_heads
+
+        if dim_context is not None:
+            self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+            self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
+        else:
+            self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
+
+        self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        # if zero_init_output:
+        #     nn.init.zeros_(self.to_out.weight)
+
+        self.qk_norm = qk_norm
+
+
+    def forward(
+        self,
+        x,
+        context = None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None,
+        causal = None
+    ):
+        h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
+
+        kv_input = context if has_context else x
+
+        if hasattr(self, 'to_q'):
+            # Use separate linear projections for q and k/v
+            q = self.to_q(x)
+            q = rearrange(q, 'b n (h d) -> b h n d', h = h)
+
+            k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
+        else:
+            # Use fused linear projection
+            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+
+        # Normalize q and k for cosine sim attention
+        if self.qk_norm:
+            q = F.normalize(q, dim=-1)
+            k = F.normalize(k, dim=-1)
+
+        if rotary_pos_emb is not None and not has_context:
+            freqs, _ = rotary_pos_emb
+
+            q_dtype = q.dtype
+            k_dtype = k.dtype
+
+            q = q.to(torch.float32)
+            k = k.to(torch.float32)
+            freqs = freqs.to(torch.float32)
+
+            q = apply_rotary_pos_emb(q, freqs)
+            k = apply_rotary_pos_emb(k, freqs)
+
+            q = q.to(q_dtype)
+            k = k.to(k_dtype)
+
+        input_mask = context_mask
+
+        if input_mask is None and not has_context:
+            input_mask = mask
+
+        # determine masking
+        masks = []
+        final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
+
+        if input_mask is not None:
+            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
+            masks.append(~input_mask)
+
+        # Other masks will be added here later
+
+        if len(masks) > 0:
+            final_attn_mask = ~or_reduce(masks)
+
+        n, device = q.shape[-2], q.device
+
+        causal = self.causal if causal is None else causal
+
+        if n == 1 and causal:
+            causal = False
+
+        if h != kv_h:
+            # Repeat interleave kv_heads to match q_heads
+            heads_per_kv_head = h // kv_h
+            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+
+        out = optimized_attention(q, k, v, h, skip_reshape=True)
+        out = self.to_out(out)
+
+        if mask is not None:
+            mask = rearrange(mask, 'b n -> b n 1')
+            out = out.masked_fill(~mask, 0.)
+
+        return out
+
+class ConformerModule(nn.Module):
+    def __init__(
+        self,
+        dim,
+        norm_kwargs = {},
+    ):
+
+        super().__init__()
+
+        self.dim = dim
+
+        self.in_norm = LayerNorm(dim, **norm_kwargs)
+        self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+        self.glu = GLU(dim, dim, nn.SiLU())
+        self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
+        self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm
+        self.swish = nn.SiLU()
+        self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        x = self.in_norm(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.pointwise_conv(x)
+        x = rearrange(x, 'b d n -> b n d')
+        x = self.glu(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.depthwise_conv(x)
+        x = rearrange(x, 'b d n -> b n d')
+        x = self.mid_norm(x)
+        x = self.swish(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.pointwise_conv_2(x)
+        x = rearrange(x, 'b d n -> b n d')
+
+        return x
+
+class TransformerBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_heads = 64,
+            cross_attend = False,
+            dim_context = None,
+            global_cond_dim = None,
+            causal = False,
+            zero_init_branch_outputs = True,
+            conformer = False,
+            layer_ix = -1,
+            remove_norms = False,
+            attn_kwargs = {},
+            ff_kwargs = {},
+            norm_kwargs = {},
+            dtype=None,
+            device=None,
+            operations=None,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.cross_attend = cross_attend
+        self.dim_context = dim_context
+        self.causal = causal
+
+        self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+
+        self.self_attn = Attention(
+            dim,
+            dim_heads = dim_heads,
+            causal = causal,
+            zero_init_output=zero_init_branch_outputs,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+            **attn_kwargs
+        )
+
+        if cross_attend:
+            self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+            self.cross_attn = Attention(
+                dim,
+                dim_heads = dim_heads,
+                dim_context=dim_context,
+                causal = causal,
+                zero_init_output=zero_init_branch_outputs,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+                **attn_kwargs
+            )
+
+        self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
+
+        self.layer_ix = layer_ix
+
+        self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
+
+        self.global_cond_dim = global_cond_dim
+
+        if global_cond_dim is not None:
+            self.to_scale_shift_gate = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(global_cond_dim, dim * 6, bias=False)
+            )
+
+            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
+            #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
+
+    def forward(
+        self,
+        x,
+        context = None,
+        global_cond=None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None
+    ):
+        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
+
+            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
+
+            # self-attention with adaLN
+            residual = x
+            x = self.pre_norm(x)
+            x = x * (1 + scale_self) + shift_self
+            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
+            x = x * torch.sigmoid(1 - gate_self)
+            x = x + residual
+
+            if context is not None:
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+            if self.conformer is not None:
+                x = x + self.conformer(x)
+
+            # feedforward with adaLN
+            residual = x
+            x = self.ff_norm(x)
+            x = x * (1 + scale_ff) + shift_ff
+            x = self.ff(x)
+            x = x * torch.sigmoid(1 - gate_ff)
+            x = x + residual
+
+        else:
+            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
+
+            if context is not None:
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+            if self.conformer is not None:
+                x = x + self.conformer(x)
+
+            x = x + self.ff(self.ff_norm(x))
+
+        return x
+
+class ContinuousTransformer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        depth,
+        *,
+        dim_in = None,
+        dim_out = None,
+        dim_heads = 64,
+        cross_attend=False,
+        cond_token_dim=None,
+        global_cond_dim=None,
+        causal=False,
+        rotary_pos_emb=True,
+        zero_init_branch_outputs=True,
+        conformer=False,
+        use_sinusoidal_emb=False,
+        use_abs_pos_emb=False,
+        abs_pos_emb_max_length=10000,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs
+        ):
+
+        super().__init__()
+
+        self.dim = dim
+        self.depth = depth
+        self.causal = causal
+        self.layers = nn.ModuleList([])
+
+        self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
+        self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
+
+        if rotary_pos_emb:
+            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
+        else:
+            self.rotary_pos_emb = None
+
+        self.use_sinusoidal_emb = use_sinusoidal_emb
+        if use_sinusoidal_emb:
+            self.pos_emb = ScaledSinusoidalEmbedding(dim)
+
+        self.use_abs_pos_emb = use_abs_pos_emb
+        if use_abs_pos_emb:
+            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
+
+        for i in range(depth):
+            self.layers.append(
+                TransformerBlock(
+                    dim,
+                    dim_heads = dim_heads,
+                    cross_attend = cross_attend,
+                    dim_context = cond_token_dim,
+                    global_cond_dim = global_cond_dim,
+                    causal = causal,
+                    zero_init_branch_outputs = zero_init_branch_outputs,
+                    conformer=conformer,
+                    layer_ix=i,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations,
+                    **kwargs
+                )
+            )
+
+    def forward(
+        self,
+        x,
+        mask = None,
+        prepend_embeds = None,
+        prepend_mask = None,
+        global_cond = None,
+        return_info = False,
+        **kwargs
+    ):
+        batch, seq, device = *x.shape[:2], x.device
+
+        info = {
+            "hidden_states": [],
+        }
+
+        x = self.project_in(x)
+
+        if prepend_embeds is not None:
+            prepend_length, prepend_dim = prepend_embeds.shape[1:]
+
+            assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'
+
+            x = torch.cat((prepend_embeds, x), dim = -2)
+
+            if prepend_mask is not None or mask is not None:
+                mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)
+                prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)
+
+                mask = torch.cat((prepend_mask, mask), dim = -1)
+
+        # Attention layers
+
+        if self.rotary_pos_emb is not None:
+            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
+        else:
+            rotary_pos_emb = None
+
+        if self.use_sinusoidal_emb or self.use_abs_pos_emb:
+            x = x + self.pos_emb(x)
+
+        # Iterate over the transformer layers
+        for layer in self.layers:
+            x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+            # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+
+            if return_info:
+                info["hidden_states"].append(x)
+
+        x = self.project_out(x)
+
+        if return_info:
+            return x, info
+
+        return x
+
+class AudioDiffusionTransformer(nn.Module):
+    def __init__(self,
+        io_channels=64,
+        patch_size=1,
+        embed_dim=1536,
+        cond_token_dim=768,
+        project_cond_tokens=False,
+        global_cond_dim=1536,
+        project_global_cond=True,
+        input_concat_dim=0,
+        prepend_cond_dim=0,
+        depth=24,
+        num_heads=24,
+        transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
+        global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+        audio_model="",
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs):
+
+        super().__init__()
+
+        self.dtype = dtype
+        self.cond_token_dim = cond_token_dim
+
+        # Timestep embeddings
+        timestep_features_dim = 256
+
+        self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
+
+        self.to_timestep_embed = nn.Sequential(
+            operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device),
+        )
+
+        if cond_token_dim > 0:
+            # Conditioning tokens
+
+            cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
+            self.to_cond_embed = nn.Sequential(
+                operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device)
+            )
+        else:
+            cond_embed_dim = 0
+
+        if global_cond_dim > 0:
+            # Global conditioning
+            global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
+            self.to_global_embed = nn.Sequential(
+                operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device)
+            )
+
+        if prepend_cond_dim > 0:
+            # Prepend conditioning
+            self.to_prepend_embed = nn.Sequential(
+                operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
+            )
+
+        self.input_concat_dim = input_concat_dim
+
+        dim_in = io_channels + self.input_concat_dim
+
+        self.patch_size = patch_size
+
+        # Transformer
+
+        self.transformer_type = transformer_type
+
+        self.global_cond_type = global_cond_type
+
+        if self.transformer_type == "continuous_transformer":
+
+            global_dim = None
+
+            if self.global_cond_type == "adaLN":
+                # The global conditioning is projected to the embed_dim already at this point
+                global_dim = embed_dim
+
+            self.transformer = ContinuousTransformer(
+                dim=embed_dim,
+                depth=depth,
+                dim_heads=embed_dim // num_heads,
+                dim_in=dim_in * patch_size,
+                dim_out=io_channels * patch_size,
+                cross_attend = cond_token_dim > 0,
+                cond_token_dim = cond_embed_dim,
+                global_cond_dim=global_dim,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+                **kwargs
+            )
+        else:
+            raise ValueError(f"Unknown transformer type: {self.transformer_type}")
+
+        self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device)
+        self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device)
+
+    def _forward(
+        self,
+        x,
+        t,
+        mask=None,
+        cross_attn_cond=None,
+        cross_attn_cond_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        return_info=False,
+        **kwargs):
+
+        if cross_attn_cond is not None:
+            cross_attn_cond = self.to_cond_embed(cross_attn_cond)
+
+        if global_embed is not None:
+            # Project the global conditioning to the embedding dimension
+            global_embed = self.to_global_embed(global_embed)
+
+        prepend_inputs = None
+        prepend_mask = None
+        prepend_length = 0
+        if prepend_cond is not None:
+            # Project the prepend conditioning to the embedding dimension
+            prepend_cond = self.to_prepend_embed(prepend_cond)
+
+            prepend_inputs = prepend_cond
+            if prepend_cond_mask is not None:
+                prepend_mask = prepend_cond_mask
+
+        if input_concat_cond is not None:
+
+            # Interpolate input_concat_cond to the same length as x
+            if input_concat_cond.shape[2] != x.shape[2]:
+                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+
+            x = torch.cat([x, input_concat_cond], dim=1)
+
+        # Get the batch of timestep embeddings
+        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim)
+
+        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
+        if global_embed is not None:
+            global_embed = global_embed + timestep_embed
+        else:
+            global_embed = timestep_embed
+
+        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
+        if self.global_cond_type == "prepend":
+            if prepend_inputs is None:
+                # Prepend inputs are just the global embed, and the mask is all ones
+                prepend_inputs = global_embed.unsqueeze(1)
+                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
+            else:
+                # Prepend inputs are the prepend conditioning + the global embed
+                prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
+                prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
+
+            prepend_length = prepend_inputs.shape[1]
+
+        x = self.preprocess_conv(x) + x
+
+        x = rearrange(x, "b c t -> b t c")
+
+        extra_args = {}
+
+        if self.global_cond_type == "adaLN":
+            extra_args["global_cond"] = global_embed
+
+        if self.patch_size > 1:
+            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
+
+        if self.transformer_type == "x-transformers":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
+        elif self.transformer_type == "continuous_transformer":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
+
+            if return_info:
+                output, info = output
+        elif self.transformer_type == "mm_transformer":
+            output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs)
+
+        output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:]
+
+        if self.patch_size > 1:
+            output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)
+
+        output = self.postprocess_conv(output) + output
+
+        if return_info:
+            return output, info
+
+        return output
+
+    def forward(
+        self,
+        x,
+        timestep,
+        context=None,
+        context_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        negative_global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        mask=None,
+        return_info=False,
+        control=None,
+        transformer_options={},
+        **kwargs):
+            return self._forward(
+                x,
+                timestep,
+                cross_attn_cond=context,
+                cross_attn_cond_mask=context_mask,
+                input_concat_cond=input_concat_cond,
+                global_embed=global_embed,
+                prepend_cond=prepend_cond,
+                prepend_cond_mask=prepend_cond_mask,
+                mask=mask,
+                return_info=return_info,
+                **kwargs
+            )
diff --git a/comfy/ldm/audio/embedders.py b/comfy/ldm/audio/embedders.py
new file mode 100644
index 000000000..82a3210c6
--- /dev/null
+++ b/comfy/ldm/audio/embedders.py
@@ -0,0 +1,108 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+import torch
+import torch.nn as nn
+from torch import Tensor, einsum
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
+from einops import rearrange
+import math
+import comfy.ops
+
+class LearnedPositionalEmbedding(nn.Module):
+    """Used for continuous time"""
+
+    def __init__(self, dim: int):
+        super().__init__()
+        assert (dim % 2) == 0
+        half_dim = dim // 2
+        self.weights = nn.Parameter(torch.empty(half_dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = rearrange(x, "b -> b 1")
+        freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi
+        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1)
+        fouriered = torch.cat((x, fouriered), dim=-1)
+        return fouriered
+
+def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
+    return nn.Sequential(
+        LearnedPositionalEmbedding(dim),
+        comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features),
+    )
+
+
+class NumberEmbedder(nn.Module):
+    def __init__(
+        self,
+        features: int,
+        dim: int = 256,
+    ):
+        super().__init__()
+        self.features = features
+        self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
+
+    def forward(self, x: Union[List[float], Tensor]) -> Tensor:
+        if not torch.is_tensor(x):
+            device = next(self.embedding.parameters()).device
+            x = torch.tensor(x, device=device)
+        assert isinstance(x, Tensor)
+        shape = x.shape
+        x = rearrange(x, "... -> (...)")
+        embedding = self.embedding(x)
+        x = embedding.view(*shape, self.features)
+        return x  # type: ignore
+
+
+class Conditioner(nn.Module):
+    def __init__(
+            self,
+            dim: int,
+            output_dim: int,
+            project_out: bool = False
+            ):
+
+        super().__init__()
+
+        self.dim = dim
+        self.output_dim = output_dim
+        self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity()
+
+    def forward(self, x):
+        raise NotImplementedError()
+
+class NumberConditioner(Conditioner):
+    '''
+        Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings
+    '''
+    def __init__(self,
+                output_dim: int,
+                min_val: float=0,
+                max_val: float=1
+                ):
+        super().__init__(output_dim, output_dim)
+
+        self.min_val = min_val
+        self.max_val = max_val
+
+        self.embedder = NumberEmbedder(features=output_dim)
+
+    def forward(self, floats, device=None):
+            # Cast the inputs to floats
+            floats = [float(x) for x in floats]
+
+            if device is None:
+                device = next(self.embedder.parameters()).device
+
+            floats = torch.tensor(floats).to(device)
+
+            floats = floats.clamp(self.min_val, self.max_val)
+
+            normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val)
+
+            # Cast floats to same type as embedder
+            embedder_dtype = next(self.embedder.parameters()).dtype
+            normalized_floats = normalized_floats.to(embedder_dtype)
+
+            float_embeds = self.embedder(normalized_floats).unsqueeze(1)
+
+            return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]
diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index da9f7aab7..65a8bcf42 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -86,22 +86,32 @@ class FeedForward(nn.Module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
-def attention_basic(q, k, v, heads, mask=None, attn_precision=None):
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
-    b, _, dim_head = q.shape
-    dim_head //= heads
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
     scale = dim_head ** -0.5
 
     h = heads
-    q, k, v = map(
-        lambda t: t.unsqueeze(3)
-        .reshape(b, -1, heads, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b * heads, -1, dim_head)
-        .contiguous(),
-        (q, k, v),
-    )
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
 
     # force cast to fp32 to avoid overflowing
     if attn_precision == torch.float32:
@@ -138,17 +148,26 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None):
     return out
 
 
-def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None):
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
-    b, _, dim_head = query.shape
-    dim_head //= heads
+    if skip_reshape:
+        b, _, _, dim_head = query.shape
+    else:
+        b, _, dim_head = query.shape
+        dim_head //= heads
 
     scale = dim_head ** -0.5
-    query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-    value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
 
-    key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+    if skip_reshape:
+        query = query.reshape(b * heads, -1, dim_head)
+        value = value.reshape(b * heads, -1, dim_head)
+        key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
+    else:
+        query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+
 
     dtype = query.dtype
     upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32
@@ -200,22 +219,32 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None)
     hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
     return hidden_states
 
-def attention_split(q, k, v, heads, mask=None, attn_precision=None):
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
-    b, _, dim_head = q.shape
-    dim_head //= heads
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
     scale = dim_head ** -0.5
 
     h = heads
-    q, k, v = map(
-        lambda t: t.unsqueeze(3)
-        .reshape(b, -1, heads, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b * heads, -1, dim_head)
-        .contiguous(),
-        (q, k, v),
-    )
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
 
@@ -311,9 +340,12 @@ try:
 except:
     pass
 
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
-    b, _, dim_head = q.shape
-    dim_head //= heads
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
 
     disabled_xformers = False
 
@@ -328,10 +360,16 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
     if disabled_xformers:
         return attention_pytorch(q, k, v, heads, mask)
 
-    q, k, v = map(
-        lambda t: t.reshape(b, -1, heads, dim_head),
-        (q, k, v),
-    )
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.reshape(b, -1, heads, dim_head),
+            (q, k, v),
+        )
 
     if mask is not None:
         pad = 8 - q.shape[1] % 8
@@ -341,18 +379,30 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None):
 
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
 
-    out = (
-        out.reshape(b, -1, heads * dim_head)
-    )
+    if skip_reshape:
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
+    else:
+        out = (
+            out.reshape(b, -1, heads * dim_head)
+        )
+
     return out
 
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None):
-    b, _, dim_head = q.shape
-    dim_head //= heads
-    q, k, v = map(
-        lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
-        (q, k, v),
-    )
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
 
     out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
     out = (
diff --git a/comfy/model_base.py b/comfy/model_base.py
index daff6e0f5..f45b375de 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -6,12 +6,15 @@ from comfy.ldm.cascade.stage_b import StageB
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+import comfy.ldm.audio.dit
+import comfy.ldm.audio.embedders
 import comfy.model_management
 import comfy.conds
 import comfy.ops
 from enum import Enum
 from . import utils
 import comfy.latent_formats
+import math
 
 class ModelType(Enum):
     EPS = 1
@@ -20,9 +23,10 @@ class ModelType(Enum):
     STABLE_CASCADE = 4
     EDM = 5
     FLOW = 6
+    V_PREDICTION_CONTINUOUS = 7
 
 
-from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling
+from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
 
 
 def model_sampling(model_config, model_type):
@@ -44,6 +48,9 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.EDM:
         c = EDM
         s = ModelSamplingContinuousEDM
+    elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
+        c = V_PREDICTION
+        s = ModelSamplingContinuousV
 
     class ModelSampling(s, c):
         pass
@@ -236,11 +243,11 @@ class BaseModel(torch.nn.Module):
             if self.manual_cast_dtype is not None:
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
-            area = input_shape[0] * input_shape[2] * input_shape[3]
+            area = input_shape[0] * math.prod(input_shape[2:])
             return (area * comfy.model_management.dtype_size(dtype) / 50) * (1024 * 1024)
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
-            area = input_shape[0] * input_shape[2] * input_shape[3]
+            area = input_shape[0] * math.prod(input_shape[2:])
             return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
 
 
@@ -590,3 +597,33 @@ class SD3(BaseModel):
         else:
             area = input_shape[0] * input_shape[2] * input_shape[3]
             return (area * 0.3) * (1024 * 1024)
+
+
+class StableAudio1(BaseModel):
+    def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)
+        self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+        self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+        self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights)
+        self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights)
+
+    def extra_conds(self, **kwargs):
+        out = {}
+
+        noise = kwargs.get("noise", None)
+        device = kwargs["device"]
+
+        seconds_start = kwargs.get("seconds_start", 0)
+        seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53))
+
+        seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device)
+        seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device)
+
+        global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1))
+        out['global_embed'] = comfy.conds.CONDRegular(global_embed)
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index dfe0ea995..4843e6a4a 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -96,6 +96,11 @@ def detect_unet_config(state_dict, key_prefix):
                 unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
         return unet_config
 
+    if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
+        unet_config = {}
+        unet_config["audio_model"] = "dit1.0"
+        return unet_config
+
     unet_config = {
         "use_checkpoint": False,
         "image_size": 32,
@@ -236,6 +241,13 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
     else:
         return model_config
 
+def unet_prefix_from_state_dict(state_dict):
+    if "model.model.postprocess_conv.weight" in state_dict: #audio models
+        unet_key_prefix = "model.model."
+    else:
+        unet_key_prefix = "model.diffusion_model."
+    return unet_key_prefix
+
 def convert_config(unet_config):
     new_config = unet_config.copy()
     num_res_blocks = new_config.get("num_res_blocks", None)
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index d6120a83e..6bd3a5d79 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -169,6 +169,14 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
         return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
 
 
+class ModelSamplingContinuousV(ModelSamplingContinuousEDM):
+    def timestep(self, sigma):
+        return sigma.atan() / math.pi * 2
+
+    def sigma(self, timestep):
+        return (timestep * math.pi / 2).tan()
+
+
 def time_snr_shift(alpha, t):
     if alpha == 1.0:
         return t
diff --git a/comfy/ops.py b/comfy/ops.py
index 7ebb3dd2f..0f1ceb574 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -51,6 +51,20 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
+    class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
+
+        def forward_comfy_cast_weights(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return self._conv_forward(input, weight, bias)
+
+        def forward(self, *args, **kwargs):
+            if self.comfy_cast_weights:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
     class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
         def reset_parameters(self):
             return None
@@ -133,6 +147,27 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
+    class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
+
+        def forward_comfy_cast_weights(self, input, output_size=None):
+            num_spatial_dims = 1
+            output_padding = self._output_padding(
+                input, output_size, self.stride, self.padding, self.kernel_size,
+                num_spatial_dims, self.dilation)
+
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.conv_transpose1d(
+                input, weight, bias, self.stride, self.padding,
+                output_padding, self.groups, self.dilation)
+
+        def forward(self, *args, **kwargs):
+            if self.comfy_cast_weights:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
     @classmethod
     def conv_nd(s, dims, *args, **kwargs):
         if dims == 2:
@@ -147,6 +182,9 @@ class manual_cast(disable_weight_init):
     class Linear(disable_weight_init.Linear):
         comfy_cast_weights = True
 
+    class Conv1d(disable_weight_init.Conv1d):
+        comfy_cast_weights = True
+
     class Conv2d(disable_weight_init.Conv2d):
         comfy_cast_weights = True
 
@@ -161,3 +199,6 @@ class manual_cast(disable_weight_init):
 
     class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
         comfy_cast_weights = True
+
+    class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
+        comfy_cast_weights = True
diff --git a/comfy/sa_t5.py b/comfy/sa_t5.py
new file mode 100644
index 000000000..37be5287e
--- /dev/null
+++ b/comfy/sa_t5.py
@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from transformers import T5TokenizerFast
+import comfy.t5
+import os
+
+class T5BaseModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+
+class T5BaseTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128)
+
+class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="t5base", tokenizer=T5BaseTokenizer)
+
+class SAT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, **kwargs):
+        super().__init__(device=device, dtype=dtype, clip_name="t5base", clip_model=T5BaseModel, **kwargs)
diff --git a/comfy/sd.py b/comfy/sd.py
index 3fd9e0e98..f1e487132 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -6,7 +6,7 @@ from comfy import model_management
 from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
 from .ldm.cascade.stage_a import StageA
 from .ldm.cascade.stage_c_coder import StageC_coder
-
+from .ldm.audio.autoencoder import AudioOobleckVAE
 import yaml
 
 import comfy.utils
@@ -20,6 +20,7 @@ from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
 from . import sd3_clip
+from . import sa_t5
 
 import comfy.model_patcher
 import comfy.lora
@@ -174,6 +175,7 @@ class VAE:
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4
+        self.output_channels = 3
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
 
@@ -232,6 +234,16 @@ class VAE:
                     self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
                                                                 encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
                                                                 decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
+            elif "decoder.layers.0.weight_v" in sd:
+                self.first_stage_model = AudioOobleckVAE()
+                self.memory_used_encode = lambda shape, dtype: (1767 * shape[2]) * model_management.dtype_size(dtype) #TODO: tweak for the audio VAE
+                self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * 64) * model_management.dtype_size(dtype)
+                self.latent_channels = 64
+                self.output_channels = 2
+                self.upscale_ratio = 2048
+                self.downscale_ratio =  2048
+                self.process_output = lambda audio: audio
+                self.process_input = lambda audio: audio
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -260,12 +272,12 @@ class VAE:
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
 
     def vae_encode_crop_pixels(self, pixels):
-        x = (pixels.shape[1] // self.downscale_ratio) * self.downscale_ratio
-        y = (pixels.shape[2] // self.downscale_ratio) * self.downscale_ratio
-        if pixels.shape[1] != x or pixels.shape[2] != y:
-            x_offset = (pixels.shape[1] % self.downscale_ratio) // 2
-            y_offset = (pixels.shape[2] % self.downscale_ratio) // 2
-            pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
+        dims = pixels.shape[1:-1]
+        for d in range(len(dims)):
+            x = (dims[d] // self.downscale_ratio) * self.downscale_ratio
+            x_offset = (dims[d] % self.downscale_ratio) // 2
+            if x != dims[d]:
+                pixels = pixels.narrow(d + 1, x_offset, x)
         return pixels
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
@@ -303,7 +315,7 @@ class VAE:
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
-            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.upscale_ratio), round(samples_in.shape[3] * self.upscale_ratio)), device=self.output_device)
+            pixel_samples = torch.empty((samples_in.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples_in.shape[2:])), device=self.output_device)
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
                 pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
@@ -328,7 +340,7 @@ class VAE:
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device)
+            samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
             for x in range(0, pixel_samples.shape[0], batch_number):
                 pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
                 samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
@@ -371,6 +383,7 @@ class CLIPType(Enum):
     STABLE_DIFFUSION = 1
     STABLE_CASCADE = 2
     SD3 = 3
+    STABLE_AUDIO = 4
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
@@ -404,6 +417,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype
             clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
             clip_target.tokenizer = sd3_clip.SD3Tokenizer
+        elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
+            clip_target.clip = sa_t5.SAT5Model
+            clip_target.tokenizer = sa_t5.SAT5Tokenizer
         else:
             clip_target.clip = sd1_clip.SD1ClipModel
             clip_target.tokenizer = sd1_clip.SD1Tokenizer
@@ -470,10 +486,11 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     model_patcher = None
     clip_target = None
 
-    parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.")
+    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
+    parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
     load_device = model_management.get_torch_device()
 
-    model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.")
+    model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix)
     unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
@@ -488,8 +505,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     if output_model:
         inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
         offload_device = model_management.unet_offload_device()
-        model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device)
-        model.load_model_weights(sd, "model.diffusion_model.")
+        model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
+        model.load_model_weights(sd, diffusion_model_prefix)
 
     if output_vae:
         vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index c8ddf3e2c..761498dbc 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -6,6 +6,7 @@ from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
 from . import sd3_clip
+from . import sa_t5
 
 from . import supported_models_base
 from . import latent_formats
@@ -524,7 +525,35 @@ class SD3(supported_models_base.BASE):
 
         return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
 
+class StableAudio(supported_models_base.BASE):
+    unet_config = {
+        "audio_model": "dit1.0",
+    }
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3]
+    sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03}
+
+    unet_extra_config = {}
+    latent_format = latent_formats.StableAudio1
+
+    text_encoder_key_prefix = ["text_encoders."]
+    vae_key_prefix = ["pretransform.model."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True)
+        seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
+        return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)
+
+
+    def process_unet_state_dict(self, state_dict):
+        for k in list(state_dict.keys()):
+            if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero
+                state_dict.pop(k)
+        return state_dict
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
+
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio]
 
 models += [SVD_img2vid]
diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
new file mode 100644
index 000000000..5f4bd3540
--- /dev/null
+++ b/comfy_extras/nodes_audio.py
@@ -0,0 +1,128 @@
+import torchaudio
+import torch
+import comfy.model_management
+import folder_paths
+import os
+
+class EmptyLatentAudio:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "_for_testing/audio"
+
+    def generate(self):
+        batch_size = 1
+        latent = torch.zeros([batch_size, 64, 1024], device=self.device)
+        return ({"samples":latent, "type": "audio"}, )
+
+class VAEEncodeAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "_for_testing/audio"
+
+    def encode(self, vae, audio):
+        t = vae.encode(audio["waveform"].movedim(1, -1))
+        return ({"samples":t}, )
+
+class VAEDecodeAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("AUDIO",)
+    FUNCTION = "decode"
+
+    CATEGORY = "_for_testing/audio"
+
+    def decode(self, vae, samples):
+        audio = vae.decode(samples["samples"]).movedim(-1, 1)
+        return ({"waveform": audio, "sample_rate": 44100}, )
+
+class SaveAudio:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+        self.compress_level = 4
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ),
+                              "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_audio"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing/audio"
+
+    def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        results = list()
+        for (batch_number, waveform) in enumerate(audio["waveform"]):
+            #TODO: metadata
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.flac"
+            torchaudio.save(os.path.join(full_output_folder, file), waveform, audio["sample_rate"], format="FLAC")
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        return { "ui": { "audio": results } }
+
+class LoadAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
+        return {"required": {"audio": [sorted(files), ]}, }
+
+    CATEGORY = "_for_testing/audio"
+
+    RETURN_TYPES = ("AUDIO", )
+    FUNCTION = "load"
+
+    def load(self, audio):
+        audio_path = folder_paths.get_annotated_filepath(audio)
+        waveform, sample_rate = torchaudio.load(audio_path)
+        multiplier = 1.0
+        audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+        return (audio, )
+
+    @classmethod
+    def IS_CHANGED(s, audio):
+        image_path = folder_paths.get_annotated_filepath(audio)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, audio):
+        if not folder_paths.exists_annotated_filepath(audio):
+            return "Invalid audio file: {}".format(audio)
+        return True
+
+NODE_CLASS_MAPPINGS = {
+    "EmptyLatentAudio": EmptyLatentAudio,
+    "VAEEncodeAudio": VAEEncodeAudio,
+    "VAEDecodeAudio": VAEDecodeAudio,
+    "SaveAudio": SaveAudio,
+    "LoadAudio": LoadAudio,
+}
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 9bcd3c397..97559cf56 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -196,6 +196,36 @@ class ModelSamplingContinuousEDM:
             m.add_object_patch("latent_format", latent_format)
         return (m, )
 
+class ModelSamplingContinuousV:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "sampling": (["v_prediction"],),
+                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, sampling, sigma_max, sigma_min):
+        m = model.clone()
+
+        latent_format = None
+        sigma_data = 1.0
+        if sampling == "v_prediction":
+            sampling_type = comfy.model_sampling.V_PREDICTION
+
+        class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
 class RescaleCFG:
     @classmethod
     def INPUT_TYPES(s):
@@ -238,6 +268,7 @@ class RescaleCFG:
 NODE_CLASS_MAPPINGS = {
     "ModelSamplingDiscrete": ModelSamplingDiscrete,
     "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
+    "ModelSamplingContinuousV": ModelSamplingContinuousV,
     "ModelSamplingStableCascade": ModelSamplingStableCascade,
     "ModelSamplingSD3": ModelSamplingSD3,
     "RescaleCFG": RescaleCFG,
diff --git a/nodes.py b/nodes.py
index 6fbeb377e..0b2a96f73 100644
--- a/nodes.py
+++ b/nodes.py
@@ -818,7 +818,7 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -826,11 +826,14 @@ class CLIPLoader:
     CATEGORY = "advanced/loaders"
 
     def load_clip(self, clip_name, type="stable_diffusion"):
-        clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         if type == "stable_cascade":
             clip_type = comfy.sd.CLIPType.STABLE_CASCADE
         elif type == "sd3":
             clip_type = comfy.sd.CLIPType.SD3
+        elif type == "stable_audio":
+            clip_type = comfy.sd.CLIPType.STABLE_AUDIO
+        else:
+            clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
         clip_path = folder_paths.get_full_path("clip", clip_name)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
@@ -1973,6 +1976,7 @@ def init_custom_nodes():
         "nodes_attention_multiply.py",
         "nodes_advanced_samplers.py",
         "nodes_webcam.py",
+        "nodes_audio.py",
         "nodes_sd3.py",
     ]
 
diff --git a/requirements.txt b/requirements.txt
index 8f681f8fd..85e1dc9b0 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,7 @@
 torch
 torchsde
 torchvision
+torchaudio
 einops
 transformers>=4.25.1
 safetensors>=0.4.2

From df7db0e0279f58d9f2f3f33ddb60bb238b6d0dc8 Mon Sep 17 00:00:00 2001
From: "Dr.Lt.Data" <128333288+ltdrdata@users.noreply.github.com>
Date: Sun, 16 Jun 2024 15:03:53 +0900
Subject: [PATCH 089/315] support TAESD3 (#3738)

---
 comfy/latent_formats.py |  1 +
 comfy/sd.py             |  6 +++---
 comfy/taesd/taesd.py    | 15 ++++++++-------
 latent_preview.py       |  2 +-
 nodes.py                | 17 +++++++++++++++--
 5 files changed, 28 insertions(+), 13 deletions(-)

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 92f39d5c2..4b4a9eda2 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -129,6 +129,7 @@ class SD3(LatentFormat):
             [-0.0749, -0.0634, -0.0456],
             [-0.1418, -0.1457, -0.1259]
         ]
+        self.taesd_decoder_name = "taesd3_decoder"
 
     def process_in(self, latent):
         return (latent - self.shift_factor) * self.scale_factor
diff --git a/comfy/sd.py b/comfy/sd.py
index f1e487132..d2720ec13 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -166,7 +166,7 @@ class CLIP:
         return self.patcher.get_key_patches()
 
 class VAE:
-    def __init__(self, sd=None, device=None, config=None, dtype=None):
+    def __init__(self, sd=None, device=None, config=None, dtype=None, latent_channels=4):
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
@@ -174,7 +174,7 @@ class VAE:
         self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
         self.downscale_ratio = 8
         self.upscale_ratio = 8
-        self.latent_channels = 4
+        self.latent_channels = latent_channels
         self.output_channels = 3
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
@@ -189,7 +189,7 @@ class VAE:
                                                             encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
                                                             decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
             elif "taesd_decoder.1.weight" in sd:
-                self.first_stage_model = comfy.taesd.taesd.TAESD()
+                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
             elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
                 self.first_stage_model = StageA()
                 self.downscale_ratio = 4
diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py
index 8f96c54e5..74031c60d 100644
--- a/comfy/taesd/taesd.py
+++ b/comfy/taesd/taesd.py
@@ -25,18 +25,19 @@ class Block(nn.Module):
     def forward(self, x):
         return self.fuse(self.conv(x) + self.skip(x))
 
-def Encoder():
+def Encoder(latent_channels=4):
     return nn.Sequential(
         conv(3, 64), Block(64, 64),
         conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
         conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
         conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, 4),
+        conv(64, latent_channels),
     )
 
-def Decoder():
+
+def Decoder(latent_channels=4):
     return nn.Sequential(
-        Clamp(), conv(4, 64), nn.ReLU(),
+        Clamp(), conv(latent_channels, 64), nn.ReLU(),
         Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
         Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
         Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
@@ -47,11 +48,11 @@ class TAESD(nn.Module):
     latent_magnitude = 3
     latent_shift = 0.5
 
-    def __init__(self, encoder_path=None, decoder_path=None):
+    def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4):
         """Initialize pretrained TAESD on the given device from the given checkpoints."""
         super().__init__()
-        self.taesd_encoder = Encoder()
-        self.taesd_decoder = Decoder()
+        self.taesd_encoder = Encoder(latent_channels=latent_channels)
+        self.taesd_decoder = Decoder(latent_channels=latent_channels)
         self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
         if encoder_path is not None:
             self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
diff --git a/latent_preview.py b/latent_preview.py
index 54aa233f2..ae6c106e4 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -64,7 +64,7 @@ def get_previewer(device, latent_format):
 
         if method == LatentPreviewMethod.TAESD:
             if taesd_decoder_path:
-                taesd = TAESD(None, taesd_decoder_path).to(device)
+                taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device)
                 previewer = TAESDPreviewerImpl(taesd)
             else:
                 logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
diff --git a/nodes.py b/nodes.py
index 0b2a96f73..ca10ca32a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -634,6 +634,8 @@ class VAELoader:
         sdxl_taesd_dec = False
         sd1_taesd_enc = False
         sd1_taesd_dec = False
+        sd3_taesd_enc = False
+        sd3_taesd_dec = False
 
         for v in approx_vaes:
             if v.startswith("taesd_decoder."):
@@ -644,10 +646,16 @@ class VAELoader:
                 sdxl_taesd_dec = True
             elif v.startswith("taesdxl_encoder."):
                 sdxl_taesd_enc = True
+            elif v.startswith("taesd3_decoder."):
+                sd3_taesd_dec = True
+            elif v.startswith("taesd3_encoder."):
+                sd3_taesd_enc = True
         if sd1_taesd_dec and sd1_taesd_enc:
             vaes.append("taesd")
         if sdxl_taesd_dec and sdxl_taesd_enc:
             vaes.append("taesdxl")
+        if sd3_taesd_dec and sd3_taesd_enc:
+            vaes.append("taesd3")
         return vaes
 
     @staticmethod
@@ -670,6 +678,8 @@ class VAELoader:
             sd["vae_scale"] = torch.tensor(0.18215)
         elif name == "taesdxl":
             sd["vae_scale"] = torch.tensor(0.13025)
+        elif name == "taesd3":
+            sd["vae_scale"] = torch.tensor(1.5305)
         return sd
 
     @classmethod
@@ -682,12 +692,15 @@ class VAELoader:
 
     #TODO: scale factor?
     def load_vae(self, vae_name):
-        if vae_name in ["taesd", "taesdxl"]:
+        if vae_name in ["taesd", "taesdxl", "taesd3"]:
             sd = self.load_taesd(vae_name)
         else:
             vae_path = folder_paths.get_full_path("vae", vae_name)
             sd = comfy.utils.load_torch_file(vae_path)
-        vae = comfy.sd.VAE(sd=sd)
+
+        latent_channels = 16 if vae_name == 'taesd3' else 4
+
+        vae = comfy.sd.VAE(sd=sd, latent_channels=latent_channels)
         return (vae,)
 
 class ControlNetLoader:

From 04e8798c37d958d74ea6bda506b86f51356d6caf Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 16 Jun 2024 02:04:24 -0400
Subject: [PATCH 090/315] Improvements to the TAESD3 implementation.

---
 comfy/sd.py          | 6 +++---
 comfy/taesd/taesd.py | 5 +++--
 nodes.py             | 8 ++++----
 3 files changed, 10 insertions(+), 9 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index d2720ec13..8d90dd063 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -166,7 +166,7 @@ class CLIP:
         return self.patcher.get_key_patches()
 
 class VAE:
-    def __init__(self, sd=None, device=None, config=None, dtype=None, latent_channels=4):
+    def __init__(self, sd=None, device=None, config=None, dtype=None):
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
@@ -174,7 +174,7 @@ class VAE:
         self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
         self.downscale_ratio = 8
         self.upscale_ratio = 8
-        self.latent_channels = latent_channels
+        self.latent_channels = 4
         self.output_channels = 3
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
@@ -189,7 +189,7 @@ class VAE:
                                                             encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
                                                             decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
             elif "taesd_decoder.1.weight" in sd:
-                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
+                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=sd["taesd_decoder.1.weight"].shape[1])
             elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
                 self.first_stage_model = StageA()
                 self.downscale_ratio = 4
diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py
index 74031c60d..ce36f1a84 100644
--- a/comfy/taesd/taesd.py
+++ b/comfy/taesd/taesd.py
@@ -54,6 +54,7 @@ class TAESD(nn.Module):
         self.taesd_encoder = Encoder(latent_channels=latent_channels)
         self.taesd_decoder = Decoder(latent_channels=latent_channels)
         self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
+        self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
         if encoder_path is not None:
             self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
         if decoder_path is not None:
@@ -70,9 +71,9 @@ class TAESD(nn.Module):
         return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
 
     def decode(self, x):
-        x_sample = self.taesd_decoder(x * self.vae_scale)
+        x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale)
         x_sample = x_sample.sub(0.5).mul(2)
         return x_sample
 
     def encode(self, x):
-        return self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale
+        return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
diff --git a/nodes.py b/nodes.py
index ca10ca32a..06ea46216 100644
--- a/nodes.py
+++ b/nodes.py
@@ -676,10 +676,13 @@ class VAELoader:
 
         if name == "taesd":
             sd["vae_scale"] = torch.tensor(0.18215)
+            sd["vae_shift"] = torch.tensor(0.0)
         elif name == "taesdxl":
             sd["vae_scale"] = torch.tensor(0.13025)
+            sd["vae_shift"] = torch.tensor(0.0)
         elif name == "taesd3":
             sd["vae_scale"] = torch.tensor(1.5305)
+            sd["vae_shift"] = torch.tensor(0.0609)
         return sd
 
     @classmethod
@@ -697,10 +700,7 @@ class VAELoader:
         else:
             vae_path = folder_paths.get_full_path("vae", vae_name)
             sd = comfy.utils.load_torch_file(vae_path)
-
-        latent_channels = 16 if vae_name == 'taesd3' else 4
-
-        vae = comfy.sd.VAE(sd=sd, latent_channels=latent_channels)
+        vae = comfy.sd.VAE(sd=sd)
         return (vae,)
 
 class ControlNetLoader:

From 746a0410d43b02dcaf91ac5abc2c1803e420203d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 16 Jun 2024 03:10:04 -0400
Subject: [PATCH 091/315] Fix VAEEncode with taesd3.

---
 comfy/sd.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 8d90dd063..82f9aeab8 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -189,7 +189,8 @@ class VAE:
                                                             encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
                                                             decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
             elif "taesd_decoder.1.weight" in sd:
-                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=sd["taesd_decoder.1.weight"].shape[1])
+                self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
+                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
             elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
                 self.first_stage_model = StageA()
                 self.downscale_ratio = 4

From ca9d300a804fd1bfc67b3de9200c2d09b78899d0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 16 Jun 2024 11:47:32 -0400
Subject: [PATCH 092/315] Better estimation for memory usage during audio VAE
 encoding/decoding.

---
 comfy/sd.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 82f9aeab8..e16cd8e53 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -237,8 +237,8 @@ class VAE:
                                                                 decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
             elif "decoder.layers.0.weight_v" in sd:
                 self.first_stage_model = AudioOobleckVAE()
-                self.memory_used_encode = lambda shape, dtype: (1767 * shape[2]) * model_management.dtype_size(dtype) #TODO: tweak for the audio VAE
-                self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * 64) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)
                 self.latent_channels = 64
                 self.output_channels = 2
                 self.upscale_ratio = 2048

From 8ddc151a4ce7f59de75b24ea8349f0e406ba0da5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 16 Jun 2024 13:06:23 -0400
Subject: [PATCH 093/315] Squash depreciation warning on new pytorch.

---
 comfy/ldm/audio/autoencoder.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py
index 7363131e0..8123e66a5 100644
--- a/comfy/ldm/audio/autoencoder.py
+++ b/comfy/ldm/audio/autoencoder.py
@@ -75,10 +75,16 @@ class SnakeBeta(nn.Module):
         return x
 
 def WNConv1d(*args, **kwargs):
-    return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs))
+    try:
+        return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs))
+    except:
+        return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older
 
 def WNConvTranspose1d(*args, **kwargs):
-    return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
+    try:
+        return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
+    except:
+        return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older
 
 def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
     if activation == "elu":

From 6425252c4f2f6acd8f4ad59a2135f5bdae3452e4 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 16 Jun 2024 13:12:54 -0400
Subject: [PATCH 094/315] Use fp16 as the default vae dtype for the audio VAE.

---
 comfy/model_management.py | 35 ++++++++++++++++++++---------------
 comfy/sd.py               |  5 ++++-
 2 files changed, 24 insertions(+), 16 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 8b8d3ff06..047193290 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -167,7 +167,7 @@ if args.use_pytorch_cross_attention:
     ENABLE_PYTORCH_ATTENTION = True
     XFORMERS_IS_AVAILABLE = False
 
-VAE_DTYPE = torch.float32
+VAE_DTYPES = [torch.float32]
 
 try:
     if is_nvidia():
@@ -176,7 +176,7 @@ try:
             if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                 ENABLE_PYTORCH_ATTENTION = True
             if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
-                VAE_DTYPE = torch.bfloat16
+                VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
     if is_intel_xpu():
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             ENABLE_PYTORCH_ATTENTION = True
@@ -184,17 +184,10 @@ except:
     pass
 
 if is_intel_xpu():
-    VAE_DTYPE = torch.bfloat16
+    VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
 
 if args.cpu_vae:
-    VAE_DTYPE = torch.float32
-
-if args.fp16_vae:
-    VAE_DTYPE = torch.float16
-elif args.bf16_vae:
-    VAE_DTYPE = torch.bfloat16
-elif args.fp32_vae:
-    VAE_DTYPE = torch.float32
+    VAE_DTYPES = [torch.float32]
 
 
 if ENABLE_PYTORCH_ATTENTION:
@@ -258,7 +251,6 @@ try:
 except:
     logging.warning("Could not pick default device.")
 
-logging.info("VAE dtype: {}".format(VAE_DTYPE))
 
 current_loaded_models = []
 
@@ -619,9 +611,22 @@ def vae_offload_device():
     else:
         return torch.device("cpu")
 
-def vae_dtype():
-    global VAE_DTYPE
-    return VAE_DTYPE
+def vae_dtype(device=None, allowed_dtypes=[]):
+    global VAE_DTYPES
+    if args.fp16_vae:
+        return torch.float16
+    elif args.bf16_vae:
+        return torch.bfloat16
+    elif args.fp32_vae:
+        return torch.float32
+
+    for d in allowed_dtypes:
+        if d == torch.float16 and should_use_fp16(device, prioritize_performance=False):
+            return d
+        if d in VAE_DTYPES:
+            return d
+
+    return VAE_DTYPES[0]
 
 def get_autocast_device(dev):
     if hasattr(dev, 'type'):
diff --git a/comfy/sd.py b/comfy/sd.py
index e16cd8e53..58a858aa0 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -178,6 +178,7 @@ class VAE:
         self.output_channels = 3
         self.process_input = lambda image: image * 2.0 - 1.0
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
+        self.working_dtypes = [torch.bfloat16, torch.float32]
 
         if config is None:
             if "decoder.mid.block_1.mix_factor" in sd:
@@ -245,6 +246,7 @@ class VAE:
                 self.downscale_ratio =  2048
                 self.process_output = lambda audio: audio
                 self.process_input = lambda audio: audio
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -265,12 +267,13 @@ class VAE:
         self.device = device
         offload_device = model_management.vae_offload_device()
         if dtype is None:
-            dtype = model_management.vae_dtype()
+            dtype = model_management.vae_dtype(self.device, self.working_dtypes)
         self.vae_dtype = dtype
         self.first_stage_model.to(self.vae_dtype)
         self.output_device = model_management.intermediate_device()
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
+        logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
 
     def vae_encode_crop_pixels(self, pixels):
         dims = pixels.shape[1:-1]

From b7c473d1abda165e1ff6f90dfacc6f589c3d1c38 Mon Sep 17 00:00:00 2001
From: Janek Mann <janekm@gmail.com>
Date: Mon, 17 Jun 2024 12:55:06 +0100
Subject: [PATCH 095/315] Fix lora keys for SimpleTuner (#3759)

---
 comfy/lora.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/comfy/lora.py b/comfy/lora.py
index 37254b03f..082a8b3cb 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -30,6 +30,7 @@ def load_lora(lora, to_load):
         regular_lora = "{}.lora_up.weight".format(x)
         diffusers_lora = "{}_lora.up.weight".format(x)
         diffusers2_lora = "{}.lora_B.weight".format(x)
+        diffusers3_lora = "{}.lora.up.weight".format(x)
         transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
         A_name = None
 
@@ -45,6 +46,10 @@ def load_lora(lora, to_load):
             A_name = diffusers2_lora
             B_name = "{}.lora_A.weight".format(x)
             mid_name = None
+        elif diffusers3_lora in lora.keys():
+            A_name = diffusers3_lora
+            B_name = "{}.lora.down.weight".format(x)
+            mid_name = None
         elif transformers_lora in lora.keys():
             A_name = transformers_lora
             B_name ="{}.lora_linear_layer.down.weight".format(x)

From 379ff92e9e872a588ad2ceeabdd08b909590b25b Mon Sep 17 00:00:00 2001
From: Juanjuan <472185568@qq.com>
Date: Mon, 17 Jun 2024 19:56:53 +0800
Subject: [PATCH 096/315] fix app.js no graph defined (#3754)

* local test

* fix "graph" not found

* fix

---------

Co-authored-by: Xiujuan Li <xiujuali@amazon.com>
---
 web/scripts/app.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index f96d197a8..58fb765c4 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -2239,7 +2239,7 @@ export class ComfyApp {
 			const node = LiteGraph.createNode(data.class_type);
 			node.id = isNaN(+id) ? id : +id;
 			node.title = data._meta?.title ?? node.title
-			graph.add(node);
+			app.graph.add(node);
 		}
 
 		for (const id of ids) {

From a45df695706e8b80a66ff6815789582d88571326 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 17 Jun 2024 22:48:23 -0400
Subject: [PATCH 097/315] Basic tiled decoding for audio VAE.

---
 comfy/sd.py | 16 +++++++++++++++-
 1 file changed, 15 insertions(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 58a858aa0..cfbf8fa4d 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -298,6 +298,17 @@ class VAE:
             / 3.0)
         return output
 
+    def decode_tiled_1d(self, samples, tile_x=128, overlap=64):
+        output = torch.empty((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
+
+        for j in range(samples.shape[0]):
+            for i in range(0, samples.shape[-1], tile_x - overlap):
+                f = i
+                t = i + tile_x
+                output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] = self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float()
+
+        return output
+
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
@@ -325,7 +336,10 @@ class VAE:
                 pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
         except model_management.OOM_EXCEPTION as e:
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
-            pixel_samples = self.decode_tiled_(samples_in)
+            if len(samples_in.shape) == 3:
+                pixel_samples = self.decode_tiled_1d(samples_in)
+            else:
+                pixel_samples = self.decode_tiled_(samples_in)
 
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples

From 55f0dc124ed4352ac52b884c13f41ff9a9736102 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 18 Jun 2024 09:57:40 -0400
Subject: [PATCH 098/315] Add soundfile dependency so that windows can save
 audio.

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 85e1dc9b0..108958d2f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -15,3 +15,4 @@ psutil
 #non essential dependencies:
 kornia>=0.7.1
 spandrel
+soundfile

From 3914d5a2ae5c755556c9f15e70d2b4104984da87 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 19 Jun 2024 10:01:43 -0400
Subject: [PATCH 099/315] Support full SD3 loras.

---
 comfy/lora.py  | 19 +++++++-------
 comfy/utils.py | 70 ++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 79 insertions(+), 10 deletions(-)

diff --git a/comfy/lora.py b/comfy/lora.py
index 082a8b3cb..9e3e7a8bb 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -252,15 +252,14 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map[diffusers_lora_key] = unet_key
 
     if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
-        for i in range(model.model_config.unet_config.get("depth", 0)):
-            k = "transformer.transformer_blocks.{}.attn.".format(i)
-            qkv = "diffusion_model.joint_blocks.{}.x_block.attn.qkv.weight".format(i)
-            proj = "diffusion_model.joint_blocks.{}.x_block.attn.proj.weight".format(i)
-            if qkv in sd:
-                offset = sd[qkv].shape[0] // 3
-                key_map["{}to_q".format(k)] = (qkv, (0, 0, offset))
-                key_map["{}to_k".format(k)] = (qkv, (0, offset, offset))
-                key_map["{}to_v".format(k)] = (qkv, (0, offset * 2, offset))
-                key_map["{}to_out.0".format(k)] = proj
+        diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format
+                key_map[key_lora] = to
+
+                key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others?
+                key_map[key_lora] = to
 
     return key_map
diff --git a/comfy/utils.py b/comfy/utils.py
index 884404cce..0d24fe730 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -249,6 +249,76 @@ def unet_to_diffusers(unet_config):
 
     return diffusers_unet_map
 
+MMDIT_MAP_BASIC = {
+    ("context_embedder.bias", "context_embedder.bias"),
+    ("context_embedder.weight", "context_embedder.weight"),
+    ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
+    ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
+    ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
+    ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
+    ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+    ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+    ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"),
+    ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"),
+    ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"),
+    ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"),
+    ("pos_embed", "pos_embed.pos_embed"),
+    ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias"),
+    ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight"),
+    ("final_layer.linear.bias", "proj_out.bias"),
+    ("final_layer.linear.weight", "proj_out.weight"),
+}
+
+MMDIT_MAP_BLOCK = {
+    ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"),
+    ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"),
+    ("context_block.attn.proj.bias", "attn.to_add_out.bias"),
+    ("context_block.attn.proj.weight", "attn.to_add_out.weight"),
+    ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"),
+    ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
+    ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
+    ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
+    ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
+    ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
+    ("x_block.attn.proj.bias", "attn.to_out.0.bias"),
+    ("x_block.attn.proj.weight", "attn.to_out.0.weight"),
+    ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
+    ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
+    ("x_block.mlp.fc2.bias", "ff.net.2.bias"),
+    ("x_block.mlp.fc2.weight", "ff.net.2.weight"),
+    ("", ""),
+}
+
+def mmdit_to_diffusers(mmdit_config, output_prefix=""):
+    key_map = {}
+
+    depth = mmdit_config.get("depth", 0)
+    for i in range(depth):
+        block_from = "transformer_blocks.{}".format(i)
+        block_to = "{}joint_blocks.{}".format(output_prefix, i)
+
+        offset = depth * 64
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(block_from)
+            qkv = "{}.x_block.attn.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+            qkv = "{}.context_block.attn.qkv.{}".format(block_to, end)
+            key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+        for k in MMDIT_MAP_BLOCK:
+            key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
+
+    for k in MMDIT_MAP_BASIC:
+        key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
 def repeat_to_batch_size(tensor, batch_size, dim=0):
     if tensor.shape[dim] > batch_size:
         return tensor.narrow(dim, 0, batch_size)

From 97ae6ef46001ed444ef1f2bd085beae98c17c240 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 19 Jun 2024 10:39:17 -0400
Subject: [PATCH 100/315] Add api/ prefix to api endpoints (#3779)

---
 server.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/server.py b/server.py
index bab3b0600..590902587 100644
--- a/server.py
+++ b/server.py
@@ -527,9 +527,20 @@ class PromptServer():
                     self.prompt_queue.delete_history_item(id_to_delete)
 
             return web.Response(status=200)
-        
+
     def add_routes(self):
         self.user_manager.add_routes(self.routes)
+
+        # Prefix every route with /api for easier matching for delegation.
+        # This is very useful for frontend dev server, which need to forward
+        # everything except serving of static files.
+        # Currently both the old endpoints without prefix and new endpoints with
+        # prefix are supported.
+        api_routes = web.RouteTableDef()
+        for route in self.routes:
+            assert isinstance(route, web.RouteDef)
+            api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs)
+        self.app.add_routes(api_routes)
         self.app.add_routes(self.routes)
 
         for name, dir in nodes.EXTENSION_WEB_DIRS.items():

From e11052afcfd1e8aa3190d47c44f79b72a7a7a78a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 19 Jun 2024 16:32:30 -0400
Subject: [PATCH 101/315] Add ipndm sampler.

---
 comfy/k_diffusion/sampling.py | 41 +++++++++++++++++++++++++++++++++++
 comfy/samplers.py             |  3 ++-
 2 files changed, 43 insertions(+), 1 deletion(-)

diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 5bb991e76..214199266 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -841,3 +841,44 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
             d_prime = w1 * d + w2 * d_2 + w3 * d_3
             x = x + d_prime * dt
     return x
+
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    x_next = x
+
+    buffer_model = []
+    for i in trange(len(sigmas) - 1, disable=disable):
+        t_cur = sigmas[i]
+        t_next = sigmas[i + 1]
+
+        x_cur = x_next
+
+        denoised = model(x_cur, t_cur * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        d_cur = (x_cur - denoised) / t_cur
+
+        order = min(max_order, i+1)
+        if order == 1:      # First Euler step.
+            x_next = x_cur + (t_next - t_cur) * d_cur
+        elif order == 2:    # Use one history point.
+            x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
+        elif order == 3:    # Use two history points.
+            x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12
+        elif order == 4:    # Use three history points.
+            x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24
+
+        if len(buffer_model) == max_order - 1:
+            for k in range(max_order - 2):
+                buffer_model[k] = buffer_model[k+1]
+            buffer_model[-1] = d_cur
+        else:
+            buffer_model.append(d_cur)
+
+    return x_next
diff --git a/comfy/samplers.py b/comfy/samplers.py
index 656e0a28f..4de377cc0 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -539,7 +539,8 @@ class Sampler:
 
 KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
-                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
+                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
+                  "ipndm"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

From eee815ec99c8ff69d9b31b9eba096442119cb3f8 Mon Sep 17 00:00:00 2001
From: Mario Klingemann <Quasimondo@users.noreply.github.com>
Date: Wed, 19 Jun 2024 22:42:41 +0200
Subject: [PATCH 102/315] Update sd1_clip.py (#3684)

Made token instance check more flexible so it also works with integers from numpy arrays or long tensors
---
 comfy/sd1_clip.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 911af0a7e..78e556b56 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -9,6 +9,7 @@ from . import model_management
 import comfy.clip_model
 import json
 import logging
+import numbers
 
 def gen_empty_tokens(special_tokens, length):
     start_token = special_tokens.get("start", None)
@@ -130,10 +131,10 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         for x in tokens:
             tokens_temp = []
             for y in x:
-                if isinstance(y, int):
+                if isinstance(y, numbers.Integral):
                     if y == token_dict_size: #EOS token
                         y = -1
-                    tokens_temp += [y]
+                    tokens_temp += [int(y)]
                 else:
                     if y.shape[0] == current_embeds.weight.shape[1]:
                         embedding_weights += [y]

From b08a9dd04b725b46d0fabc4a48b4ddd659677267 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 19 Jun 2024 20:20:35 -0400
Subject: [PATCH 103/315] Remove empty line.

---
 comfy/utils.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/comfy/utils.py b/comfy/utils.py
index 0d24fe730..877f14b01 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -286,7 +286,6 @@ MMDIT_MAP_BLOCK = {
     ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
     ("x_block.mlp.fc2.bias", "ff.net.2.bias"),
     ("x_block.mlp.fc2.weight", "ff.net.2.weight"),
-    ("", ""),
 }
 
 def mmdit_to_diffusers(mmdit_config, output_prefix=""):

From 0d6a57938e4fdb5b22b473dfe5a71af8150009e6 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 19 Jun 2024 21:46:37 -0400
Subject: [PATCH 104/315] Support loading diffusers SD3 model format with
 UNETLoader node.

---
 comfy/model_detection.py                      | 37 +++++++++++++++++++
 comfy/sd.py                                   |  9 ++++-
 comfy/utils.py                                | 20 ++++++++--
 .../nodes_model_merging_model_specific.py     | 23 ++++++++++++
 4 files changed, 84 insertions(+), 5 deletions(-)

diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 4843e6a4a..e09dd381a 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -1,7 +1,9 @@
 import comfy.supported_models
 import comfy.supported_models_base
+import comfy.utils
 import math
 import logging
+import torch
 
 def count_blocks(state_dict_keys, prefix_string):
     count = 0
@@ -431,3 +433,38 @@ def model_config_from_diffusers_unet(state_dict):
     if unet_config is not None:
         return model_config_from_unet_config(unet_config)
     return None
+
+def convert_diffusers_mmdit(state_dict, output_prefix=""):
+    depth = count_blocks(state_dict, 'transformer_blocks.{}.')
+    if depth > 0:
+        out_sd = {}
+        sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth}, output_prefix=output_prefix)
+        for k in sd_map:
+            weight = state_dict.get(k, None)
+            if weight is not None:
+                t = sd_map[k]
+
+                if not isinstance(t, str):
+                    if len(t) > 2:
+                        fun = t[2]
+                    else:
+                        fun = lambda a: a
+                    offset = t[1]
+                    if offset is not None:
+                        old_weight = out_sd.get(t[0], None)
+                        if old_weight is None:
+                            old_weight = torch.empty_like(weight)
+                            old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
+
+                        w = old_weight.narrow(offset[0], offset[1], offset[2])
+                    else:
+                        old_weight = weight
+                        w = weight
+                    w[:] = fun(weight)
+                    t = t[0]
+                    out_sd[t] = old_weight
+                else:
+                    out_sd[t] = weight
+                state_dict.pop(k)
+
+    return out_sd
diff --git a/comfy/sd.py b/comfy/sd.py
index cfbf8fa4d..178f52e8b 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -568,7 +568,14 @@ def load_unet_state_dict(sd): #load unet in diffusers format
     unet_dtype = model_management.unet_dtype(model_params=parameters)
     load_device = model_management.get_torch_device()
 
-    if "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
+    if 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
+        new_sd = model_detection.convert_diffusers_mmdit(sd, "")
+        if new_sd is None:
+            return None
+        model_config = model_detection.model_config_from_unet(new_sd, "")
+        if model_config is None:
+            return None
+    elif "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
         model_config = model_detection.model_config_from_unet(sd, "")
         if model_config is None:
             return None
diff --git a/comfy/utils.py b/comfy/utils.py
index 877f14b01..f0d675004 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -249,6 +249,11 @@ def unet_to_diffusers(unet_config):
 
     return diffusers_unet_map
 
+def swap_scale_shift(weight):
+    shift, scale = weight.chunk(2, dim=0)
+    new_weight = torch.cat([scale, shift], dim=0)
+    return new_weight
+
 MMDIT_MAP_BASIC = {
     ("context_embedder.bias", "context_embedder.bias"),
     ("context_embedder.weight", "context_embedder.weight"),
@@ -263,8 +268,8 @@ MMDIT_MAP_BASIC = {
     ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"),
     ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"),
     ("pos_embed", "pos_embed.pos_embed"),
-    ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias"),
-    ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight"),
+    ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
+    ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
     ("final_layer.linear.bias", "proj_out.bias"),
     ("final_layer.linear.weight", "proj_out.weight"),
 }
@@ -313,8 +318,15 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
         for k in MMDIT_MAP_BLOCK:
             key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
 
-    for k in MMDIT_MAP_BASIC:
-        key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+    map_basic = MMDIT_MAP_BASIC.copy()
+    map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift))
+    map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift))
+
+    for k in map_basic:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
 
     return key_map
 
diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py
index f2d008d8b..74ab6814c 100644
--- a/comfy_extras/nodes_model_merging_model_specific.py
+++ b/comfy_extras/nodes_model_merging_model_specific.py
@@ -52,9 +52,32 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
+class ModelMergeSD3(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["pos_embed."] = argument
+        arg_dict["x_embedder."] = argument
+        arg_dict["context_embedder."] = argument
+        arg_dict["y_embedder."] = argument
+        arg_dict["t_embedder."] = argument
+
+        for i in range(38):
+            arg_dict["joint_blocks.{}.".format(i)] = argument
+
+        arg_dict["final_layer."] = argument
+
+        return {"required": arg_dict}
 
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
     "ModelMergeSDXL": ModelMergeSDXL,
+    "ModelMergeSD3": ModelMergeSD3,
 }

From 028a583befa6dab7d60a10d780474f58e945bc05 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 19 Jun 2024 22:32:04 -0400
Subject: [PATCH 105/315] Fix issue with full diffusers SD3 loras.

---
 comfy/model_patcher.py | 26 ++++++++++++++++----------
 1 file changed, 16 insertions(+), 10 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index 44b82795f..bf8787768 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -210,16 +210,19 @@ class ModelPatcher:
         model_sd = self.model.state_dict()
         for k in patches:
             offset = None
+            function = None
             if isinstance(k, str):
                 key = k
             else:
                 offset = k[1]
                 key = k[0]
+                if len(k) > 2:
+                    function = k[2]
 
             if key in model_sd:
                 p.add(k)
                 current_patches = self.patches.get(key, [])
-                current_patches.append((strength_patch, patches[k], strength_model, offset))
+                current_patches.append((strength_patch, patches[k], strength_model, offset, function))
                 self.patches[key] = current_patches
 
         self.patches_uuid = uuid.uuid4()
@@ -347,6 +350,9 @@ class ModelPatcher:
             v = p[1]
             strength_model = p[2]
             offset = p[3]
+            function = p[4]
+            if function is None:
+                function = lambda a: a
 
             old_weight = None
             if offset is not None:
@@ -371,7 +377,7 @@ class ModelPatcher:
                     if w1.shape != weight.shape:
                         logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
                     else:
-                        weight += strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
+                        weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
             elif patch_type == "lora": #lora/locon
                 mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
                 mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
@@ -389,9 +395,9 @@ class ModelPatcher:
                 try:
                     lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
                     else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "lokr":
@@ -435,9 +441,9 @@ class ModelPatcher:
                 try:
                     lora_diff = torch.kron(w1, w2).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
                     else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "loha":
@@ -472,9 +478,9 @@ class ModelPatcher:
                 try:
                     lora_diff = (m1 * m2).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
                     else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "glora":
@@ -493,9 +499,9 @@ class ModelPatcher:
                 try:
                     lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
                     if dora_scale is not None:
-                        weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength)
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
                     else:
-                        weight += ((strength * alpha) * lora_diff).type(weight.dtype)
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
                     logging.error("ERROR {} {} {}".format(patch_type, key, e))
             else:

From d7f0964266170d1070eac9823bbb340e89371282 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 19 Jun 2024 22:36:31 -0400
Subject: [PATCH 106/315] Fix routes (#3790)

---
 server.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/server.py b/server.py
index 590902587..30ea90c6c 100644
--- a/server.py
+++ b/server.py
@@ -538,8 +538,10 @@ class PromptServer():
         # prefix are supported.
         api_routes = web.RouteTableDef()
         for route in self.routes:
-            assert isinstance(route, web.RouteDef)
-            api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs)
+            # Custom nodes might add extra static routes. Only process non-static
+            # routes to add /api prefix.
+            if isinstance(route, web.RouteDef):
+                api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs)
         self.app.add_routes(api_routes)
         self.app.add_routes(self.routes)
 

From 45e10cac19de5c819674ef3df39bd22fe1397ef9 Mon Sep 17 00:00:00 2001
From: Zhenyu Zhou <79033747+zhyzhouu@users.noreply.github.com>
Date: Thu, 20 Jun 2024 20:12:15 +0800
Subject: [PATCH 107/315] feat: add gits scheduler (#3769)

---
 comfy_extras/nodes_gits.py | 369 +++++++++++++++++++++++++++++++++++++
 nodes.py                   |   1 +
 2 files changed, 370 insertions(+)
 create mode 100644 comfy_extras/nodes_gits.py

diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py
new file mode 100644
index 000000000..7bfae4ce6
--- /dev/null
+++ b/comfy_extras/nodes_gits.py
@@ -0,0 +1,369 @@
+# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main
+import numpy as np
+import torch
+
+def loglinear_interp(t_steps, num_steps):
+    """
+    Performs log-linear interpolation of a given array of decreasing numbers.
+    """
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    interped_ys = np.exp(new_ys)[::-1].copy()
+    return interped_ys
+
+NOISE_LEVELS = {
+    0.80: [
+        [14.61464119, 7.49001646, 0.02916753],
+        [14.61464119, 11.54541874, 6.77309084, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753],
+    ],
+    0.85: [
+        [14.61464119, 7.49001646, 0.02916753],
+        [14.61464119, 7.49001646, 1.84880662, 0.02916753],
+        [14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753],
+        [14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.60512662, 2.6383388, 1.56271636, 0.72133851, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
+    ],
+    0.90: [
+        [14.61464119, 6.77309084, 0.02916753],
+        [14.61464119, 7.49001646, 1.56271636, 0.02916753],
+        [14.61464119, 7.49001646, 3.07277966, 0.95350921, 0.02916753],
+        [14.61464119, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.09240818, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.11996698, 4.86714602, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 2.95596409, 1.61558151, 0.69515091, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
+        [14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.84484982, 1.84880662, 1.08895338, 0.52423614, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.84484982, 1.84880662, 1.08895338, 0.52423614, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.45427561, 3.32507086, 2.45070267, 1.61558151, 0.95350921, 0.45573691, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.45427561, 3.32507086, 2.45070267, 1.61558151, 0.95350921, 0.45573691, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.19988537, 1.51179266, 0.89115214, 0.43325692, 0.02916753],
+    ],
+    0.95: [
+        [14.61464119, 6.77309084, 0.02916753],
+        [14.61464119, 6.77309084, 1.56271636, 0.02916753],
+        [14.61464119, 7.49001646, 2.84484982, 0.89115214, 0.02916753],
+        [14.61464119, 7.49001646, 4.86714602, 2.36326075, 0.803307, 0.02916753],
+        [14.61464119, 7.49001646, 4.86714602, 2.95596409, 1.56271636, 0.64427125, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.95596409, 1.56271636, 0.64427125, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 4.86714602, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.07277966, 1.91321158, 1.08895338, 0.50118381, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.41535246, 0.803307, 0.38853383, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.46139455, 2.6383388, 1.84880662, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
+        [14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.46139455, 2.6383388, 1.84880662, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
+        [14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
+        [14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.60512662, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.60512662, 2.95596409, 2.19988537, 1.56271636, 1.05362725, 0.64427125, 0.32104823, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.65472794, 3.75677586, 3.07277966, 2.45070267, 1.78698075, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.36326075, 1.72759056, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.60512662, 2.95596409, 2.36326075, 1.72759056, 1.24153244, 0.83188516, 0.50118381, 0.22545385, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.45427561, 3.75677586, 3.07277966, 2.45070267, 1.91321158, 1.46270394, 1.05362725, 0.72133851, 0.43325692, 0.19894916, 0.02916753],
+    ],
+    1.00: [
+        [14.61464119, 1.56271636, 0.02916753],
+        [14.61464119, 6.77309084, 0.95350921, 0.02916753],
+        [14.61464119, 6.77309084, 2.36326075, 0.803307, 0.02916753],
+        [14.61464119, 7.11996698, 3.07277966, 1.56271636, 0.59516323, 0.02916753],
+        [14.61464119, 7.49001646, 4.86714602, 2.84484982, 1.41535246, 0.57119018, 0.02916753],
+        [14.61464119, 7.49001646, 4.86714602, 2.84484982, 1.61558151, 0.86115354, 0.38853383, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.84484982, 1.61558151, 0.86115354, 0.38853383, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 4.86714602, 3.07277966, 1.98035145, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.07277966, 1.98035145, 1.24153244, 0.72133851, 0.34370604, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.27973175, 1.51179266, 0.95350921, 0.54755926, 0.25053367, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.36326075, 1.61558151, 1.08895338, 0.72133851, 0.41087446, 0.17026083, 0.02916753],
+        [14.61464119, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.36326075, 1.61558151, 1.08895338, 0.72133851, 0.41087446, 0.17026083, 0.02916753],
+        [14.61464119, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.60512662, 2.84484982, 2.12350607, 1.56271636, 1.08895338, 0.72133851, 0.41087446, 0.17026083, 0.02916753],
+        [14.61464119, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.60512662, 2.84484982, 2.19988537, 1.61558151, 1.162866, 0.803307, 0.50118381, 0.27464288, 0.09824532, 0.02916753],
+        [14.61464119, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 4.65472794, 3.75677586, 3.07277966, 2.45070267, 1.84880662, 1.36964464, 1.01931262, 0.72133851, 0.45573691, 0.25053367, 0.09824532, 0.02916753],
+        [14.61464119, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 5.09240818, 4.26497746, 3.46139455, 2.84484982, 2.19988537, 1.67050016, 1.24153244, 0.92192322, 0.64427125, 0.43325692, 0.25053367, 0.09824532, 0.02916753],
+        [14.61464119, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 5.09240818, 4.26497746, 3.60512662, 2.95596409, 2.45070267, 1.91321158, 1.51179266, 1.12534678, 0.83188516, 0.59516323, 0.38853383, 0.22545385, 0.09824532, 0.02916753],
+        [14.61464119, 12.2308979, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 5.09240818, 4.26497746, 3.60512662, 2.95596409, 2.45070267, 1.91321158, 1.51179266, 1.12534678, 0.83188516, 0.59516323, 0.38853383, 0.22545385, 0.09824532, 0.02916753],
+        [14.61464119, 12.2308979, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 5.09240818, 4.26497746, 3.60512662, 2.95596409, 2.45070267, 1.91321158, 1.51179266, 1.12534678, 0.83188516, 0.59516323, 0.38853383, 0.22545385, 0.09824532, 0.02916753],
+    ],
+    1.05: [
+        [14.61464119, 0.95350921, 0.02916753],
+        [14.61464119, 6.77309084, 0.89115214, 0.02916753],
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+        [14.61464119, 5.85520077, 2.45070267, 1.41535246, 0.92192322, 0.64427125, 0.45573691, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.56271636, 1.01931262, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.84484982, 1.61558151, 1.08895338, 0.77538133, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.84484982, 1.72759056, 1.162866, 0.83188516, 0.64427125, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.84484982, 1.72759056, 1.162866, 0.83188516, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.84484982, 1.78698075, 1.24153244, 0.92192322, 0.72133851, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.84484982, 1.78698075, 1.24153244, 0.92192322, 0.72133851, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ], 
+    1.35: [
+        [14.61464119, 0.69515091, 0.02916753],
+        [14.61464119, 0.95350921, 0.34370604, 0.02916753],
+        [14.61464119, 1.56271636, 0.57119018, 0.19894916, 0.02916753],
+        [14.61464119, 1.61558151, 0.69515091, 0.29807833, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.83188516, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.162866, 0.64427125, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.36964464, 0.803307, 0.50118381, 0.32104823, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.41535246, 0.83188516, 0.54755926, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.95350921, 0.64427125, 0.45573691, 0.32104823, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.61558151, 1.05362725, 0.74807048, 0.54755926, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.45070267, 1.51179266, 1.01931262, 0.74807048, 0.57119018, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.40: [
+        [14.61464119, 0.59516323, 0.02916753],
+        [14.61464119, 0.95350921, 0.34370604, 0.02916753],
+        [14.61464119, 1.08895338, 0.43325692, 0.13792117, 0.02916753],
+        [14.61464119, 1.56271636, 0.64427125, 0.27464288, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
+        [14.61464119, 2.05039096, 0.95350921, 0.54755926, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.72133851, 0.43325692, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.52423614, 0.36617002, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.41535246, 0.86115354, 0.59516323, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.4783645, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.72133851, 0.54755926, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.43325692, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.45: [
+        [14.61464119, 0.59516323, 0.02916753],
+        [14.61464119, 0.803307, 0.25053367, 0.02916753],
+        [14.61464119, 0.95350921, 0.34370604, 0.09824532, 0.02916753],
+        [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
+        [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.91321158, 0.95350921, 0.57119018, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.08895338, 0.64427125, 0.41087446, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.50: [
+        [14.61464119, 0.54755926, 0.02916753],
+        [14.61464119, 0.803307, 0.25053367, 0.02916753],
+        [14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
+        [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
+        [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+}
+
+class GITSScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}),
+                     "steps": ("INT", {"default": 10, "min": 2, "max": 1000}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                      }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, coeff, steps, denoise):
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return (torch.FloatTensor([]),)
+            total_steps = round(steps * denoise)
+
+        if steps <= 20:
+            sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:]
+        else:
+            sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:]
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return (torch.FloatTensor(sigmas), )
+
+NODE_CLASS_MAPPINGS = {
+    "GITSScheduler": GITSScheduler,
+}
diff --git a/nodes.py b/nodes.py
index 06ea46216..99645b81c 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1991,6 +1991,7 @@ def init_custom_nodes():
         "nodes_webcam.py",
         "nodes_audio.py",
         "nodes_sd3.py",
+        "nodes_gits.py",
     ]
 
     import_failed = []

From d5efde89b76c40c72668daec052c07c71a737908 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 20 Jun 2024 08:51:49 -0400
Subject: [PATCH 108/315] Add ipndm_v sampler, works best with the exponential
 scheduler.

---
 comfy/k_diffusion/sampling.py | 63 +++++++++++++++++++++++++++++++++++
 comfy/samplers.py             |  2 +-
 2 files changed, 64 insertions(+), 1 deletion(-)

diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 214199266..78896b8b5 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -882,3 +882,66 @@ def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None,
             buffer_model.append(d_cur)
 
     return x_next
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    x_next = x
+    t_steps = sigmas
+
+    buffer_model = []
+    for i in trange(len(sigmas) - 1, disable=disable):
+        t_cur = sigmas[i]
+        t_next = sigmas[i + 1]
+
+        x_cur = x_next
+
+        denoised = model(x_cur, t_cur * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        d_cur = (x_cur - denoised) / t_cur
+
+        order = min(max_order, i+1)
+        if order == 1:      # First Euler step.
+            x_next = x_cur + (t_next - t_cur) * d_cur
+        elif order == 2:    # Use one history point.
+            h_n = (t_next - t_cur)
+            h_n_1 = (t_cur - t_steps[i-1])
+            coeff1 = (2 + (h_n / h_n_1)) / 2
+            coeff2 = -(h_n / h_n_1) / 2
+            x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])
+        elif order == 3:    # Use two history points.
+            h_n = (t_next - t_cur)
+            h_n_1 = (t_cur - t_steps[i-1])
+            h_n_2 = (t_steps[i-1] - t_steps[i-2])
+            temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
+            coeff1 = (2 + (h_n / h_n_1)) / 2 + temp
+            coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp
+            coeff3 = temp * h_n_1 / h_n_2
+            x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])
+        elif order == 4:    # Use three history points.
+            h_n = (t_next - t_cur)
+            h_n_1 = (t_cur - t_steps[i-1])
+            h_n_2 = (t_steps[i-1] - t_steps[i-2])
+            h_n_3 = (t_steps[i-2] - t_steps[i-3])
+            temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
+            temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \
+                   * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
+            coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2
+            coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2
+            coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2
+            coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2
+            x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3])
+
+        if len(buffer_model) == max_order - 1:
+            for k in range(max_order - 2):
+                buffer_model[k] = buffer_model[k+1]
+            buffer_model[-1] = d_cur.detach()
+        else:
+            buffer_model.append(d_cur.detach())
+
+    return x_next
diff --git a/comfy/samplers.py b/comfy/samplers.py
index 4de377cc0..55b1486b4 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -540,7 +540,7 @@ class Sampler:
 KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
-                  "ipndm"]
+                  "ipndm", "ipndm_v"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

From 1e2839f4d904b686cea2686b2855279572df8022 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 20 Jun 2024 16:50:31 -0400
Subject: [PATCH 109/315] More proper tiled audio decoding.

---
 comfy/sd.py | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 178f52e8b..470bd717b 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -299,15 +299,24 @@ class VAE:
         return output
 
     def decode_tiled_1d(self, samples, tile_x=128, overlap=64):
-        output = torch.empty((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
+        output = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
+        output_mult = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
 
         for j in range(samples.shape[0]):
             for i in range(0, samples.shape[-1], tile_x - overlap):
                 f = i
                 t = i + tile_x
-                output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] = self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float()
+                o = output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio]
+                m = torch.ones_like(o)
+                l = m.shape[-1]
+                for x in range(overlap):
+                    c = ((x + 1) / overlap)
+                    m[:,:,x:x+1] *= c
+                    m[:,:,l-x-1:l-x] *= c
+                o += self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float().to(self.output_device) * m
+                output_mult[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] += m
 
-        return output
+        return output / output_mult
 
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)

From 887a6341ed14c9904230cf55a0eabe95cd0218d3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 21 Jun 2024 08:41:31 -0400
Subject: [PATCH 110/315] Proper ModelMergeSD3_2B node.

---
 comfy_extras/nodes_model_merging_model_specific.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py
index 74ab6814c..df111bd60 100644
--- a/comfy_extras/nodes_model_merging_model_specific.py
+++ b/comfy_extras/nodes_model_merging_model_specific.py
@@ -52,7 +52,7 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
-class ModelMergeSD3(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
     CATEGORY = "advanced/model_merging/model_specific"
 
     @classmethod
@@ -68,7 +68,7 @@ class ModelMergeSD3(comfy_extras.nodes_model_merging.ModelMergeBlocks):
         arg_dict["y_embedder."] = argument
         arg_dict["t_embedder."] = argument
 
-        for i in range(38):
+        for i in range(24):
             arg_dict["joint_blocks.{}.".format(i)] = argument
 
         arg_dict["final_layer."] = argument
@@ -79,5 +79,5 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
     "ModelMergeSDXL": ModelMergeSDXL,
-    "ModelMergeSD3": ModelMergeSD3,
+    "ModelMergeSD3_2B": ModelMergeSD3_2B,
 }

From 4ef1479dcde5bbb51aabe99a5e0c295a453b5bb8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 22 Jun 2024 11:45:58 -0400
Subject: [PATCH 111/315] Multi dimension tiled scale function and tiled VAE
 audio encoding fallback.

---
 comfy/sd.py    | 31 +++++++++----------------
 comfy/utils.py | 61 +++++++++++++++++++++++++++++++++-----------------
 2 files changed, 51 insertions(+), 41 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 470bd717b..ea6e9b663 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -298,25 +298,9 @@ class VAE:
             / 3.0)
         return output
 
-    def decode_tiled_1d(self, samples, tile_x=128, overlap=64):
-        output = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
-        output_mult = torch.zeros((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device)
-
-        for j in range(samples.shape[0]):
-            for i in range(0, samples.shape[-1], tile_x - overlap):
-                f = i
-                t = i + tile_x
-                o = output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio]
-                m = torch.ones_like(o)
-                l = m.shape[-1]
-                for x in range(overlap):
-                    c = ((x + 1) / overlap)
-                    m[:,:,x:x+1] *= c
-                    m[:,:,l-x-1:l-x] *= c
-                o += self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float().to(self.output_device) * m
-                output_mult[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] += m
-
-        return output / output_mult
+    def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+        return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
 
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
@@ -331,6 +315,10 @@ class VAE:
         samples /= 3.0
         return samples
 
+    def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+        return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
+
     def decode(self, samples_in):
         try:
             memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
@@ -374,7 +362,10 @@ class VAE:
 
         except model_management.OOM_EXCEPTION as e:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
-            samples = self.encode_tiled_(pixel_samples)
+            if len(pixel_samples.shape) == 3:
+                samples = self.encode_tiled_1d(pixel_samples)
+            else:
+                samples = self.encode_tiled_(pixel_samples)
 
         return samples
 
diff --git a/comfy/utils.py b/comfy/utils.py
index f0d675004..ed6c58a64 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -6,6 +6,7 @@ import safetensors.torch
 import numpy as np
 from PIL import Image
 import logging
+import itertools
 
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
@@ -506,34 +507,52 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
     return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
 
 @torch.inference_mode()
-def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
-    output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device)
+def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
+    dims = len(tile)
+    output = torch.empty([samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])), device=output_device)
+
     for b in range(samples.shape[0]):
         s = samples[b:b+1]
-        out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
-        out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
-        for y in range(0, s.shape[2], tile_y - overlap):
-            for x in range(0, s.shape[3], tile_x - overlap):
-                x = max(0, min(s.shape[-1] - overlap, x))
-                y = max(0, min(s.shape[-2] - overlap, y))
-                s_in = s[:,:,y:y+tile_y,x:x+tile_x]
+        out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
+        out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
 
-                ps = function(s_in).to(output_device)
-                mask = torch.ones_like(ps)
-                feather = round(overlap * upscale_amount)
-                for t in range(feather):
-                        mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
-                out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask
-                out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask
-                if pbar is not None:
-                    pbar.update(1)
+        for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
+            s_in = s
+            upscaled = []
+
+            for d in range(dims):
+                pos = max(0, min(s.shape[d + 2] - overlap, it[d]))
+                l = min(tile[d], s.shape[d + 2] - pos)
+                s_in = s_in.narrow(d + 2, pos, l)
+                upscaled.append(round(pos * upscale_amount))
+            ps = function(s_in).to(output_device)
+            mask = torch.ones_like(ps)
+            feather = round(overlap * upscale_amount)
+            for t in range(feather):
+                for d in range(2, dims + 2):
+                    m = mask.narrow(d, t, 1)
+                    m *= ((1.0/feather) * (t + 1))
+                    m = mask.narrow(d, mask.shape[d] -1 -t, 1)
+                    m *= ((1.0/feather) * (t + 1))
+
+            o = out
+            o_d = out_div
+            for d in range(dims):
+                o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+                o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+
+            o += ps * mask
+            o_d += mask
+
+            if pbar is not None:
+                pbar.update(1)
 
         output[b:b+1] = out/out_div
     return output
 
+def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
+    return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar)
+
 PROGRESS_BAR_ENABLED = True
 def set_progress_bar_enabled(enabled):
     global PROGRESS_BAR_ENABLED

From 2f360ae89853bfe40cc281428a3bafde06f114d5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 22 Jun 2024 13:08:04 -0400
Subject: [PATCH 112/315] Support OneTrainer SD3 lora format.

---
 comfy/lora.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/comfy/lora.py b/comfy/lora.py
index 9e3e7a8bb..037431775 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -218,12 +218,21 @@ def model_lora_keys_clip(model, key_map={}):
                     lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
                     key_map[lora_key] = k
 
+    for k in sdk: #OneTrainer SD3 lora
+        if k.startswith("t5xxl.transformer.") and k.endswith(".weight"):
+            l_key = k[len("t5xxl.transformer."):-len(".weight")]
+            lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
+            key_map[lora_key] = k
 
     k = "clip_g.transformer.text_projection.weight"
     if k in sdk:
         key_map["lora_prior_te_text_projection"] = k #cascade lora?
         # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too
-        # key_map["lora_te_text_projection"] = k
+        key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora
+
+    k = "clip_l.transformer.text_projection.weight"
+    if k in sdk:
+        key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning
 
     return key_map
 
@@ -262,4 +271,7 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others?
                 key_map[key_lora] = to
 
+                key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
+                key_map[key_lora] = to
+
     return key_map

From 73ca7800192f6bc2e2194966b316b7cc3028e09a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 23 Jun 2024 13:21:18 -0400
Subject: [PATCH 113/315] Add SamplerEulerCFG++ node.

This node should match the DDIM implementation of CFG++ when "regular" is
selected.

"alternative" is a slightly different take on CFG++
---
 comfy/model_patcher.py                  | 10 +++-
 comfy_extras/nodes_advanced_samplers.py | 74 +++++++++++++++++++++++++
 2 files changed, 81 insertions(+), 3 deletions(-)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index bf8787768..b949031e9 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -51,6 +51,12 @@ def set_model_options_patch_replace(model_options, patch, name, block_name, numb
     model_options["transformer_options"] = to
     return model_options
 
+def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False):
+    model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
+    if disable_cfg1_optimization:
+        model_options["disable_cfg1_optimization"] = True
+    return model_options
+
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
         self.size = size
@@ -122,9 +128,7 @@ class ModelPatcher:
             self.model_options["disable_cfg1_optimization"] = True
 
     def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
-        self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
-        if disable_cfg1_optimization:
-            self.model_options["disable_cfg1_optimization"] = True
+        self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization)
 
     def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
         self.model_options["model_function_wrapper"] = unet_wrapper_function
diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py
index d973def81..7aa89c644 100644
--- a/comfy_extras/nodes_advanced_samplers.py
+++ b/comfy_extras/nodes_advanced_samplers.py
@@ -56,6 +56,80 @@ class SamplerLCMUpscale:
         sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
         return (sampler, )
 
+from comfy.k_diffusion.sampling import to_d
+import comfy.model_patcher
+
+@torch.no_grad()
+def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        sigma_hat = sigmas[i]
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, temp[0])
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        x = denoised + sigmas[i + 1] * d
+    return x
+
+@torch.no_grad()
+def sample_euler_cfgpp_alt(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        sigma_hat = sigmas[i]
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x - denoised + temp[0], sigma_hat, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        # Euler method
+        x = x + d * dt
+    return x
+
+class SamplerEulerCFGpp:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"version": (["regular", "alternative"],),}
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    # CATEGORY = "sampling/custom_sampling/samplers"
+    CATEGORY = "_for_testing"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, version):
+        if version == "regular":
+            sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp)
+        else:
+            sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp_alt)
+        return (sampler, )
+
 NODE_CLASS_MAPPINGS = {
     "SamplerLCMUpscale": SamplerLCMUpscale,
+    "SamplerEulerCFGpp": SamplerEulerCFGpp,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SamplerEulerCFGpp": "SamplerEulerCFG++",
 }

From 866f54da8d7eed41a0554648aef8cf213551ee59 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Mon, 24 Jun 2024 14:47:28 -0400
Subject: [PATCH 114/315] Add browser test action synced with TS repo (#3852)

* Add browser test action

* Add npm install task
---
 .github/workflows/test-browser.yml | 61 ++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)
 create mode 100644 .github/workflows/test-browser.yml

diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml
new file mode 100644
index 000000000..049b1cde2
--- /dev/null
+++ b/.github/workflows/test-browser.yml
@@ -0,0 +1,61 @@
+# This is a temporary action during frontend TS migration.
+# This file should be removed after TS migration is completed.
+# The browser test is here to ensure TS repo is working the same way as the
+# current JS code.
+# If you are adding UI feature, please sync your changes to the TS repo:
+# huchenlei/ComfyUI_frontend and update test expectation files accordingly.
+name: Playwright Browser Tests CI
+
+on:
+  pull_request:
+    branches: [ main, master ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout ComfyUI
+      uses: actions/checkout@v4
+      with:
+        repository: "comfyanonymous/ComfyUI"
+        path: "ComfyUI"
+    - name: Checkout ComfyUI_frontend
+      uses: actions/checkout@v4
+      with:
+        repository: "huchenlei/ComfyUI_frontend"
+        path: "ComfyUI_frontend"
+        ref: "23a4ee83144d9bf22e7218b9c78c71f8544c8b27"
+    - uses: actions/setup-node@v3
+      with:
+        node-version: lts/*
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+        pip install wait-for-it
+      working-directory: ComfyUI
+    - name: Start ComfyUI server
+      run: |
+        python main.py --cpu &
+        wait-for-it --service 127.0.0.1:8188 -t 600
+      working-directory: ComfyUI
+    - name: Install ComfyUI_frontend dependencies
+      run: |
+        npm ci
+      working-directory: ComfyUI_frontend
+    - name: Install Playwright Browsers
+      run: npx playwright install --with-deps
+      working-directory: ComfyUI_frontend
+    - name: Run Playwright tests
+      run: npx playwright test
+      working-directory: ComfyUI_frontend
+    - uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: playwright-report
+        path: ComfyUI_frontend/playwright-report/
+        retention-days: 30

From eab211bb1e79fbda0b8eef00e44249993c040ae0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 24 Jun 2024 16:55:20 -0400
Subject: [PATCH 115/315] Resample audio to 44100 when VAE encoding it.

---
 comfy_extras/nodes_audio.py | 8 +++++++-
 1 file changed, 7 insertions(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 5f4bd3540..57d0a20ab 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -31,7 +31,13 @@ class VAEEncodeAudio:
     CATEGORY = "_for_testing/audio"
 
     def encode(self, vae, audio):
-        t = vae.encode(audio["waveform"].movedim(1, -1))
+        sample_rate = audio["sample_rate"]
+        if 44100 != sample_rate:
+            waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
+        else:
+            waveform = audio["waveform"]
+
+        t = vae.encode(waveform.movedim(1, -1))
         return ({"samples":t}, )
 
 class VAEDecodeAudio:

From 90aebb6c868d713c3e6457d1474c53808cb5f6a2 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Tue, 25 Jun 2024 11:49:25 +0100
Subject: [PATCH 116/315] New Menu & Workflow Management (#3112)

* menu

* wip

* wip

* wip

* wip

* wip

* workflow saving/loading

* Support inserting workflows
Move buttosn to top of lists

* fix session storage
implement renaming

* temp

* refactor, better workflow instance management

* wip

* progress on progress

* added send to workflow
various fixes

* Support multiple image loaders

* Support dynamic size breakpoints based on content

* various fixes
add close unsaved warning

* Add filtering tree

* prevent renaming unsaved

* fix zindex on hover

* fix top offset

* use filename as workflow name

* resize on setting change

* hide element until it is drawn

* remove glow

* Fix export name

* Fix test, revert accidental changes to groupNode

* Fix colors on all themes

* show hover items on smaller screen (mobile)

* remove debugging code

* dialog fix

* Dont reorder open workflows
Allow elements around canvas

* Toggle body display on setting change

* Fix menu disappearing on chrome

* Increase delay when typing, remove margin on Safari, fix dialog location

* Fix overflow issue on iOS

* Add reset view button
Prevent view changes causing history entries

* Bottom menu wip

* Various fixes

* Fix merge

* Fix breaking old menu position

* Fix merge adding restore view to loadGraphData
---
 app/user_manager.py                         |  91 ++-
 tests-ui/utils/setup.js                     |   1 +
 web/extensions/core/colorPalette.js         |  30 +-
 web/extensions/core/groupNode.js            |   2 +-
 web/extensions/core/undoRedo.js             | 177 -----
 web/fonts/materialdesignicons-webfont.woff2 | Bin 0 -> 403216 bytes
 web/index.html                              |   1 +
 web/jsconfig.json                           |   4 +-
 web/lib/materialdesignicons.min.css         |   3 +
 web/scripts/api.js                          |  75 +-
 web/scripts/app.js                          | 223 ++++--
 web/scripts/changeTracker.js                | 242 +++++++
 web/scripts/domWidget.js                    |  14 +-
 web/scripts/ui.js                           |  34 +-
 web/scripts/ui/components/asyncDialog.js    |  64 ++
 web/scripts/ui/components/button.js         | 163 +++++
 web/scripts/ui/components/buttonGroup.js    |  45 ++
 web/scripts/ui/components/popup.js          | 128 ++++
 web/scripts/ui/components/splitButton.js    |  43 ++
 web/scripts/ui/dialog.js                    |  28 +-
 web/scripts/ui/menu/index.js                | 302 ++++++++
 web/scripts/ui/menu/interruptButton.js      |  23 +
 web/scripts/ui/menu/menu.css                | 701 ++++++++++++++++++
 web/scripts/ui/menu/queueButton.js          |  93 +++
 web/scripts/ui/menu/queueOptions.js         |  77 ++
 web/scripts/ui/menu/viewHistory.js          |  27 +
 web/scripts/ui/menu/viewList.js             | 203 ++++++
 web/scripts/ui/menu/viewQueue.js            |  55 ++
 web/scripts/ui/menu/workflows.js            | 764 ++++++++++++++++++++
 web/scripts/ui/settings.js                  |  20 +-
 web/scripts/ui/utils.js                     |  56 ++
 web/scripts/utils.js                        |  68 ++
 web/scripts/workflows.js                    | 450 ++++++++++++
 web/style.css                               |  75 ++
 web/types/comfy.d.ts                        |  16 +-
 35 files changed, 3986 insertions(+), 312 deletions(-)
 delete mode 100644 web/extensions/core/undoRedo.js
 create mode 100644 web/fonts/materialdesignicons-webfont.woff2
 create mode 100644 web/lib/materialdesignicons.min.css
 create mode 100644 web/scripts/changeTracker.js
 create mode 100644 web/scripts/ui/components/asyncDialog.js
 create mode 100644 web/scripts/ui/components/button.js
 create mode 100644 web/scripts/ui/components/buttonGroup.js
 create mode 100644 web/scripts/ui/components/popup.js
 create mode 100644 web/scripts/ui/components/splitButton.js
 create mode 100644 web/scripts/ui/menu/index.js
 create mode 100644 web/scripts/ui/menu/interruptButton.js
 create mode 100644 web/scripts/ui/menu/menu.css
 create mode 100644 web/scripts/ui/menu/queueButton.js
 create mode 100644 web/scripts/ui/menu/queueOptions.js
 create mode 100644 web/scripts/ui/menu/viewHistory.js
 create mode 100644 web/scripts/ui/menu/viewList.js
 create mode 100644 web/scripts/ui/menu/viewQueue.js
 create mode 100644 web/scripts/ui/menu/workflows.js
 create mode 100644 web/scripts/ui/utils.js
 create mode 100644 web/scripts/workflows.js

diff --git a/app/user_manager.py b/app/user_manager.py
index 209094af1..53dff18b7 100644
--- a/app/user_manager.py
+++ b/app/user_manager.py
@@ -2,6 +2,8 @@ import json
 import os
 import re
 import uuid
+import glob
+import shutil
 from aiohttp import web
 from comfy.cli_args import args
 from folder_paths import user_directory
@@ -56,16 +58,16 @@ class UserManager():
         if os.path.commonpath((root_dir, user_root)) != root_dir:
             return None
 
-        parent = user_root
-
         if file is not None:
             # prevent leaving /{type}/{user}
             path = os.path.abspath(os.path.join(user_root, file))
             if os.path.commonpath((user_root, path)) != user_root:
                 return None
 
+        parent = os.path.split(path)[0]
+
         if create_dir and not os.path.exists(parent):
-            os.mkdir(parent)
+            os.makedirs(parent, exist_ok=True)
 
         return path
 
@@ -108,33 +110,96 @@ class UserManager():
             user_id = self.add_user(username)
             return web.json_response(user_id)
 
-        @routes.get("/userdata/{file}")
-        async def getuserdata(request):
-            file = request.match_info.get("file", None)
-            if not file:
+        @routes.get("/userdata")
+        async def listuserdata(request):
+            directory = request.rel_url.query.get('dir', '')
+            if not directory:
                 return web.Response(status=400)
                 
-            path = self.get_request_user_filepath(request, file)
+            path = self.get_request_user_filepath(request, directory)
             if not path:
                 return web.Response(status=403)
             
             if not os.path.exists(path):
                 return web.Response(status=404)
             
-            return web.FileResponse(path)
+            recurse = request.rel_url.query.get('recurse', '').lower() == "true"
+            results = glob.glob(os.path.join(
+                glob.escape(path), '**/*'), recursive=recurse)
+            results = [os.path.relpath(x, path) for x in results if os.path.isfile(x)]
+            
+            split_path = request.rel_url.query.get('split', '').lower() == "true"
+            if split_path:
+                results = [[x] + x.split(os.sep) for x in results]
 
-        @routes.post("/userdata/{file}")
-        async def post_userdata(request):
-            file = request.match_info.get("file", None)
+            return web.json_response(results)
+
+        def get_user_data_path(request, check_exists = False, param = "file"):
+            file = request.match_info.get(param, None)
             if not file:
                 return web.Response(status=400)
                 
             path = self.get_request_user_filepath(request, file)
             if not path:
                 return web.Response(status=403)
+            
+            if check_exists and not os.path.exists(path):
+                return web.Response(status=404)
+            
+            return path
+
+        @routes.get("/userdata/{file}")
+        async def getuserdata(request):
+            path = get_user_data_path(request, check_exists=True)
+            if not isinstance(path, str):
+                return path
+            
+            return web.FileResponse(path)
+
+        @routes.post("/userdata/{file}")
+        async def post_userdata(request):
+            path = get_user_data_path(request)
+            if not isinstance(path, str):
+                return path
+            
+            overwrite = request.query["overwrite"] != "false"
+            if not overwrite and os.path.exists(path):
+                return web.Response(status=409)
 
             body = await request.read()
+
             with open(path, "wb") as f:
                 f.write(body)
                 
-            return web.Response(status=200)
+            resp = os.path.relpath(path, self.get_request_user_filepath(request, None))
+            return web.json_response(resp)
+
+        @routes.delete("/userdata/{file}")
+        async def delete_userdata(request):
+            path = get_user_data_path(request, check_exists=True)
+            if not isinstance(path, str):
+                return path
+
+            os.remove(path)
+                
+            return web.Response(status=204)
+
+        @routes.post("/userdata/{file}/move/{dest}")
+        async def move_userdata(request):
+            source = get_user_data_path(request, check_exists=True)
+            if not isinstance(source, str):
+                return source
+            
+            dest = get_user_data_path(request, check_exists=False, param="dest")
+            if not isinstance(source, str):
+                return dest
+            
+            overwrite = request.query["overwrite"] != "false"
+            if not overwrite and os.path.exists(dest):
+                return web.Response(status=409)
+
+            print(f"moving '{source}' -> '{dest}'")
+            shutil.move(source, dest)
+                
+            resp = os.path.relpath(dest, self.get_request_user_filepath(request, None))
+            return web.json_response(resp)
diff --git a/tests-ui/utils/setup.js b/tests-ui/utils/setup.js
index e46258943..3a6d69b72 100644
--- a/tests-ui/utils/setup.js
+++ b/tests-ui/utils/setup.js
@@ -72,6 +72,7 @@ export function mockApi(config = {}) {
 		storeUserData: jest.fn((file, data) => {
 			userData[file] = data;
 		}),
+		listUserData: jest.fn(() => [])
 	};
 	jest.mock("../../web/scripts/api", () => ({
 		get api() {
diff --git a/web/extensions/core/colorPalette.js b/web/extensions/core/colorPalette.js
index 02546782f..245f3b93e 100644
--- a/web/extensions/core/colorPalette.js
+++ b/web/extensions/core/colorPalette.js
@@ -63,6 +63,10 @@ const colorPalettes = {
 				"border-color": "#4e4e4e",
 				"tr-even-bg-color": "#222",
 				"tr-odd-bg-color": "#353535",
+				"content-bg": "#4e4e4e",
+				"content-fg": "#fff",
+				"content-hover-bg": "#222",
+				"content-hover-fg": "#fff"
 			}
 		},
 	},
@@ -120,6 +124,10 @@ const colorPalettes = {
 				"border-color": "#888",
 				"tr-even-bg-color": "#f9f9f9",
 				"tr-odd-bg-color": "#fff",
+				"content-bg": "#e0e0e0",
+				"content-fg": "#222",
+				"content-hover-bg": "#adadad",
+				"content-hover-fg": "#222"
 			}
 		},
 	},
@@ -176,6 +184,10 @@ const colorPalettes = {
 				"border-color": "#657b83", // Base00
 				"tr-even-bg-color": "#002b36",
 				"tr-odd-bg-color": "#073642",
+				"content-bg": "#657b83",
+				"content-fg": "#fdf6e3",
+				"content-hover-bg": "#002b36",
+				"content-hover-fg": "#fdf6e3"
 			}
 		},
 	},
@@ -244,7 +256,11 @@ const colorPalettes = {
 			  "error-text": "#ff4444",
 			  "border-color": "#6e7581",
 			  "tr-even-bg-color": "#2b2f38",
-			  "tr-odd-bg-color": "#242730"
+			  "tr-odd-bg-color": "#242730",
+			  "content-bg": "#6e7581",
+			  "content-fg": "#fff",
+			  "content-hover-bg": "#2b2f38",
+			  "content-hover-fg": "#fff"
 			}
 		},
 	},
@@ -313,7 +329,11 @@ const colorPalettes = {
 			  "error-text": "#ff4444",
 			  "border-color": "#545d70",
 			  "tr-even-bg-color": "#2e3440",
-			  "tr-odd-bg-color": "#161b22"
+			  "tr-odd-bg-color": "#161b22",
+			  "content-bg": "#545d70",
+			  "content-fg": "#e5eaf0",
+			  "content-hover-bg": "#2e3440",
+			  "content-hover-fg": "#e5eaf0"
 			}
 		},
 	},
@@ -382,7 +402,11 @@ const colorPalettes = {
 			  "error-text": "#ff4444",
 			  "border-color": "#30363d",
 			  "tr-even-bg-color": "#161b22",
-			  "tr-odd-bg-color": "#13171d"
+			  "tr-odd-bg-color": "#13171d",
+			  "content-bg": "#30363d",
+			  "content-fg": "#e5eaf0",
+			  "content-hover-bg": "#161b22",
+			  "content-hover-fg": "#e5eaf0"
 			}
 		},
 	}
diff --git a/web/extensions/core/groupNode.js b/web/extensions/core/groupNode.js
index 0b0763d1d..9a2238908 100644
--- a/web/extensions/core/groupNode.js
+++ b/web/extensions/core/groupNode.js
@@ -1278,4 +1278,4 @@ const ext = {
 	}
 };
 
-app.registerExtension(ext);
+app.registerExtension(ext);
\ No newline at end of file
diff --git a/web/extensions/core/undoRedo.js b/web/extensions/core/undoRedo.js
deleted file mode 100644
index 900eed2a7..000000000
--- a/web/extensions/core/undoRedo.js
+++ /dev/null
@@ -1,177 +0,0 @@
-import { app } from "../../scripts/app.js";
-import { api } from "../../scripts/api.js"
-
-const MAX_HISTORY = 50;
-
-let undo = [];
-let redo = [];
-let activeState = null;
-let isOurLoad = false;
-function checkState() {
-	const currentState = app.graph.serialize();
-	if (!graphEqual(activeState, currentState)) {
-		undo.push(activeState);
-		if (undo.length > MAX_HISTORY) {
-			undo.shift();
-		}
-		activeState = clone(currentState);
-		redo.length = 0;
-		api.dispatchEvent(new CustomEvent("graphChanged", { detail: activeState }));
-	}
-}
-
-const loadGraphData = app.loadGraphData;
-app.loadGraphData = async function () {
-	const v = await loadGraphData.apply(this, arguments);
-	if (isOurLoad) {
-		isOurLoad = false;
-	} else {
-		checkState();
-	}
-	return v;
-};
-
-function clone(obj) {
-	try {
-		if (typeof structuredClone !== "undefined") {
-			return structuredClone(obj);
-		}
-	} catch (error) {
-		// structuredClone is stricter than using JSON.parse/stringify so fallback to that
-	}
-
-	return JSON.parse(JSON.stringify(obj));
-}
-
-function graphEqual(a, b, root = true) {
-	if (a === b) return true;
-
-	if (typeof a == "object" && a && typeof b == "object" && b) {
-		const keys = Object.getOwnPropertyNames(a);
-
-		if (keys.length != Object.getOwnPropertyNames(b).length) {
-			return false;
-		}
-
-		for (const key of keys) {
-			let av = a[key];
-			let bv = b[key];
-			if (root && key === "nodes") {
-				// Nodes need to be sorted as the order changes when selecting nodes
-				av = [...av].sort((a, b) => a.id - b.id);
-				bv = [...bv].sort((a, b) => a.id - b.id);
-			}
-			if (!graphEqual(av, bv, false)) {
-				return false;
-			}
-		}
-
-		return true;
-	}
-
-	return false;
-}
-
-const undoRedo = async (e) => {
-	const updateState = async (source, target) => {
-		const prevState = source.pop();
-		if (prevState) {
-			target.push(activeState);
-			isOurLoad = true;
-			await app.loadGraphData(prevState, false);
-			activeState = prevState;
-		}
-	}
-	if (e.ctrlKey || e.metaKey) {
-		if (e.key === "y") {
-			updateState(redo, undo);
-			return true;
-		} else if (e.key === "z") {
-			updateState(undo, redo);
-			return true;
-		}
-	}
-};
-
-const bindInput = (activeEl) => {
-	if (activeEl && activeEl.tagName !== "CANVAS" && activeEl.tagName !== "BODY") {
-		for (const evt of ["change", "input", "blur"]) {
-			if (`on${evt}` in activeEl) {
-				const listener = () => {
-					checkState();
-					activeEl.removeEventListener(evt, listener);
-				};
-				activeEl.addEventListener(evt, listener);
-				return true;
-			}
-		}
-	}
-};
-
-let keyIgnored = false;
-window.addEventListener(
-	"keydown",
-	(e) => {
-		requestAnimationFrame(async () => {
-			let activeEl;
-			// If we are auto queue in change mode then we do want to trigger on inputs
-			if (!app.ui.autoQueueEnabled || app.ui.autoQueueMode === "instant") {
-				activeEl = document.activeElement;
-				if (activeEl?.tagName === "INPUT" || activeEl?.type === "textarea") {
-					// Ignore events on inputs, they have their native history
-					return;
-				}
-			}
-		
-			keyIgnored = e.key === "Control" || e.key === "Shift" || e.key === "Alt" || e.key === "Meta";
-			if (keyIgnored) return;
-
-			// Check if this is a ctrl+z ctrl+y
-			if (await undoRedo(e)) return;
-
-			// If our active element is some type of input then handle changes after they're done
-			if (bindInput(activeEl)) return;
-			checkState();
-		});
-	},
-	true
-);
-
-window.addEventListener("keyup", (e) => {
-	if (keyIgnored) {
-		keyIgnored = false;
-		checkState();
-	}
-});
-
-// Handle clicking DOM elements (e.g. widgets)
-window.addEventListener("mouseup", () => {
-	checkState();
-});
-
-// Handle prompt queue event for dynamic widget changes
-api.addEventListener("promptQueued", () => {
-	checkState();
-});
-
-// Handle litegraph clicks
-const processMouseUp = LGraphCanvas.prototype.processMouseUp;
-LGraphCanvas.prototype.processMouseUp = function (e) {
-	const v = processMouseUp.apply(this, arguments);
-	checkState();
-	return v;
-};
-const processMouseDown = LGraphCanvas.prototype.processMouseDown;
-LGraphCanvas.prototype.processMouseDown = function (e) {
-	const v = processMouseDown.apply(this, arguments);
-	checkState();
-	return v;
-};
-
-// Handle litegraph context menu for COMBO widgets
-const close = LiteGraph.ContextMenu.prototype.close;
-LiteGraph.ContextMenu.prototype.close = function(e) {
-	const v = close.apply(this, arguments);
-	checkState();
-	return v;
-}
\ No newline at end of file
diff --git a/web/fonts/materialdesignicons-webfont.woff2 b/web/fonts/materialdesignicons-webfont.woff2
new file mode 100644
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diff --git a/web/index.html b/web/index.html
index 094db9d15..5ebf03c01 100644
--- a/web/index.html
+++ b/web/index.html
@@ -5,6 +5,7 @@
 		<title>ComfyUI</title>
 		<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
 		<link rel="stylesheet" type="text/css" href="./lib/litegraph.css" />
+		<link rel="stylesheet" type="text/css" href="./lib/materialdesignicons.min.css" />
 		<link rel="stylesheet" type="text/css" href="./style.css" />
 		<link rel="stylesheet" type="text/css" href="./user.css" />
 		<script type="text/javascript" src="./lib/litegraph.core.js"></script>
diff --git a/web/jsconfig.json b/web/jsconfig.json
index b65fa2746..cd55a0cc7 100644
--- a/web/jsconfig.json
+++ b/web/jsconfig.json
@@ -4,7 +4,9 @@
 		"paths": {
 			"/*": ["./*"]
 		},
-		"lib": ["DOM", "ES2022"]
+		"lib": ["DOM", "ES2022", "DOM.Iterable"],
+		"target": "ES2015",
+		"module": "es2020"
 	},
 	"include": ["."]
 }
diff --git a/web/lib/materialdesignicons.min.css b/web/lib/materialdesignicons.min.css
new file mode 100644
index 000000000..459ce9ea8
--- /dev/null
+++ b/web/lib/materialdesignicons.min.css
@@ -0,0 +1,3 @@
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::before{content:"\F02E5"}.mdi-pot-mix::before{content:"\F065B"}.mdi-pot-mix-outline::before{content:"\F0677"}.mdi-pot-outline::before{content:"\F02FF"}.mdi-pot-steam::before{content:"\F065A"}.mdi-pot-steam-outline::before{content:"\F0326"}.mdi-pound::before{content:"\F0423"}.mdi-pound-box::before{content:"\F0424"}.mdi-pound-box-outline::before{content:"\F117F"}.mdi-power::before{content:"\F0425"}.mdi-power-cycle::before{content:"\F0901"}.mdi-power-off::before{content:"\F0902"}.mdi-power-on::before{content:"\F0903"}.mdi-power-plug::before{content:"\F06A5"}.mdi-power-plug-battery::before{content:"\F1C3B"}.mdi-power-plug-battery-outline::before{content:"\F1C3C"}.mdi-power-plug-off::before{content:"\F06A6"}.mdi-power-plug-off-outline::before{content:"\F1424"}.mdi-power-plug-outline::before{content:"\F1425"}.mdi-power-settings::before{content:"\F0426"}.mdi-power-sleep::before{content:"\F0904"}.mdi-power-socket::before{content:"\F0427"}.mdi-power-socket-au::before{content:"\F0905"}.mdi-power-socket-ch::before{content:"\F0FB3"}.mdi-power-socket-de::before{content:"\F1107"}.mdi-power-socket-eu::before{content:"\F07E7"}.mdi-power-socket-fr::before{content:"\F1108"}.mdi-power-socket-it::before{content:"\F14FF"}.mdi-power-socket-jp::before{content:"\F1109"}.mdi-power-socket-uk::before{content:"\F07E8"}.mdi-power-socket-us::before{content:"\F07E9"}.mdi-power-standby::before{content:"\F0906"}.mdi-powershell::before{content:"\F0A0A"}.mdi-prescription::before{content:"\F0706"}.mdi-presentation::before{content:"\F0428"}.mdi-presentation-play::before{content:"\F0429"}.mdi-pretzel::before{content:"\F1562"}.mdi-printer::before{content:"\F042A"}.mdi-printer-3d::before{content:"\F042B"}.mdi-printer-3d-nozzle::before{content:"\F0E5B"}.mdi-printer-3d-nozzle-alert::before{content:"\F11C0"}.mdi-printer-3d-nozzle-alert-outline::before{content:"\F11C1"}.mdi-printer-3d-nozzle-heat::before{content:"\F18B8"}.mdi-printer-3d-nozzle-heat-outline::before{content:"\F18B9"}.mdi-printer-3d-nozzle-off::before{content:"\F1B19"}.mdi-printer-3d-nozzle-off-outline::before{content:"\F1B1A"}.mdi-printer-3d-nozzle-outline::before{content:"\F0E5C"}.mdi-printer-3d-off::before{content:"\F1B0E"}.mdi-printer-alert::before{content:"\F042C"}.mdi-printer-check::before{content:"\F1146"}.mdi-printer-eye::before{content:"\F1458"}.mdi-printer-off::before{content:"\F0E5D"}.mdi-printer-off-outline::before{content:"\F1785"}.mdi-printer-outline::before{content:"\F1786"}.mdi-printer-pos::before{content:"\F1057"}.mdi-printer-pos-alert::before{content:"\F1BBC"}.mdi-printer-pos-alert-outline::before{content:"\F1BBD"}.mdi-printer-pos-cancel::before{content:"\F1BBE"}.mdi-printer-pos-cancel-outline::before{content:"\F1BBF"}.mdi-printer-pos-check::before{content:"\F1BC0"}.mdi-printer-pos-check-outline::before{content:"\F1BC1"}.mdi-printer-pos-cog::before{content:"\F1BC2"}.mdi-printer-pos-cog-outline::before{content:"\F1BC3"}.mdi-printer-pos-edit::before{content:"\F1BC4"}.mdi-printer-pos-edit-outline::before{content:"\F1BC5"}.mdi-printer-pos-minus::before{content:"\F1BC6"}.mdi-printer-pos-minus-outline::before{content:"\F1BC7"}.mdi-printer-pos-network::before{content:"\F1BC8"}.mdi-printer-pos-network-outline::before{content:"\F1BC9"}.mdi-printer-pos-off::before{content:"\F1BCA"}.mdi-printer-pos-off-outline::before{content:"\F1BCB"}.mdi-printer-pos-outline::before{content:"\F1BCC"}.mdi-printer-pos-pause::before{content:"\F1BCD"}.mdi-printer-pos-pause-outline::before{content:"\F1BCE"}.mdi-printer-pos-play::before{content:"\F1BCF"}.mdi-printer-pos-play-outline::before{content:"\F1BD0"}.mdi-printer-pos-plus::before{content:"\F1BD1"}.mdi-printer-pos-plus-outline::before{content:"\F1BD2"}.mdi-printer-pos-refresh::before{content:"\F1BD3"}.mdi-printer-pos-refresh-outline::before{content:"\F1BD4"}.mdi-printer-pos-remove::before{content:"\F1BD5"}.mdi-printer-pos-remove-outline::before{content:"\F1BD6"}.mdi-printer-pos-star::before{content:"\F1BD7"}.mdi-printer-pos-star-outline::before{content:"\F1BD8"}.mdi-printer-pos-stop::before{content:"\F1BD9"}.mdi-printer-pos-stop-outline::before{content:"\F1BDA"}.mdi-printer-pos-sync::before{content:"\F1BDB"}.mdi-printer-pos-sync-outline::before{content:"\F1BDC"}.mdi-printer-pos-wrench::before{content:"\F1BDD"}.mdi-printer-pos-wrench-outline::before{content:"\F1BDE"}.mdi-printer-search::before{content:"\F1457"}.mdi-printer-settings::before{content:"\F0707"}.mdi-printer-wireless::before{content:"\F0A0B"}.mdi-priority-high::before{content:"\F0603"}.mdi-priority-low::before{content:"\F0604"}.mdi-professional-hexagon::before{content:"\F042D"}.mdi-progress-alert::before{content:"\F0CBC"}.mdi-progress-check::before{content:"\F0995"}.mdi-progress-clock::before{content:"\F0996"}.mdi-progress-close::before{content:"\F110A"}.mdi-progress-download::before{content:"\F0997"}.mdi-progress-helper::before{content:"\F1BA2"}.mdi-progress-pencil::before{content:"\F1787"}.mdi-progress-question::before{content:"\F1522"}.mdi-progress-star::before{content:"\F1788"}.md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efore{-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.mdi-rotate-225:before{-webkit-transform:rotate(225deg);-ms-transform:rotate(225deg);transform:rotate(225deg)}.mdi-rotate-270:before{-webkit-transform:rotate(270deg);-ms-transform:rotate(270deg);transform:rotate(270deg)}.mdi-rotate-315:before{-webkit-transform:rotate(315deg);-ms-transform:rotate(315deg);transform:rotate(315deg)}.mdi-flip-h:before{-webkit-transform:scaleX(-1);transform:scaleX(-1);filter:FlipH;-ms-filter:"FlipH"}.mdi-flip-v:before{-webkit-transform:scaleY(-1);transform:scaleY(-1);filter:FlipV;-ms-filter:"FlipV"}.mdi-spin:before{-webkit-animation:mdi-spin 2s infinite linear;animation:mdi-spin 2s infinite linear}@-webkit-keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}
+
+/*# sourceMappingURL=materialdesignicons.css.map */
\ No newline at end of file
diff --git a/web/scripts/api.js b/web/scripts/api.js
index 8c8155be6..39f0a9bb2 100644
--- a/web/scripts/api.js
+++ b/web/scripts/api.js
@@ -327,7 +327,7 @@ class ComfyApi extends EventTarget {
 
 	/**
 	 * Gets user configuration data and where data should be stored
-	 * @returns { Promise<{ storage: "server" | "browser", users?: Promise<string, unknown>, migrated?: boolean }> } 
+	 * @returns { Promise<{ storage: "server" | "browser", users?: Promise<string, unknown>, migrated?: boolean }> }
 	 */
 	async getUserConfig() {
 		return (await this.fetchApi("/users")).json();
@@ -335,7 +335,7 @@ class ComfyApi extends EventTarget {
 
 	/**
 	 * Creates a new user
-	 * @param { string } username 
+	 * @param { string } username
 	 * @returns The fetch response
 	 */
 	createUser(username) {
@@ -394,7 +394,7 @@ class ComfyApi extends EventTarget {
 	 * Gets a user data file for the current user
 	 * @param { string } file The name of the userdata file to load
 	 * @param { RequestInit } [options]
-	 * @returns { Promise<unknown> } The fetch response object
+	 * @returns { Promise<Response> } The fetch response object
 	 */
 	async getUserData(file, options) {
 		return this.fetchApi(`/userdata/${encodeURIComponent(file)}`, options);
@@ -404,18 +404,75 @@ class ComfyApi extends EventTarget {
 	 * Stores a user data file for the current user
 	 * @param { string } file The name of the userdata file to save
 	 * @param { unknown } data The data to save to the file
-	 * @param { RequestInit & { stringify?: boolean, throwOnError?: boolean } } [options]
-	 * @returns { Promise<void> }
+	 * @param { RequestInit & { overwrite?: boolean, stringify?: boolean, throwOnError?: boolean } } [options]
+	 * @returns { Promise<Response> }
 	 */
-	async storeUserData(file, data, options = { stringify: true, throwOnError: true }) {
-		const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}`, {
+	async storeUserData(file, data, options = { overwrite: true, stringify: true, throwOnError: true }) {
+		const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}?overwrite=${options?.overwrite}`, {
 			method: "POST",
 			body: options?.stringify ? JSON.stringify(data) : data,
 			...options,
-		});	
-		if (resp.status !== 200) {
+		});
+		if (resp.status !== 200 && options?.throwOnError !== false) {
 			throw new Error(`Error storing user data file '${file}': ${resp.status} ${(await resp).statusText}`);
 		}
+		return resp;
+	}
+
+	/**
+	 * Deletes a user data file for the current user
+	 * @param { string } file The name of the userdata file to delete
+	 */
+	async deleteUserData(file) {
+		const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}`, {
+			method: "DELETE",
+		});
+		if (resp.status !== 204) {
+			throw new Error(`Error removing user data file '${file}': ${resp.status} ${(resp).statusText}`);
+		}
+	}
+
+	/**
+	 * Move a user data file for the current user
+	 * @param { string } source The userdata file to move
+	 * @param { string } dest The destination for the file
+	 */
+	async moveUserData(source, dest, options = { overwrite: false }) {
+		const resp = await this.fetchApi(`/userdata/${encodeURIComponent(source)}/move/${encodeURIComponent(dest)}?overwrite=${options?.overwrite}`, {
+			method: "POST",
+		});
+		return resp;
+	}
+
+	/**
+	 * @overload
+	 * Lists user data files for the current user
+	 * @param { string } dir The directory in which to list files
+	 * @param { boolean } [recurse] If the listing should be recursive
+	 * @param { true } [split] If the paths should be split based on the os path separator
+	 * @returns { Promise<string[][]>> } The list of split file paths in the format [fullPath, ...splitPath]
+	 */
+	/**
+	 * @overload
+	 * Lists user data files for the current user
+	 * @param { string } dir The directory in which to list files
+	 * @param { boolean } [recurse] If the listing should be recursive
+	 * @param { false | undefined } [split] If the paths should be split based on the os path separator
+	 * @returns { Promise<string[]>> } The list of files
+	 */
+	async listUserData(dir, recurse, split) {
+		const resp = await this.fetchApi(
+			`/userdata?${new URLSearchParams({
+				recurse,
+				dir,
+				split,
+			})}`
+		);
+		if (resp.status === 404) return [];
+		if (resp.status !== 200) {
+			throw new Error(`Error getting user data list '${dir}': ${resp.status} ${resp.statusText}`);
+		}
+		return resp.json();
 	}
 }
 
diff --git a/web/scripts/app.js b/web/scripts/app.js
index 58fb765c4..725538563 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -5,9 +5,11 @@ import { api } from "./api.js";
 import { defaultGraph } from "./defaultGraph.js";
 import { getPngMetadata, getWebpMetadata, importA1111, getLatentMetadata } from "./pnginfo.js";
 import { addDomClippingSetting } from "./domWidget.js";
-import { createImageHost, calculateImageGrid } from "./ui/imagePreview.js"
-
-export const ANIM_PREVIEW_WIDGET = "$$comfy_animation_preview"
+import { createImageHost, calculateImageGrid } from "./ui/imagePreview.js";
+import { ComfyAppMenu } from "./ui/menu/index.js";
+import { getStorageValue, setStorageValue } from "./utils.js";
+import { ComfyWorkflowManager } from "./workflows.js";
+export const ANIM_PREVIEW_WIDGET = "$$comfy_animation_preview";
 
 function sanitizeNodeName(string) {
 	let entityMap = {
@@ -52,6 +54,12 @@ export class ComfyApp {
 	constructor() {
 		this.ui = new ComfyUI(this);
 		this.logging = new ComfyLogging(this);
+		this.workflowManager = new ComfyWorkflowManager(this);
+		this.bodyTop = $el("div.comfyui-body-top", { parent: document.body });
+		this.bodyLeft = $el("div.comfyui-body-left", { parent: document.body });
+		this.bodyRight = $el("div.comfyui-body-right", { parent: document.body });
+		this.bodyBottom = $el("div.comfyui-body-bottom", { parent: document.body });		  
+		this.menu = new ComfyAppMenu(this);
 
 		/**
 		 * List of extensions that are registered with the app
@@ -1313,11 +1321,15 @@ export class ComfyApp {
 		});
 
 		api.addEventListener("progress", ({ detail }) => {
+			if (this.workflowManager.activePrompt?.workflow 
+				&& this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return;
 			this.progress = detail;
 			this.graph.setDirtyCanvas(true, false);
 		});
 
 		api.addEventListener("executing", ({ detail }) => {
+			if (this.workflowManager.activePrompt ?.workflow
+				&& this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return;
 			this.progress = null;
 			this.runningNodeId = detail;
 			this.graph.setDirtyCanvas(true, false);
@@ -1325,6 +1337,8 @@ export class ComfyApp {
 		});
 
 		api.addEventListener("executed", ({ detail }) => {
+			if (this.workflowManager.activePrompt ?.workflow
+				&& this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return;
 			const output = this.nodeOutputs[detail.node];
 			if (detail.merge && output) {
 				for (const k in detail.output ?? {}) {
@@ -1433,6 +1447,11 @@ export class ComfyApp {
 	    });
 	
 	    await Promise.all(extensionPromises);
+		try {
+			this.menu.workflows.registerExtension(this);
+		} catch (error) {
+			console.error(error);
+		}
 	}
 
 	async #migrateSettings() {
@@ -1520,15 +1539,17 @@ export class ComfyApp {
 	 */
 	async setup() {
 		await this.#setUser();
-		await this.ui.settings.load();
-		await this.#loadExtensions();
 
 		// Create and mount the LiteGraph in the DOM
 		const mainCanvas = document.createElement("canvas")
 		mainCanvas.style.touchAction = "none"
 		const canvasEl = (this.canvasEl = Object.assign(mainCanvas, { id: "graph-canvas" }));
 		canvasEl.tabIndex = "1";
-		document.body.prepend(canvasEl);
+		document.body.append(canvasEl);
+		this.resizeCanvas();
+
+		await Promise.all([this.workflowManager.loadWorkflows(), this.ui.settings.load()]);
+		await this.#loadExtensions();
 
 		addDomClippingSetting();
 		this.#addProcessMouseHandler();
@@ -1541,7 +1562,7 @@ export class ComfyApp {
 
 		this.#addAfterConfigureHandler();
 
-		const canvas = (this.canvas = new LGraphCanvas(canvasEl, this.graph));
+		this.canvas = new LGraphCanvas(canvasEl, this.graph);
 		this.ctx = canvasEl.getContext("2d");
 
 		LiteGraph.release_link_on_empty_shows_menu = true;
@@ -1549,19 +1570,14 @@ export class ComfyApp {
 
 		this.graph.start();
 
-		function resizeCanvas() {
-			// Limit minimal scale to 1, see https://github.com/comfyanonymous/ComfyUI/pull/845
-			const scale = Math.max(window.devicePixelRatio, 1);
-			const { width, height } = canvasEl.getBoundingClientRect();
-			canvasEl.width = Math.round(width * scale);
-			canvasEl.height = Math.round(height * scale);
-			canvasEl.getContext("2d").scale(scale, scale);
-			canvas.draw(true, true);
-		}
-
 		// Ensure the canvas fills the window
-		resizeCanvas();
-		window.addEventListener("resize", resizeCanvas);
+		this.resizeCanvas();
+		window.addEventListener("resize", () => this.resizeCanvas());
+		const ro = new ResizeObserver(() => this.resizeCanvas());
+		ro.observe(this.bodyTop);
+		ro.observe(this.bodyLeft);
+		ro.observe(this.bodyRight);
+		ro.observe(this.bodyBottom);
 
 		await this.#invokeExtensionsAsync("init");
 		await this.registerNodes();
@@ -1573,7 +1589,8 @@ export class ComfyApp {
 			const loadWorkflow = async (json) => {
 				if (json) {
 					const workflow = JSON.parse(json);
-					await this.loadGraphData(workflow);
+					const workflowName = getStorageValue("Comfy.PreviousWorkflow");
+					await this.loadGraphData(workflow, true, workflowName);
 					return true;
 				}
 			};
@@ -1609,6 +1626,19 @@ export class ComfyApp {
 		await this.#invokeExtensionsAsync("setup");
 	}
 
+	resizeCanvas() {
+		// Limit minimal scale to 1, see https://github.com/comfyanonymous/ComfyUI/pull/845
+		const scale = Math.max(window.devicePixelRatio, 1);
+		
+		// Clear fixed width and height while calculating rect so it uses 100% instead
+		this.canvasEl.height = this.canvasEl.width = "";
+		const { width, height } = this.canvasEl.getBoundingClientRect();
+		this.canvasEl.width = Math.round(width * scale);
+		this.canvasEl.height = Math.round(height * scale);
+		this.canvasEl.getContext("2d").scale(scale, scale);
+		this.canvas?.draw(true, true);
+	}
+
 	/**
 	 * Registers nodes with the graph
 	 */
@@ -1795,12 +1825,29 @@ export class ComfyApp {
 		});
 	}
 
+	async changeWorkflow(callback, workflow = null) {
+		try {
+			this.workflowManager.activeWorkflow?.changeTracker?.store()
+		} catch (error) {
+			console.error(error);
+		}
+		await callback();
+		try {
+			this.workflowManager.setWorkflow(workflow);
+			this.workflowManager.activeWorkflow?.track()
+		} catch (error) {
+			console.error(error);
+		}
+	}
+
 	/**
 	 * Populates the graph with the specified workflow data
 	 * @param {*} graphData A serialized graph object
 	 * @param { boolean } clean If the graph state, e.g. images, should be cleared
+	 * @param { boolean } restore_view If the graph position should be restored
+	 * @param { import("./workflows.js").ComfyWorkflowInstance | null } workflow The workflow
 	 */
-	async loadGraphData(graphData, clean = true, restore_view = true) {
+	async loadGraphData(graphData, clean = true, restore_view = true, workflow = null) {
 		if (clean !== false) {
 			this.clean();
 		}
@@ -1818,6 +1865,12 @@ export class ComfyApp {
 		{
 			graphData = structuredClone(graphData);
 		}
+	
+		try {
+			this.workflowManager.setWorkflow(workflow);
+		} catch (error) {
+			console.error(error);
+		}
 
 		const missingNodeTypes = [];
 		await this.#invokeExtensionsAsync("beforeConfigureGraph", graphData, missingNodeTypes);
@@ -1840,6 +1893,11 @@ export class ComfyApp {
 				this.canvas.ds.offset = graphData.extra.ds.offset;
 				this.canvas.ds.scale = graphData.extra.ds.scale;
 			}
+			
+			try {
+				this.workflowManager.activeWorkflow?.track()
+			} catch (error) {
+			}
 		} catch (error) {
 			let errorHint = [];
 			// Try extracting filename to see if it was caused by an extension script
@@ -1927,14 +1985,17 @@ export class ComfyApp {
 			this.showMissingNodesError(missingNodeTypes);
 		}
 		await this.#invokeExtensionsAsync("afterConfigureGraph", missingNodeTypes);
+		requestAnimationFrame(() => {
+			this.graph.setDirtyCanvas(true, true);
+		});
 	}
 
 	/**
 	 * Converts the current graph workflow for sending to the API
 	 * @returns The workflow and node links
 	 */
-	async graphToPrompt() {
-		for (const outerNode of this.graph.computeExecutionOrder(false)) {
+	async graphToPrompt(graph = this.graph, clean = true) {
+		for (const outerNode of graph.computeExecutionOrder(false)) {
 			if (outerNode.widgets) {
 				for (const widget of outerNode.widgets) {
 					// Allow widgets to run callbacks before a prompt has been queued
@@ -1954,10 +2015,10 @@ export class ComfyApp {
 			}
 		}
 
-		const workflow = this.graph.serialize();
+		const workflow = graph.serialize();
 		const output = {};
 		// Process nodes in order of execution
-		for (const outerNode of this.graph.computeExecutionOrder(false)) {
+		for (const outerNode of graph.computeExecutionOrder(false)) {
 			const skipNode = outerNode.mode === 2 || outerNode.mode === 4;
 			const innerNodes = (!skipNode && outerNode.getInnerNodes) ? outerNode.getInnerNodes() : [outerNode];
 			for (const node of innerNodes) {
@@ -2049,13 +2110,14 @@ export class ComfyApp {
 		}
 
 		// Remove inputs connected to removed nodes
-
-		for (const o in output) {
-			for (const i in output[o].inputs) {
-				if (Array.isArray(output[o].inputs[i])
-					&& output[o].inputs[i].length === 2
-					&& !output[output[o].inputs[i][0]]) {
-					delete output[o].inputs[i];
+		if(clean) {
+			for (const o in output) {
+				for (const i in output[o].inputs) {
+					if (Array.isArray(output[o].inputs[i])
+						&& output[o].inputs[i].length === 2
+						&& !output[output[o].inputs[i][0]]) {
+						delete output[o].inputs[i];
+					}
 				}
 			}
 		}
@@ -2123,6 +2185,14 @@ export class ComfyApp {
 						this.lastNodeErrors = res.node_errors;
 						if (this.lastNodeErrors.length > 0) {
 							this.canvas.draw(true, true);
+						} else {
+							try {
+								this.workflowManager.storePrompt({
+									id: res.prompt_id,
+									nodes: Object.keys(p.output)
+								});
+							} catch (error) {
+							}
 						}
 					} catch (error) {
 						const formattedError = this.#formatPromptError(error)
@@ -2155,6 +2225,7 @@ export class ComfyApp {
 			this.#processingQueue = false;
 		}
 		api.dispatchEvent(new CustomEvent("promptQueued", { detail: { number, batchCount } }));
+		return !this.lastNodeErrors;
 	}
 
 	showErrorOnFileLoad(file) {
@@ -2170,14 +2241,24 @@ export class ComfyApp {
 	 * @param {File} file
 	 */
 	async handleFile(file) {
+		const removeExt = f => {
+			if(!f) return f;
+			const p = f.lastIndexOf(".");
+			if(p === -1) return f;
+			return f.substring(0, p);
+		};
+
+		const fileName = removeExt(file.name);
 		if (file.type === "image/png") {
 			const pngInfo = await getPngMetadata(file);
 			if (pngInfo?.workflow) {
-				await this.loadGraphData(JSON.parse(pngInfo.workflow));
+				await this.loadGraphData(JSON.parse(pngInfo.workflow), true, true, fileName);
 			} else if (pngInfo?.prompt) {
-				this.loadApiJson(JSON.parse(pngInfo.prompt));
+				this.loadApiJson(JSON.parse(pngInfo.prompt), fileName);
 			} else if (pngInfo?.parameters) {
-				importA1111(this.graph, pngInfo.parameters);
+				this.changeWorkflow(() => {
+					importA1111(this.graph, pngInfo.parameters);
+				}, fileName)
 			} else {
 				this.showErrorOnFileLoad(file);
 			}
@@ -2188,9 +2269,9 @@ export class ComfyApp {
 			const prompt = pngInfo?.prompt || pngInfo?.Prompt;
 
 			if (workflow) {
-				this.loadGraphData(JSON.parse(workflow));
+				this.loadGraphData(JSON.parse(workflow), true, true, fileName);
 			} else if (prompt) {
-				this.loadApiJson(JSON.parse(prompt));
+				this.loadApiJson(JSON.parse(prompt), fileName);
 			} else {
 				this.showErrorOnFileLoad(file);
 			}
@@ -2201,16 +2282,16 @@ export class ComfyApp {
 				if (jsonContent?.templates) {
 					this.loadTemplateData(jsonContent);
 				} else if(this.isApiJson(jsonContent)) {
-					this.loadApiJson(jsonContent);
+					this.loadApiJson(jsonContent, fileName);
 				} else {
-					await this.loadGraphData(jsonContent);
+					await this.loadGraphData(jsonContent, true, fileName);
 				}
 			};
 			reader.readAsText(file);
 		} else if (file.name?.endsWith(".latent") || file.name?.endsWith(".safetensors")) {
 			const info = await getLatentMetadata(file);
 			if (info.workflow) {
-				await this.loadGraphData(JSON.parse(info.workflow));
+				await this.loadGraphData(JSON.parse(info.workflow), true, fileName);
 			} else if (info.prompt) {
 				this.loadApiJson(JSON.parse(info.prompt));
 			} else {
@@ -2225,7 +2306,7 @@ export class ComfyApp {
 		return Object.values(data).every((v) => v.class_type);
 	}
 
-	loadApiJson(apiData) {
+	loadApiJson(apiData, fileName) {
 		const missingNodeTypes = Object.values(apiData).filter((n) => !LiteGraph.registered_node_types[n.class_type]);
 		if (missingNodeTypes.length) {
 			this.showMissingNodesError(missingNodeTypes.map(t => t.class_type), false);
@@ -2240,40 +2321,42 @@ export class ComfyApp {
 			node.id = isNaN(+id) ? id : +id;
 			node.title = data._meta?.title ?? node.title
 			app.graph.add(node);
+			graph.add(node);
 		}
 
-		for (const id of ids) {
-			const data = apiData[id];
-			const node = app.graph.getNodeById(id);
-			for (const input in data.inputs ?? {}) {
-				const value = data.inputs[input];
-				if (value instanceof Array) {
-					const [fromId, fromSlot] = value;
-					const fromNode = app.graph.getNodeById(fromId);
-					let toSlot = node.inputs?.findIndex((inp) => inp.name === input);
-					if (toSlot == null || toSlot === -1) {
-						try {
-							// Target has no matching input, most likely a converted widget
-							const widget = node.widgets?.find((w) => w.name === input);
-							if (widget && node.convertWidgetToInput?.(widget)) {
-								toSlot = node.inputs?.length - 1;
-							}
-						} catch (error) {}
-					}
-					if (toSlot != null || toSlot !== -1) {
-						fromNode.connect(fromSlot, node, toSlot);
-					}
-				} else {
-					const widget = node.widgets?.find((w) => w.name === input);
-					if (widget) {
-						widget.value = value;
-						widget.callback?.(value);
+		this.changeWorkflow(() => {
+			for (const id of ids) {
+				const data = apiData[id];
+				const node = app.graph.getNodeById(id);
+				for (const input in data.inputs ?? {}) {
+					const value = data.inputs[input];
+					if (value instanceof Array) {
+						const [fromId, fromSlot] = value;
+						const fromNode = app.graph.getNodeById(fromId);
+						let toSlot = node.inputs?.findIndex((inp) => inp.name === input);
+						if (toSlot == null || toSlot === -1) {
+							try {
+								// Target has no matching input, most likely a converted widget
+								const widget = node.widgets?.find((w) => w.name === input);
+								if (widget && node.convertWidgetToInput?.(widget)) {
+									toSlot = node.inputs?.length - 1;
+								}
+							} catch (error) {}
+						}
+						if (toSlot != null || toSlot !== -1) {
+							fromNode.connect(fromSlot, node, toSlot);
+						}
+					} else {
+						const widget = node.widgets?.find((w) => w.name === input);
+						if (widget) {
+							widget.value = value;
+							widget.callback?.(value);
+						}
 					}
 				}
 			}
-		}
-
-		app.graph.arrange();
+			app.graph.arrange();
+		}, fileName);
 	}
 
 	/**
diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js
new file mode 100644
index 000000000..59901d5fc
--- /dev/null
+++ b/web/scripts/changeTracker.js
@@ -0,0 +1,242 @@
+// @ts-check
+
+import { api } from "./api.js";
+import { clone } from "./utils.js";
+
+
+export class ChangeTracker {
+	static MAX_HISTORY = 50;
+	#app;
+	undo = [];
+	redo = [];
+	activeState = null;
+	isOurLoad = false;
+	/** @type { import("./workflows").ComfyWorkflow | null } */
+	workflow;
+
+	ds;
+	nodeOutputs;
+
+	get app() {
+		return this.#app ?? this.workflow.manager.app;
+	}
+
+	constructor(workflow) {
+		this.workflow = workflow;
+	}
+
+	#setApp(app) {
+		this.#app = app;
+	}
+
+	store() {
+		this.ds = { scale: this.app.canvas.ds.scale, offset: [...this.app.canvas.ds.offset] };
+	}
+
+	restore() {
+		if (this.ds) {
+			this.app.canvas.ds.scale = this.ds.scale;
+			this.app.canvas.ds.offset = this.ds.offset;
+		}
+		if (this.nodeOutputs) {
+			this.app.nodeOutputs = this.nodeOutputs;
+		}
+	}
+
+	checkState() {
+		if (!this.app.graph) return;
+
+		const currentState = this.app.graph.serialize();
+		if (!this.activeState) {
+			this.activeState = clone(currentState);
+			return;
+		}
+		if (!ChangeTracker.graphEqual(this.activeState, currentState)) {
+			this.undo.push(this.activeState);
+			if (this.undo.length > ChangeTracker.MAX_HISTORY) {
+				this.undo.shift();
+			}
+			this.activeState = clone(currentState);
+			this.redo.length = 0;
+			this.workflow.unsaved = true;
+			api.dispatchEvent(new CustomEvent("graphChanged", { detail: this.activeState }));
+		}
+	}
+
+	async updateState(source, target) {
+		const prevState = source.pop();
+		if (prevState) {
+			target.push(this.activeState);
+			this.isOurLoad = true;
+			await this.app.loadGraphData(prevState, false, false, this.workflow);
+			this.activeState = prevState;
+		}
+	}
+
+	async undoRedo(e) {
+		if (e.ctrlKey || e.metaKey) {
+			if (e.key === "y") {
+				this.updateState(this.redo, this.undo);
+				return true;
+			} else if (e.key === "z") {
+				this.updateState(this.undo, this.redo);
+				return true;
+			}
+		}
+	}
+
+	/** @param { import("./app.js").ComfyApp } app */
+	static init(app) {
+		const changeTracker = () => app.workflowManager.activeWorkflow?.changeTracker ?? globalTracker;
+		globalTracker.#setApp(app);
+
+		const loadGraphData = app.loadGraphData;
+		app.loadGraphData = async function () {
+			const v = await loadGraphData.apply(this, arguments);
+			const ct = changeTracker();
+			if (ct.isOurLoad) {
+				ct.isOurLoad = false;
+			} else {
+				ct.checkState();
+			}
+			return v;
+		};
+
+		let keyIgnored = false;
+		window.addEventListener(
+			"keydown",
+			(e) => {
+				requestAnimationFrame(async () => {
+					let activeEl;
+					// If we are auto queue in change mode then we do want to trigger on inputs
+					if (!app.ui.autoQueueEnabled || app.ui.autoQueueMode === "instant") {
+						activeEl = document.activeElement;
+						if (activeEl?.tagName === "INPUT" || activeEl?.["type"] === "textarea") {
+							// Ignore events on inputs, they have their native history
+							return;
+						}
+					}
+
+					keyIgnored = e.key === "Control" || e.key === "Shift" || e.key === "Alt" || e.key === "Meta";
+					if (keyIgnored) return;
+
+					// Check if this is a ctrl+z ctrl+y
+					if (await changeTracker().undoRedo(e)) return;
+
+					// If our active element is some type of input then handle changes after they're done
+					if (ChangeTracker.bindInput(activeEl)) return;
+					changeTracker().checkState();
+				});
+			},
+			true
+		);
+
+		window.addEventListener("keyup", (e) => {
+			if (keyIgnored) {
+				keyIgnored = false;
+				changeTracker().checkState();
+			}
+		});
+
+		// Handle clicking DOM elements (e.g. widgets)
+		window.addEventListener("mouseup", () => {
+			changeTracker().checkState();
+		});
+
+		// Handle prompt queue event for dynamic widget changes
+		api.addEventListener("promptQueued", () => {
+			changeTracker().checkState();
+		});
+
+		// Handle litegraph clicks
+		const processMouseUp = LGraphCanvas.prototype.processMouseUp;
+		LGraphCanvas.prototype.processMouseUp = function (e) {
+			const v = processMouseUp.apply(this, arguments);
+			changeTracker().checkState();
+			return v;
+		};
+		const processMouseDown = LGraphCanvas.prototype.processMouseDown;
+		LGraphCanvas.prototype.processMouseDown = function (e) {
+			const v = processMouseDown.apply(this, arguments);
+			changeTracker().checkState();
+			return v;
+		};
+
+		// Handle litegraph context menu for COMBO widgets
+		const close = LiteGraph.ContextMenu.prototype.close;
+		LiteGraph.ContextMenu.prototype.close = function (e) {
+			const v = close.apply(this, arguments);
+			changeTracker().checkState();
+			return v;
+		};
+
+		// Store node outputs
+		api.addEventListener("executed", ({ detail }) => {
+			const prompt = app.workflowManager.queuedPrompts[detail.prompt_id];
+			if (!prompt?.workflow) return;
+			const nodeOutputs = (prompt.workflow.changeTracker.nodeOutputs ??= {});
+			const output = nodeOutputs[detail.node];
+			if (detail.merge && output) {
+				for (const k in detail.output ?? {}) {
+					const v = output[k];
+					if (v instanceof Array) {
+						output[k] = v.concat(detail.output[k]);
+					} else {
+						output[k] = detail.output[k];
+					}
+				}
+			} else {
+				nodeOutputs[detail.node] = detail.output;
+			}
+		});
+	}
+
+	static bindInput(app, activeEl) {
+		if (activeEl && activeEl.tagName !== "CANVAS" && activeEl.tagName !== "BODY") {
+			for (const evt of ["change", "input", "blur"]) {
+				if (`on${evt}` in activeEl) {
+					const listener = () => {
+						app.workflowManager.activeWorkflow.changeTracker.checkState();
+						activeEl.removeEventListener(evt, listener);
+					};
+					activeEl.addEventListener(evt, listener);
+					return true;
+				}
+			}
+		}
+	}
+
+	static graphEqual(a, b, path = "") {
+		if (a === b) return true;
+
+		if (typeof a == "object" && a && typeof b == "object" && b) {
+			const keys = Object.getOwnPropertyNames(a);
+
+			if (keys.length != Object.getOwnPropertyNames(b).length) {
+				return false;
+			}
+
+			for (const key of keys) {
+				let av = a[key];
+				let bv = b[key];
+				if (!path && key === "nodes") {
+					// Nodes need to be sorted as the order changes when selecting nodes
+					av = [...av].sort((a, b) => a.id - b.id);
+					bv = [...bv].sort((a, b) => a.id - b.id);
+				} else if (path === "extra.ds") {
+					// Ignore view changes
+					continue;
+				}
+				if (!ChangeTracker.graphEqual(av, bv, path + (path ? "." : "") + key)) {
+					return false;
+				}
+			}
+
+			return true;
+		}
+
+		return false;
+	}
+}
+
+const globalTracker = new ChangeTracker({});
\ No newline at end of file
diff --git a/web/scripts/domWidget.js b/web/scripts/domWidget.js
index b7f437ad2..d97122f9d 100644
--- a/web/scripts/domWidget.js
+++ b/web/scripts/domWidget.js
@@ -34,8 +34,8 @@ function getClipPath(node, element) {
 		}
 
 		const widgetRect = element.getBoundingClientRect();
-		const clipX = intersection[0] - widgetRect.x / scale + "px";
-		const clipY = intersection[1] - widgetRect.y / scale + "px";
+		const clipX = elRect.left + intersection[0] - widgetRect.x / scale + "px";
+		const clipY = elRect.top + intersection[1] - widgetRect.y / scale + "px";
 		const clipWidth = intersection[2] + "px";
 		const clipHeight = intersection[3] + "px";
 		const path = `polygon(0% 0%, 0% 100%, ${clipX} 100%, ${clipX} ${clipY}, calc(${clipX} + ${clipWidth}) ${clipY}, calc(${clipX} + ${clipWidth}) calc(${clipY} + ${clipHeight}), ${clipX} calc(${clipY} + ${clipHeight}), ${clipX} 100%, 100% 100%, 100% 0%)`;
@@ -210,7 +210,9 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) {
 	if (!element.parentElement) {
 		document.body.append(element);
 	}
-
+	element.hidden = true;
+	element.style.display = "none";
+	
 	let mouseDownHandler;
 	if (element.blur) {
 		mouseDownHandler = (event) => {
@@ -254,15 +256,15 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) {
 			const transform = new DOMMatrix()
 				.scaleSelf(elRect.width / ctx.canvas.width, elRect.height / ctx.canvas.height)
 				.multiplySelf(ctx.getTransform())
-				.translateSelf(margin, margin + y);
+				.translateSelf(margin, margin + y );
 
 			const scale = new DOMMatrix().scaleSelf(transform.a, transform.d);
 
 			Object.assign(element.style, {
 				transformOrigin: "0 0",
 				transform: scale,
-				left: `${transform.a + transform.e}px`,
-				top: `${transform.d + transform.f}px`,
+				left: `${transform.a + transform.e + elRect.left}px`,
+				top: `${transform.d + transform.f + elRect.top}px`,
 				width: `${widgetWidth - margin * 2}px`,
 				height: `${(widget.computedHeight ?? 50) - margin * 2}px`,
 				position: "absolute",
diff --git a/web/scripts/ui.js b/web/scripts/ui.js
index 72e43d357..87b4887eb 100644
--- a/web/scripts/ui.js
+++ b/web/scripts/ui.js
@@ -6,17 +6,22 @@ import { ComfySettingsDialog } from "./ui/settings.js";
 export const ComfyDialog = _ComfyDialog;
 
 /**
- * 
- * @param { string } tag HTML Element Tag and optional classes e.g. div.class1.class2
- * @param { string | Element | Element[] | {
+ * @template { string | (keyof HTMLElementTagNameMap) } K
+ * @typedef { K extends keyof HTMLElementTagNameMap ? HTMLElementTagNameMap[K] : HTMLElement } ElementType 
+ */
+
+/**
+ * @template { string | (keyof HTMLElementTagNameMap) } K 
+ * @param { K } tag HTML Element Tag and optional classes e.g. div.class1.class2
+ * @param { string | Element | Element[] | ({
  * 	 parent?: Element,
- *   $?: (el: Element) => void, 
+ *   $?: (el: ElementType<K>) => void, 
  *   dataset?: DOMStringMap,
- *   style?: CSSStyleDeclaration,
+ *   style?: Partial<CSSStyleDeclaration>,
  * 	 for?: string
- * } | undefined } propsOrChildren 
- * @param { Element[] | undefined } [children]
- * @returns 
+ * } & Omit<Partial<ElementType<K>>, "style">) | undefined } [propsOrChildren]
+ * @param { string | Element | Element[] | undefined } [children]
+ * @returns { ElementType<K> }
  */
 export function $el(tag, propsOrChildren, children) {
 	const split = tag.split(".");
@@ -54,7 +59,7 @@ export function $el(tag, propsOrChildren, children) {
 
 			Object.assign(element, propsOrChildren);
 			if (children) {
-				element.append(...(children instanceof Array ? children : [children]));
+				element.append(...(children instanceof Array ? children.filter(Boolean) : [children]));
 			}
 
 			if (parent) {
@@ -102,6 +107,8 @@ function dragElement(dragEl, settings) {
 	}
 
 	function positionElement() {
+		if(dragEl.style.display === "none") return;
+
 		const halfWidth = document.body.clientWidth / 2;
 		const anchorRight = newPosX + dragEl.clientWidth / 2 > halfWidth;
 
@@ -191,6 +198,8 @@ function dragElement(dragEl, settings) {
 		document.onmouseup = null;
 		document.onmousemove = null;
 	}
+
+	return restorePos;
 }
 
 class ComfyList {
@@ -372,6 +381,8 @@ export class ComfyUI {
 			},
 		});
 
+		this.loadFile = () => fileInput.click();
+
 		const autoQueueModeEl = toggleSwitch(
 			"autoQueueMode",
 			[
@@ -592,6 +603,7 @@ export class ComfyUI {
 				onclick: () => app.refreshComboInNodes()
 			}),
 			$el("button", {id: "comfy-clipspace-button", textContent: "Clipspace", onclick: () => app.openClipspace()}),
+			$el("button", {id: "comfy-reset-view-button", textContent: "Reset View", onclick: () => app.resetView()}),
 			$el("button", {
 				id: "comfy-clear-button", textContent: "Clear", onclick: () => {
 					if (!confirmClear.value || confirm("Clear workflow?")) {
@@ -621,10 +633,10 @@ export class ComfyUI {
 			name: "Enable Dev mode Options",
 			type: "boolean",
 			defaultValue: false,
-			onChange: function(value) { document.getElementById("comfy-dev-save-api-button").style.display = value ? "block" : "none"},
+			onChange: function(value) { document.getElementById("comfy-dev-save-api-button").style.display = value ? "flex" : "none"},
 		});
 
-		dragElement(this.menuContainer, this.settings);
+		this.restoreMenuPosition = dragElement(this.menuContainer, this.settings);
 
 		this.setStatus({exec_info: {queue_remaining: "X"}});
 	}
diff --git a/web/scripts/ui/components/asyncDialog.js b/web/scripts/ui/components/asyncDialog.js
new file mode 100644
index 000000000..434ce4b30
--- /dev/null
+++ b/web/scripts/ui/components/asyncDialog.js
@@ -0,0 +1,64 @@
+import { ComfyDialog } from "../dialog.js";
+import { $el } from "../../ui.js";
+
+export class ComfyAsyncDialog extends ComfyDialog {
+	#resolve;
+
+	constructor(actions) {
+		super(
+			"dialog.comfy-dialog.comfyui-dialog",
+			actions?.map((opt) => {
+				if (typeof opt === "string") {
+					opt = { text: opt };
+				}
+				return $el("button.comfyui-button", {
+					type: "button",
+					textContent: opt.text,
+					onclick: () => this.close(opt.value ?? opt.text),
+				});
+			})
+		);
+	}
+
+	show(html) {
+		this.element.addEventListener("close", () => {
+			this.close();
+		});
+
+		super.show(html);
+
+		return new Promise((resolve) => {
+			this.#resolve = resolve;
+		});
+	}
+
+	showModal(html) {
+		this.element.addEventListener("close", () => {
+			this.close();
+		});
+
+		super.show(html);
+		this.element.showModal();
+
+		return new Promise((resolve) => {
+			this.#resolve = resolve;
+		});
+	}
+
+	close(result = null) {
+		this.#resolve(result);
+		this.element.close();
+		super.close();
+	}
+
+	static async prompt({ title = null, message, actions }) {
+		const dialog = new ComfyAsyncDialog(actions);
+		const content = [$el("span", message)];
+		if (title) {
+			content.unshift($el("h3", title));
+		}
+		const res = await dialog.showModal(content);
+		dialog.element.remove();
+		return res;
+	}
+}
diff --git a/web/scripts/ui/components/button.js b/web/scripts/ui/components/button.js
new file mode 100644
index 000000000..25e5aeebd
--- /dev/null
+++ b/web/scripts/ui/components/button.js
@@ -0,0 +1,163 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { applyClasses, toggleElement } from "../utils.js";
+import { prop } from "../../utils.js";
+
+/**
+ * @typedef {{
+ *    icon?: string;
+ *    overIcon?: string;
+ *    iconSize?: number;
+ *    content?: string | HTMLElement;
+ *    tooltip?: string;
+ *    enabled?: boolean;
+ *    action?: (e: Event, btn: ComfyButton) => void,
+ *    classList?: import("../utils.js").ClassList,
+ * 	  visibilitySetting?: { id: string, showValue: any },
+ * 	  app?: import("../../app.js").ComfyApp
+ * }} ComfyButtonProps
+ */
+export class ComfyButton {
+	#over = 0;
+	#popupOpen = false;
+	isOver = false;	
+	iconElement = $el("i.mdi");
+	contentElement = $el("span");
+	/**
+	 * @type {import("./popup.js").ComfyPopup}
+	 */
+	popup;
+
+	/**
+	 * @param {ComfyButtonProps} opts
+	 */
+	constructor({
+		icon,
+		overIcon,
+		iconSize,
+		content,
+		tooltip,
+		action,
+		classList = "comfyui-button",
+		visibilitySetting,
+		app,
+		enabled = true,
+	}) {
+		this.element = $el("button", {
+			onmouseenter: () => {
+				this.isOver = true;
+				if(this.overIcon) {
+					this.updateIcon();
+				}
+			},
+			onmouseleave: () => {
+				this.isOver = false;
+				if(this.overIcon) {
+					this.updateIcon();
+				}
+			}
+
+		}, [this.iconElement, this.contentElement]);
+
+		this.icon = prop(this, "icon", icon, toggleElement(this.iconElement, { onShow: this.updateIcon }));
+		this.overIcon = prop(this, "overIcon", overIcon, () => {
+			if(this.isOver) {
+				this.updateIcon();
+			}
+		});
+		this.iconSize = prop(this, "iconSize", iconSize, this.updateIcon);
+		this.content = prop(
+			this,
+			"content",
+			content,
+			toggleElement(this.contentElement, {
+				onShow: (el, v) => {
+					if (typeof v === "string") {
+						el.textContent = v;
+					} else {
+						el.replaceChildren(v);
+					}
+				},
+			})
+		);
+
+		this.tooltip = prop(this, "tooltip", tooltip, (v) => {
+			if (v) {
+				this.element.title = v;
+			} else {
+				this.element.removeAttribute("title");
+			}
+		});
+		this.classList = prop(this, "classList", classList, this.updateClasses);
+		this.hidden = prop(this, "hidden", false, this.updateClasses);
+		this.enabled = prop(this, "enabled", enabled, () => {
+			this.updateClasses();
+			this.element.disabled = !this.enabled;
+		});
+		this.action = prop(this, "action", action);
+		this.element.addEventListener("click", (e) => {
+			if (this.popup) {
+				// we are either a touch device or triggered by click not hover
+				if (!this.#over) {
+					this.popup.toggle();
+				}
+			}
+			this.action?.(e, this);
+		});
+
+		if (visibilitySetting?.id) {
+			const settingUpdated = () => {
+				this.hidden = app.ui.settings.getSettingValue(visibilitySetting.id) !== visibilitySetting.showValue;
+			};
+			app.ui.settings.addEventListener(visibilitySetting.id + ".change", settingUpdated);
+			settingUpdated();
+		}
+	}
+
+	updateIcon = () => (this.iconElement.className = `mdi mdi-${(this.isOver && this.overIcon) || this.icon}${this.iconSize ? " mdi-" + this.iconSize + "px" : ""}`);
+	updateClasses = () => {
+		const internalClasses = [];
+		if (this.hidden) {
+			internalClasses.push("hidden");
+		}
+		if (!this.enabled) {
+			internalClasses.push("disabled");
+		}
+		if (this.popup) {
+			if (this.#popupOpen) {
+				internalClasses.push("popup-open");
+			} else {
+				internalClasses.push("popup-closed");
+			}
+		}
+		applyClasses(this.element, this.classList, ...internalClasses);
+	};
+
+	/**
+	 *
+	 * @param { import("./popup.js").ComfyPopup } popup
+	 * @param { "click" | "hover" } mode
+	 */
+	withPopup(popup, mode = "click") {
+		this.popup = popup;
+
+		if (mode === "hover") {
+			for (const el of [this.element, this.popup.element]) {
+				el.addEventListener("mouseenter", () => {
+					this.popup.open = !!++this.#over;
+				});
+				el.addEventListener("mouseleave", () => {
+					this.popup.open = !!--this.#over;
+				});
+			}
+		}
+
+		popup.addEventListener("change", () => {
+			this.#popupOpen = popup.open;
+			this.updateClasses();
+		});
+
+		return this;
+	}
+}
diff --git a/web/scripts/ui/components/buttonGroup.js b/web/scripts/ui/components/buttonGroup.js
new file mode 100644
index 000000000..573572fd0
--- /dev/null
+++ b/web/scripts/ui/components/buttonGroup.js
@@ -0,0 +1,45 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { ComfyButton } from "./button.js";
+import { prop } from "../../utils.js";
+
+export class ComfyButtonGroup {
+	element = $el("div.comfyui-button-group");
+
+	/** @param {Array<ComfyButton | HTMLElement>} buttons */
+	constructor(...buttons) {
+		this.buttons = prop(this, "buttons", buttons, () => this.update());
+	}
+
+	/**
+	 * @param {ComfyButton} button
+	 * @param {number} index
+	 */
+	insert(button, index) {
+		this.buttons.splice(index, 0, button);
+		this.update();
+	}
+
+	/** @param {ComfyButton} button */
+	append(button) {
+		this.buttons.push(button);
+		this.update();
+	}
+
+	/** @param {ComfyButton|number} indexOrButton */
+	remove(indexOrButton) {
+		if (typeof indexOrButton !== "number") {
+			indexOrButton = this.buttons.indexOf(indexOrButton);
+		}
+		if (indexOrButton > -1) {
+			const r = this.buttons.splice(indexOrButton, 1);
+			this.update();
+			return r;
+		}
+	}
+
+	update() {
+		this.element.replaceChildren(...this.buttons.map((b) => b["element"] ?? b));
+	}
+}
diff --git a/web/scripts/ui/components/popup.js b/web/scripts/ui/components/popup.js
new file mode 100644
index 000000000..ee59b35d9
--- /dev/null
+++ b/web/scripts/ui/components/popup.js
@@ -0,0 +1,128 @@
+// @ts-check
+
+import { prop } from "../../utils.js";
+import { $el } from "../../ui.js";
+import { applyClasses } from "../utils.js";
+
+export class ComfyPopup extends EventTarget {
+	element = $el("div.comfyui-popup");
+
+	/**
+	 * @param {{
+	 *     target: HTMLElement,
+	 *      container?: HTMLElement,
+	 *      classList?: import("../utils.js").ClassList,
+	 * 		ignoreTarget?: boolean,
+	 * 		closeOnEscape?: boolean,
+	 * 		position?: "absolute" | "relative",
+	 * 		horizontal?: "left" | "right"
+	 * }} param0
+	 * @param  {...HTMLElement} children
+	 */
+	constructor(
+		{
+			target,
+			container = document.body,
+			classList = "",
+			ignoreTarget = true,
+			closeOnEscape = true,
+			position = "absolute",
+			horizontal = "left",
+		},
+		...children
+	) {
+		super();
+		this.target = target;
+		this.ignoreTarget = ignoreTarget;
+		this.container = container;
+		this.position = position;
+		this.closeOnEscape = closeOnEscape;
+		this.horizontal = horizontal;
+
+		container.append(this.element);
+
+		this.children = prop(this, "children", children, () => {
+			this.element.replaceChildren(...this.children);
+			this.update();
+		});
+		this.classList = prop(this, "classList", classList, () => applyClasses(this.element, this.classList, "comfyui-popup", horizontal));
+		this.open = prop(this, "open", false, (v, o) => {
+			if (v === o) return;
+			if (v) {
+				this.#show();
+			} else {
+				this.#hide();
+			}
+		});
+	}
+
+	toggle() {
+		this.open = !this.open;
+	}
+
+	#hide() {
+		this.element.classList.remove("open");
+		window.removeEventListener("resize", this.update);
+		window.removeEventListener("click", this.#clickHandler, { capture: true });
+		window.removeEventListener("keydown", this.#escHandler, { capture: true });
+
+		this.dispatchEvent(new CustomEvent("close"));
+		this.dispatchEvent(new CustomEvent("change"));
+	}
+
+	#show() {
+		this.element.classList.add("open");
+		this.update();
+
+		window.addEventListener("resize", this.update);
+		window.addEventListener("click", this.#clickHandler, { capture: true });
+		if (this.closeOnEscape) {
+			window.addEventListener("keydown", this.#escHandler, { capture: true });
+		}
+
+		this.dispatchEvent(new CustomEvent("open"));
+		this.dispatchEvent(new CustomEvent("change"));
+	}
+
+	#escHandler = (e) => {
+		if (e.key === "Escape") {
+			this.open = false;
+			e.preventDefault();
+			e.stopImmediatePropagation();
+		}
+	};
+
+	#clickHandler = (e) => {
+		/** @type {any} */
+		const target = e.target;
+		if (!this.element.contains(target) && this.ignoreTarget && !this.target.contains(target)) {
+			this.open = false;
+		}
+	};
+
+	update = () => {
+		const rect = this.target.getBoundingClientRect();
+		this.element.style.setProperty("--bottom", "unset");
+		if (this.position === "absolute") {
+			if (this.horizontal === "left") {
+				this.element.style.setProperty("--left", rect.left + "px");
+			} else {
+				this.element.style.setProperty("--left", rect.right - this.element.clientWidth + "px");
+			}
+			this.element.style.setProperty("--top", rect.bottom + "px");
+			this.element.style.setProperty("--limit", rect.bottom + "px");
+		} else {
+			this.element.style.setProperty("--left", 0 + "px");
+			this.element.style.setProperty("--top", rect.height + "px");
+			this.element.style.setProperty("--limit", rect.height + "px");
+		}
+
+		const thisRect = this.element.getBoundingClientRect();
+		if (thisRect.height < 30) {
+			// Move up instead
+			this.element.style.setProperty("--top", "unset");
+			this.element.style.setProperty("--bottom", rect.height + 5 + "px");
+			this.element.style.setProperty("--limit", rect.height + 5 + "px");
+		}
+	};
+}
diff --git a/web/scripts/ui/components/splitButton.js b/web/scripts/ui/components/splitButton.js
new file mode 100644
index 000000000..2b4e6d9f8
--- /dev/null
+++ b/web/scripts/ui/components/splitButton.js
@@ -0,0 +1,43 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { ComfyButton } from "./button.js";
+import { prop } from "../../utils.js";
+import { ComfyPopup } from "./popup.js";
+
+export class ComfySplitButton {
+	/**
+	 *  @param {{
+	 * 		primary: ComfyButton,
+	 * 		mode?: "hover" | "click",
+	 * 		horizontal?: "left" | "right",
+	 * 		position?: "relative" | "absolute"
+	 *  }} param0
+	 *  @param {Array<ComfyButton> | Array<HTMLElement>} items
+	 */
+	constructor({ primary, mode, horizontal = "left", position = "relative" }, ...items) {
+		this.arrow = new ComfyButton({
+			icon: "chevron-down",
+		});
+		this.element = $el("div.comfyui-split-button" + (mode === "hover" ? ".hover" : ""), [
+			$el("div.comfyui-split-primary", primary.element),
+			$el("div.comfyui-split-arrow", this.arrow.element),
+		]);
+		this.popup = new ComfyPopup({
+			target: this.element,
+			container: position === "relative" ? this.element : document.body,
+			classList: "comfyui-split-button-popup" + (mode === "hover" ? " hover" : ""),
+			closeOnEscape: mode === "click",
+			position,
+			horizontal,
+		});
+
+		this.arrow.withPopup(this.popup, mode);
+
+		this.items = prop(this, "items", items, () => this.update());
+	}
+
+	update() {
+		this.popup.element.replaceChildren(...this.items.map((b) => b.element ?? b));
+	}
+}
diff --git a/web/scripts/ui/dialog.js b/web/scripts/ui/dialog.js
index aee93b3c8..803a97a2b 100644
--- a/web/scripts/ui/dialog.js
+++ b/web/scripts/ui/dialog.js
@@ -1,20 +1,26 @@
 import { $el } from "../ui.js";
 
-export class ComfyDialog {
-	constructor() {
-		this.element = $el("div.comfy-modal", { parent: document.body }, [
+export class ComfyDialog extends EventTarget {
+	#buttons;
+
+	constructor(type = "div", buttons = null) {
+		super();
+		this.#buttons = buttons;
+		this.element = $el(type + ".comfy-modal", { parent: document.body }, [
 			$el("div.comfy-modal-content", [$el("p", { $: (p) => (this.textElement = p) }), ...this.createButtons()]),
 		]);
 	}
 
 	createButtons() {
-		return [
-			$el("button", {
-				type: "button",
-				textContent: "Close",
-				onclick: () => this.close(),
-			}),
-		];
+		return (
+			this.#buttons ?? [
+				$el("button", {
+					type: "button",
+					textContent: "Close",
+					onclick: () => this.close(),
+				}),
+			]
+		);
 	}
 
 	close() {
@@ -25,7 +31,7 @@ export class ComfyDialog {
 		if (typeof html === "string") {
 			this.textElement.innerHTML = html;
 		} else {
-			this.textElement.replaceChildren(html);
+			this.textElement.replaceChildren(...(html instanceof Array ? html : [html]));
 		}
 		this.element.style.display = "flex";
 	}
diff --git a/web/scripts/ui/menu/index.js b/web/scripts/ui/menu/index.js
new file mode 100644
index 000000000..1e00b3d22
--- /dev/null
+++ b/web/scripts/ui/menu/index.js
@@ -0,0 +1,302 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { downloadBlob } from "../../utils.js";
+import { ComfyButton } from "../components/button.js";
+import { ComfyButtonGroup } from "../components/buttonGroup.js";
+import { ComfySplitButton } from "../components/splitButton.js";
+import { ComfyViewHistoryButton } from "./viewHistory.js";
+import { ComfyQueueButton } from "./queueButton.js";
+import { ComfyWorkflowsMenu } from "./workflows.js";
+import { ComfyViewQueueButton } from "./viewQueue.js";
+import { getInteruptButton } from "./interruptButton.js";
+
+const collapseOnMobile = (t) => {
+	(t.element ?? t).classList.add("comfyui-menu-mobile-collapse");
+	return t;
+};
+const showOnMobile = (t) => {
+	(t.element ?? t).classList.add("lt-lg-show");
+	return t;
+};
+
+export class ComfyAppMenu {
+	#sizeBreak = "lg";
+	#lastSizeBreaks = {
+		lg: null,
+		md: null,
+		sm: null,
+		xs: null,
+	};
+	#sizeBreaks = Object.keys(this.#lastSizeBreaks);
+	#cachedInnerSize = null;
+	#cacheTimeout = null;
+
+	/**
+	 * @param { import("../../app.js").ComfyApp } app
+	 */
+	constructor(app) {
+		this.app = app;
+
+		this.workflows = new ComfyWorkflowsMenu(app);
+		const getSaveButton = (t) =>
+			new ComfyButton({
+				icon: "content-save",
+				tooltip: "Save the current workflow",
+				action: () => app.workflowManager.activeWorkflow.save(),
+				content: t,
+			});
+
+		this.logo = $el("h1.comfyui-logo.nlg-hide", { title: "ComfyUI" }, "ComfyUI");
+		this.saveButton = new ComfySplitButton(
+			{
+				primary: getSaveButton(),
+				mode: "hover",
+				position: "absolute",
+			},
+			getSaveButton("Save"),
+			new ComfyButton({
+				icon: "content-save-edit",
+				content: "Save As",
+				tooltip: "Save the current graph as a new workflow",
+				action: () => app.workflowManager.activeWorkflow.save(true),
+			}),
+			new ComfyButton({
+				icon: "download",
+				content: "Export",
+				tooltip: "Export the current workflow as JSON",
+				action: () => this.exportWorkflow("workflow", "workflow"),
+			}),
+			new ComfyButton({
+				icon: "api",
+				content: "Export (API Format)",
+				tooltip: "Export the current workflow as JSON for use with the ComfyUI API",
+				action: () => this.exportWorkflow("workflow_api", "output"),
+				visibilitySetting: { id: "Comfy.DevMode", showValue: true },
+				app,
+			})
+		);
+		this.actionsGroup = new ComfyButtonGroup(
+			new ComfyButton({
+				icon: "refresh",
+				content: "Refresh",
+				tooltip: "Refresh widgets in nodes to find new models or files",
+				action: () => app.refreshComboInNodes(),
+			}),
+			new ComfyButton({
+				icon: "clipboard-edit-outline",
+				content: "Clipspace",
+				tooltip: "Open Clipspace window",
+				action: () => app["openClipspace"](),
+			}),
+			new ComfyButton({
+				icon: "fit-to-page-outline",
+				content: "Reset View",
+				tooltip: "Reset the canvas view",
+				action: () => app.resetView(),
+			}),
+			new ComfyButton({
+				icon: "cancel",
+				content: "Clear",
+				tooltip: "Clears current workflow",
+				action: () => {
+					if (!app.ui.settings.getSettingValue("Comfy.ConfirmClear", true) || confirm("Clear workflow?")) {
+						app.clean();
+						app.graph.clear();
+					}
+				},
+			})
+		);
+		this.settingsGroup = new ComfyButtonGroup(
+			new ComfyButton({
+				icon: "cog",
+				content: "Settings",
+				tooltip: "Open settings",
+				action: () => {
+					app.ui.settings.show();
+				},
+			})
+		);
+		this.viewGroup = new ComfyButtonGroup(
+			new ComfyViewHistoryButton(app).element,
+			new ComfyViewQueueButton(app).element,
+			getInteruptButton("nlg-hide").element
+		);
+		this.mobileMenuButton = new ComfyButton({
+			icon: "menu",
+			action: (_, btn) => {
+				btn.icon = this.element.classList.toggle("expanded") ? "menu-open" : "menu";
+				window.dispatchEvent(new Event("resize"));
+			},
+			classList: "comfyui-button comfyui-menu-button",
+		});
+
+		this.element = $el("nav.comfyui-menu.lg", { style: { display: "none" } }, [
+			this.logo,
+			this.workflows.element,
+			this.saveButton.element,
+			collapseOnMobile(this.actionsGroup).element,
+			$el("section.comfyui-menu-push"),
+			collapseOnMobile(this.settingsGroup).element,
+			collapseOnMobile(this.viewGroup).element,
+
+			getInteruptButton("lt-lg-show").element,
+			new ComfyQueueButton(app).element,
+			showOnMobile(this.mobileMenuButton).element,
+		]);
+
+		let resizeHandler;
+		this.menuPositionSetting = app.ui.settings.addSetting({
+			id: "Comfy.UseNewMenu",
+			defaultValue: "Disabled",
+			name: "[Beta] Use new menu and workflow management. Note: On small screens the menu will always be at the top.",
+			type: "combo",
+			options: ["Disabled", "Top", "Bottom"],
+			onChange: async (v) => {
+				if (v && v !== "Disabled") {
+					if (!resizeHandler) {
+						resizeHandler = () => {
+							this.calculateSizeBreak();
+						};
+						window.addEventListener("resize", resizeHandler);
+					}
+					this.updatePosition(v);
+				} else {
+					if (resizeHandler) {
+						window.removeEventListener("resize", resizeHandler);
+						resizeHandler = null;
+					}
+					document.body.style.removeProperty("display");
+					app.ui.menuContainer.style.removeProperty("display");
+					this.element.style.display = "none";
+					app.ui.restoreMenuPosition();
+				}
+				window.dispatchEvent(new Event("resize"));
+			},
+		});
+	}
+
+	updatePosition(v) {
+		document.body.style.display = "grid";
+		this.app.ui.menuContainer.style.display = "none";
+		this.element.style.removeProperty("display");
+		this.position = v;
+		if (v === "Bottom") {
+			this.app.bodyBottom.append(this.element);
+		} else {
+			this.app.bodyTop.prepend(this.element);
+		}
+		this.calculateSizeBreak();
+	}
+
+	updateSizeBreak(idx, prevIdx, direction) {
+		const newSize = this.#sizeBreaks[idx];
+		if (newSize === this.#sizeBreak) return;
+		this.#cachedInnerSize = null;
+		clearTimeout(this.#cacheTimeout);
+
+		this.#sizeBreak = this.#sizeBreaks[idx];
+		for (let i = 0; i < this.#sizeBreaks.length; i++) {
+			const sz = this.#sizeBreaks[i];
+			if (sz === this.#sizeBreak) {
+				this.element.classList.add(sz);
+			} else {
+				this.element.classList.remove(sz);
+			}
+			if (i < idx) {
+				this.element.classList.add("lt-" + sz);
+			} else {
+				this.element.classList.remove("lt-" + sz);
+			}
+		}
+
+		if (idx) {
+			// We're on a small screen, force the menu at the top
+			if (this.position !== "Top") {
+				this.updatePosition("Top");
+			}
+		} else if (this.position != this.menuPositionSetting.value) {
+			// Restore user position
+			this.updatePosition(this.menuPositionSetting.value);
+		}
+
+		// Allow multiple updates, but prevent bouncing
+		if (!direction) {
+			direction = prevIdx - idx;
+		} else if (direction != prevIdx - idx) {
+			return;
+		}
+		this.calculateSizeBreak(direction);
+	}
+
+	calculateSizeBreak(direction = 0) {
+		let idx = this.#sizeBreaks.indexOf(this.#sizeBreak);
+		const currIdx = idx;
+		const innerSize = this.calculateInnerSize(idx);
+		if (window.innerWidth >= this.#lastSizeBreaks[this.#sizeBreaks[idx - 1]]) {
+			if (idx > 0) {
+				idx--;
+			}
+		} else if (innerSize > this.element.clientWidth) {
+			this.#lastSizeBreaks[this.#sizeBreak] = Math.max(window.innerWidth, innerSize);
+			// We need to shrink
+			if (idx < this.#sizeBreaks.length - 1) {
+				idx++;
+			}
+		}
+
+		this.updateSizeBreak(idx, currIdx, direction);
+	}
+
+	calculateInnerSize(idx) {
+		// Cache the inner size to prevent too much calculation when resizing the window
+		clearTimeout(this.#cacheTimeout);
+		if (this.#cachedInnerSize) {
+			// Extend cache time
+			this.#cacheTimeout = setTimeout(() => (this.#cachedInnerSize = null), 100);
+		} else {
+			let innerSize = 0;
+			let count = 1;
+			for (const c of this.element.children) {
+				if (c.classList.contains("comfyui-menu-push")) continue; // ignore right push
+				if (idx && c.classList.contains("comfyui-menu-mobile-collapse")) continue; // ignore collapse items
+				innerSize += c.clientWidth;
+				count++;
+			}
+			innerSize += 8 * count;
+			this.#cachedInnerSize = innerSize;
+			this.#cacheTimeout = setTimeout(() => (this.#cachedInnerSize = null), 100);
+		}
+		return this.#cachedInnerSize;
+	}
+
+	/**
+	 * @param {string} defaultName
+	 */
+	getFilename(defaultName) {
+		if (this.app.ui.settings.getSettingValue("Comfy.PromptFilename", true)) {
+			defaultName = prompt("Save workflow as:", defaultName);
+			if (!defaultName) return;
+			if (!defaultName.toLowerCase().endsWith(".json")) {
+				defaultName += ".json";
+			}
+		}
+		return defaultName;
+	}
+
+	/**
+	 * @param {string} [filename]
+	 * @param { "workflow" | "output" } [promptProperty]
+	 */
+	async exportWorkflow(filename, promptProperty) {
+		if (this.app.workflowManager.activeWorkflow?.path) {
+			filename = this.app.workflowManager.activeWorkflow.name;
+		}
+		const p = await this.app.graphToPrompt();
+		const json = JSON.stringify(p[promptProperty], null, 2);
+		const blob = new Blob([json], { type: "application/json" });
+		const file = this.getFilename(filename);
+		if (!file) return;
+		downloadBlob(file, blob);
+	}
+}
diff --git a/web/scripts/ui/menu/interruptButton.js b/web/scripts/ui/menu/interruptButton.js
new file mode 100644
index 000000000..4db3328db
--- /dev/null
+++ b/web/scripts/ui/menu/interruptButton.js
@@ -0,0 +1,23 @@
+// @ts-check
+
+import { api } from "../../api.js";
+import { ComfyButton } from "../components/button.js";
+
+export function getInteruptButton(visibility) {
+	const btn = new ComfyButton({
+		icon: "close",
+		tooltip: "Cancel current generation",
+		enabled: false,
+		action: () => {
+			api.interrupt();
+		},
+		classList: ["comfyui-button", "comfyui-interrupt-button", visibility],
+	});
+
+	api.addEventListener("status", ({ detail }) => {
+		const sz = detail?.exec_info?.queue_remaining;
+		btn.enabled = sz > 0;
+	});
+
+	return btn;
+}
diff --git a/web/scripts/ui/menu/menu.css b/web/scripts/ui/menu/menu.css
new file mode 100644
index 000000000..20eeab2cf
--- /dev/null
+++ b/web/scripts/ui/menu/menu.css
@@ -0,0 +1,701 @@
+.relative {
+	position: relative;
+}
+.hidden {
+	display: none !important;
+}
+.mdi.rotate270::before {
+	transform: rotate(270deg);
+}
+
+/* Generic */
+.comfyui-button {
+	display: flex;
+	align-items: center;
+	gap: 0.5em;
+	cursor: pointer;
+	border: none;
+	border-radius: 4px;
+	padding: 4px 8px;
+	box-sizing: border-box;
+	margin: 0;
+}
+
+.comfyui-button:disabled {
+	opacity: 0.5;
+	cursor: not-allowed;
+}
+.primary .comfyui-button,
+.primary.comfyui-button {
+	background-color: var(--primary-bg) !important;
+	color: var(--primary-fg) !important;
+}
+
+.primary .comfyui-button:not(:disabled):hover,
+.primary.comfyui-button:not(:disabled):hover {
+	background-color: var(--primary-hover-bg) !important;
+	color: var(--primary-hover-fg) !important;
+}
+
+/* Popup */
+.comfyui-popup {
+	position: absolute;
+	left: var(--left);
+	right: var(--right);
+	top: var(--top);
+	bottom: var(--bottom);
+	z-index: 2000;
+	max-height: calc(100vh - var(--limit) - 10px);
+	box-shadow: 3px 3px 5px 0px rgba(0, 0, 0, 0.3);
+}
+
+.comfyui-popup:not(.open) {
+	display: none;
+}
+
+.comfyui-popup.right.open {
+	border-top-left-radius: 4px;
+	border-bottom-right-radius: 4px;
+	border-bottom-left-radius: 4px;
+	overflow: hidden;
+}
+/* Split button */
+.comfyui-split-button {
+	position: relative;
+	display: flex;
+}
+
+.comfyui-split-primary {
+	flex: auto;
+}
+
+.comfyui-split-primary .comfyui-button {
+	border-top-right-radius: 0;
+	border-bottom-right-radius: 0;
+	border-right: 1px solid var(--comfy-menu-bg);
+	width: 100%;
+}
+
+.comfyui-split-arrow .comfyui-button {
+	border-top-left-radius: 0;
+	border-bottom-left-radius: 0;
+	padding-left: 2px;
+	padding-right: 2px;
+}
+
+.comfyui-split-button-popup {
+	white-space: nowrap;
+	background-color: var(--content-bg);
+	color: var(--content-fg);
+	display: flex;
+	flex-direction: column;
+	overflow: auto;
+}
+
+.comfyui-split-button-popup.hover {
+	z-index: 2001;
+}
+.comfyui-split-button-popup > .comfyui-button {
+	border: none;
+	background-color: transparent;
+	color: var(--fg-color);
+	padding: 8px 12px 8px 8px;
+}
+
+.comfyui-split-button-popup > .comfyui-button:not(:disabled):hover {
+	background-color: var(--comfy-input-bg);
+}
+
+/* Button group */
+.comfyui-button-group {
+	display: flex;
+	border-radius: 4px;
+	overflow: hidden;
+}
+
+.comfyui-button-group > .comfyui-button,
+.comfyui-button-group > .comfyui-button-wrapper > .comfyui-button {
+	padding: 4px 10px;
+	border-radius: 0;
+}
+
+/* Menu */
+.comfyui-menu {
+	width: 100vw;
+	background: var(--comfy-menu-bg);
+	color: var(--fg-color);
+	font-family: Arial, Helvetica, sans-serif;
+	font-size: 0.8em;
+	display: flex;
+	padding: 4px 8px;
+	align-items: center;
+	gap: 8px;
+	box-sizing: border-box;
+	z-index: 1000;
+	order: 0;
+	grid-column: 1/-1;
+	overflow: auto;
+	max-height: 90vh;
+}
+
+.comfyui-menu>* {
+	flex-shrink: 0;
+}
+.comfyui-menu .mdi::before {
+	font-size: 18px;
+}
+
+.comfyui-menu .comfyui-button {
+	background: var(--comfy-input-bg);
+	color: var(--fg-color);
+	white-space: nowrap;
+}
+
+.comfyui-menu .comfyui-button:not(:disabled):hover {
+	background: var(--border-color);
+	color: var(--content-fg);
+}
+
+.comfyui-menu .comfyui-split-button-popup > .comfyui-button {
+	border-radius: 0;
+	background-color: transparent;
+}
+
+.comfyui-menu .comfyui-split-button-popup > .comfyui-button:not(:disabled):hover {
+	background-color: var(--comfy-input-bg);
+}
+
+.comfyui-menu .comfyui-split-button-popup.left {
+	border-top-right-radius: 4px;
+	border-bottom-left-radius: 4px;
+	border-bottom-right-radius: 4px;
+}
+
+.comfyui-menu .comfyui-button.popup-open {
+	background-color: var(--content-bg);
+	color: var(--content-fg);
+}
+
+.comfyui-menu-push {
+	margin-left: -0.8em;
+	flex: auto;
+}
+
+.comfyui-logo {
+	font-size: 1.2em;
+	margin: 0;
+	user-select: none;
+	cursor: default;
+}
+
+/* Workflows */
+.comfyui-workflows-button {
+	flex-direction: row-reverse;
+	max-width: 200px;
+	position: relative;
+	z-index: 0;
+}
+
+.comfyui-workflows-button.popup-open {
+	border-bottom-left-radius: 0;
+	border-bottom-right-radius: 0;
+}
+.comfyui-workflows-button.unsaved {
+	font-style: italic;
+}
+.comfyui-workflows-button-progress {
+	position: absolute;
+	top: 0;
+	left: 0;
+	background-color: green;
+	height: 100%;
+	border-radius: 4px;
+	z-index: -1;
+}
+
+.comfyui-workflows-button > span {
+	flex: auto;
+	text-align: left;
+	overflow: hidden;
+}
+.comfyui-workflows-button-inner {
+	display: flex;
+	align-items: center;
+	gap: 7px;
+	width: 150px;
+}
+.comfyui-workflows-label {
+	overflow: hidden;
+	text-overflow: ellipsis;
+	direction: rtl;
+	flex: auto;
+	position: relative;
+}
+
+.comfyui-workflows-button.unsaved .comfyui-workflows-label {
+	padding-left: 8px;
+}
+
+.comfyui-workflows-button.unsaved .comfyui-workflows-label:after {
+	content: "*";
+	position: absolute;
+	top: 0;
+	left: 0;
+}
+.comfyui-workflows-button-inner .mdi-graph::before {
+	transform: rotate(-90deg);
+}
+
+.comfyui-workflows-popup {
+	font-family: Arial, Helvetica, sans-serif;
+	font-size: 0.8em;
+	padding: 10px;
+	overflow: auto;
+	background-color: var(--content-bg);
+	color: var(--content-fg);
+	border-top-right-radius: 4px;
+	border-bottom-right-radius: 4px;
+	border-bottom-left-radius: 4px;
+	z-index: 400;
+}
+
+.comfyui-workflows-panel {
+	min-height: 150px;
+}
+
+.comfyui-workflows-panel .lds-ring {
+	transform: translate(-50%);
+	position: absolute;
+	left: 50%;
+	top: 75px;
+}
+
+.comfyui-workflows-panel h3 {
+	margin: 10px 0 10px 0;
+	font-size: 11px;
+	opacity: 0.8;
+}
+
+.comfyui-workflows-panel section header {
+	display: flex;
+	justify-content: space-between;
+	align-items: center;
+}
+.comfy-ui-workflows-search .mdi {
+	position: relative;
+	top: 2px;
+	pointer-events: none;
+}
+.comfy-ui-workflows-search input {
+	background-color: var(--comfy-input-bg);
+	color: var(--input-text);
+	border: none;
+	border-radius: 4px;
+	padding: 4px 10px;
+	margin-left: -24px;
+	text-indent: 18px;
+}
+.comfy-ui-workflows-search input:placeholder-shown {
+	width: 10px;
+}
+.comfy-ui-workflows-search input:placeholder-shown:focus {
+	width: auto;
+}
+.comfyui-workflows-actions {
+	display: flex;
+	gap: 10px;
+	margin-bottom: 10px;
+}
+
+.comfyui-workflows-actions .comfyui-button {
+	background: var(--comfy-input-bg);
+	color: var(--input-text);
+}
+
+.comfyui-workflows-actions .comfyui-button:not(:disabled):hover {
+	background: var(--primary-bg);
+	color: var(--primary-fg);
+}
+
+.comfyui-workflows-favorites,
+.comfyui-workflows-open {
+	border-bottom: 1px solid var(--comfy-input-bg);
+	padding-bottom: 5px;
+	margin-bottom: 5px;
+}
+
+.comfyui-workflows-open .active {
+	font-weight: bold;
+}
+
+.comfyui-workflows-favorites:empty {
+	display: none;
+}
+
+.comfyui-workflows-tree {
+	padding: 0;
+	margin: 0;
+}
+
+.comfyui-workflows-tree:empty::after {
+	content: "No saved workflows";
+	display: block;
+	text-align: center;
+}
+.comfyui-workflows-tree > ul {
+	padding: 0;
+}
+
+.comfyui-workflows-tree > ul ul {
+	margin: 0;
+	padding: 0 0 0 25px;
+}
+
+.comfyui-workflows-tree:not(.filtered) .closed > ul {
+	display: none;
+}
+
+.comfyui-workflows-tree li,
+.comfyui-workflows-tree-file {
+	--item-height: 32px;
+	list-style-type: none;
+	height: var(--item-height);
+	display: flex;
+	align-items: center;
+	gap: 5px;
+	cursor: pointer;
+	user-select: none;
+}
+
+.comfyui-workflows-tree-file.active::before,
+.comfyui-workflows-tree li:hover::before,
+.comfyui-workflows-tree-file:hover::before {
+	content: "";
+	position: absolute;
+	width: 100%;
+	left: 0;
+	height: var(--item-height);
+	background-color: var(--content-hover-bg);
+	color: var(--content-hover-fg);
+	z-index: -1;
+}
+
+.comfyui-workflows-tree-file.active::before {
+	background-color: var(--primary-bg);
+	color: var(--primary-fg);
+}
+
+.comfyui-workflows-tree-file.running:not(:hover)::before {
+	content: "";
+	position: absolute;
+	width: var(--progress, 0);
+	left: 0;
+	height: var(--item-height);
+	background-color: green;
+	z-index: -1;
+}
+
+.comfyui-workflows-tree-file.unsaved span {
+	font-style: italic;
+}
+
+.comfyui-workflows-tree-file span {
+	flex: auto;
+}
+
+.comfyui-workflows-tree-file span + .comfyui-workflows-file-action {
+	margin-left: 10px;
+}
+
+.comfyui-workflows-tree-file .comfyui-workflows-file-action {
+	background-color: transparent;
+	color: var(--fg-color);
+	padding: 2px 4px;
+}
+
+.lg ~ .comfyui-workflows-popup .comfyui-workflows-tree-file:not(:hover) .comfyui-workflows-file-action {
+	opacity: 0;
+}
+
+.comfyui-workflows-tree-file .comfyui-workflows-file-action:hover {
+	background-color: var(--primary-bg);
+	color: var(--primary-fg);
+}
+
+.comfyui-workflows-tree-file .comfyui-workflows-file-action-primary {
+	background-color: transparent;
+	color: var(--fg-color);
+	padding: 2px 4px;
+	margin: 0 -4px;
+}
+
+.comfyui-workflows-file-action-favorite .mdi-star {
+	color: orange;
+}
+
+/* View List */
+.comfyui-view-list-popup {
+	padding: 10px;
+	background-color: var(--content-bg);
+	color: var(--content-fg);
+	min-width: 170px;
+	min-height: 435px;
+	display: flex;
+	flex-direction: column;
+	align-items: center;
+	box-sizing: border-box;
+}
+.comfyui-view-list-popup h3 {
+	margin: 0 0 5px 0;
+}
+.comfyui-view-list-items {
+	width: 100%;
+	background: var(--comfy-menu-bg);
+	border-radius: 5px;
+	display: flex;
+	justify-content: center;
+	flex: auto;
+	align-items: center;
+	flex-direction: column;
+}
+.comfyui-view-list-items section {
+	max-height: 400px;
+	overflow: auto;
+	width: 100%;
+	display: grid;
+	grid-template-columns: auto auto auto;
+	align-items: center;
+	justify-content: center;
+	gap: 5px;
+	padding: 5px 0;
+}
+.comfyui-view-list-items section + section {
+	border-top: 1px solid var(--border-color);
+	margin-top: 10px;
+	padding-top: 5px;
+}
+.comfyui-view-list-items section h5 {
+	grid-column: 1 / 4;
+	text-align: center;
+	margin: 5px;
+}
+.comfyui-view-list-items span {
+	text-align: center;
+	padding: 0 2px;
+}
+.comfyui-view-list-popup header {
+	margin-bottom: 10px;
+	display: flex;
+	gap: 5px;
+}
+.comfyui-view-list-popup header .comfyui-button {
+	border: 1px solid transparent;
+}
+.comfyui-view-list-popup header .comfyui-button:not(:disabled):hover {
+	border: 1px solid var(--comfy-menu-bg);
+}
+/* Queue button */
+.comfyui-queue-button .comfyui-split-primary .comfyui-button {
+	padding-right: 12px;
+}
+.comfyui-queue-count {
+	margin-left: 5px;
+	border-radius: 10px;
+	background-color: rgb(8, 80, 153);
+	padding: 2px 4px;
+	font-size: 10px;
+	min-width: 1em;
+	display: inline-block;
+}
+/* Queue options*/
+.comfyui-queue-options {
+	padding: 10px;
+	font-family: Arial, Helvetica, sans-serif;
+	font-size: 12px;
+	display: flex;
+	gap: 10px;
+}
+
+.comfyui-queue-batch {
+	display: flex;
+	flex-direction: column;
+	border-right: 1px solid var(--comfy-menu-bg);
+	padding-right: 10px;
+	gap: 5px;
+}
+
+.comfyui-queue-batch input {
+	width: 145px;
+}
+
+.comfyui-queue-batch .comfyui-queue-batch-value {
+	width: 70px;
+}
+
+.comfyui-queue-mode {
+	display: flex;
+	flex-direction: column;
+}
+
+.comfyui-queue-mode span {
+	font-weight: bold;
+	margin-bottom: 2px;
+}
+
+.comfyui-queue-mode label {
+	display: flex;
+	flex-direction: row-reverse;
+	justify-content: start;
+	gap: 5px;
+	padding: 2px 0;
+}
+
+.comfyui-queue-mode label input {
+	padding: 0;
+	margin: 0;
+}
+
+/** Send to workflow widget selection dialog */
+.comfy-widget-selection-dialog {
+	border: none;
+}
+
+.comfy-widget-selection-dialog div {
+	color: var(--fg-color);
+	font-family: Arial, Helvetica, sans-serif;
+}
+
+.comfy-widget-selection-dialog h2 {
+	margin-top: 0;
+}
+
+.comfy-widget-selection-dialog section {
+	width: fit-content;
+	display: flex;
+	flex-direction: column;
+}
+
+.comfy-widget-selection-item {
+	display: flex;
+	gap: 10px;
+	align-items: center;
+}
+
+.comfy-widget-selection-item span {
+	margin-right: auto;
+}
+
+.comfy-widget-selection-item span::before {
+	content: '#' attr(data-id);
+	opacity: 0.5;
+	margin-right: 5px;
+}
+
+.comfy-modal .comfy-widget-selection-item button {
+	font-size: 1em;
+}
+
+/***** Responsive *****/
+.lg.comfyui-menu .lt-lg-show {
+	display: none !important;
+}
+.comfyui-menu:not(.lg) .nlg-hide {
+	display: none !important;
+}
+/** Large screen */
+.lg.comfyui-menu>.comfyui-menu-mobile-collapse .comfyui-button span,
+.lg.comfyui-menu>.comfyui-menu-mobile-collapse.comfyui-button span {
+	display: none;
+}
+.lg.comfyui-menu>.comfyui-menu-mobile-collapse .comfyui-popup .comfyui-button span {
+	display: unset;
+}
+
+/** Non large screen */
+.lt-lg.comfyui-menu {
+	flex-wrap: wrap;
+}
+
+.lt-lg.comfyui-menu > *:not(.comfyui-menu-mobile-collapse) {
+	order: 1;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse {
+	order: 9999;
+	width: 100%;
+}
+
+.comfyui-body-bottom .lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse {
+	order: -1;
+}
+
+.comfyui-body-bottom .lt-lg.comfyui-menu > .comfyui-menu-button {
+	top: unset;
+	bottom: 4px;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse.comfyui-button-group {
+	flex-wrap: wrap;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button,
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse.comfyui-button {
+	padding: 10px;
+}
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button,
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button-wrapper {
+	width: 100%;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-popup {
+	position: static;
+	background-color: var(--comfy-input-bg);
+	max-width: unset;
+	max-height: 50vh;
+	overflow: auto;
+}
+
+.lt-lg.comfyui-menu:not(.expanded) > .comfyui-menu-mobile-collapse {
+	display: none;
+}
+
+.lt-lg .comfyui-queue-button {
+	margin-right: 44px;
+}
+
+.lt-lg .comfyui-menu-button {
+	position: absolute;
+	top: 4px;
+	right: 8px;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-view-list-popup {
+	border-radius: 0;
+}
+
+.lt-lg.comfyui-menu .comfyui-workflows-popup {
+	width: 100vw;
+}
+
+/** Small */
+.lt-md .comfyui-workflows-button-inner {
+	width: unset !important;
+}
+.lt-md  .comfyui-workflows-label {
+	display: none;
+}
+
+/** Extra small */
+.lt-sm .comfyui-queue-button {
+	margin-right: 0;
+	width: 100%;
+}
+.lt-sm .comfyui-queue-button .comfyui-button {
+	justify-content: center;
+}
+.lt-sm .comfyui-interrupt-button {
+	margin-right: 45px;
+}
+.comfyui-body-bottom .lt-sm.comfyui-menu > .comfyui-menu-button{
+	bottom: 41px;
+}
\ No newline at end of file
diff --git a/web/scripts/ui/menu/queueButton.js b/web/scripts/ui/menu/queueButton.js
new file mode 100644
index 000000000..3c29ab090
--- /dev/null
+++ b/web/scripts/ui/menu/queueButton.js
@@ -0,0 +1,93 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { $el } from "../../ui.js";
+import { api } from "../../api.js";
+import { ComfySplitButton } from "../components/splitButton.js";
+import { ComfyQueueOptions } from "./queueOptions.js";
+import { prop } from "../../utils.js";
+
+export class ComfyQueueButton {
+	element = $el("div.comfyui-queue-button");
+	#internalQueueSize = 0;
+
+	queuePrompt = async (e) => {
+		this.#internalQueueSize += this.queueOptions.batchCount;
+		// Hold shift to queue front
+		await this.app.queuePrompt(-e.shiftKey, this.queueOptions.batchCount);
+	};
+
+	constructor(app) {
+		this.app = app;
+		this.queueSizeElement = $el("span.comfyui-queue-count", {
+			textContent: "?",
+		});
+
+		const queue = new ComfyButton({
+			content: $el("div", [
+				$el("span", {
+					textContent: "Queue",
+				}),
+				this.queueSizeElement,
+			]),
+			icon: "play",
+			classList: "comfyui-button",
+			action: this.queuePrompt,
+		});
+
+		this.queueOptions = new ComfyQueueOptions(app);
+
+		const btn = new ComfySplitButton(
+			{
+				primary: queue,
+				mode: "click",
+				position: "absolute",
+				horizontal: "right",
+			},
+			this.queueOptions.element
+		);
+		btn.element.classList.add("primary");
+		this.element.append(btn.element);
+
+		this.autoQueueMode = prop(this, "autoQueueMode", "", () => {
+			switch (this.autoQueueMode) {
+				case "instant":
+					queue.icon = "infinity";
+					break;
+				case "change":
+					queue.icon = "auto-mode";
+					break;
+				default:
+					queue.icon = "play";
+					break;
+			}
+		});
+
+		this.queueOptions.addEventListener("autoQueueMode", (e) => (this.autoQueueMode = e["detail"]));
+
+		api.addEventListener("graphChanged", () => {
+			if (this.autoQueueMode === "change") {
+				if (this.#internalQueueSize) {
+					this.graphHasChanged = true;
+				} else {
+					this.graphHasChanged = false;
+					this.queuePrompt();
+				}
+			}
+		});
+
+		api.addEventListener("status", ({ detail }) => {
+			this.#internalQueueSize = detail?.exec_info?.queue_remaining;
+			if (this.#internalQueueSize != null) {
+				this.queueSizeElement.textContent = this.#internalQueueSize > 99 ? "99+" : this.#internalQueueSize + "";
+				this.queueSizeElement.title = `${this.#internalQueueSize} prompts in queue`;
+				if (!this.#internalQueueSize && !app.lastExecutionError) {
+					if (this.autoQueueMode === "instant" || (this.autoQueueMode === "change" && this.graphHasChanged)) {
+						this.graphHasChanged = false;
+						this.queuePrompt();
+					}
+				}
+			}
+		});
+	}
+}
diff --git a/web/scripts/ui/menu/queueOptions.js b/web/scripts/ui/menu/queueOptions.js
new file mode 100644
index 000000000..f3d34e74e
--- /dev/null
+++ b/web/scripts/ui/menu/queueOptions.js
@@ -0,0 +1,77 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { prop } from "../../utils.js";
+
+export class ComfyQueueOptions extends EventTarget {
+	element = $el("div.comfyui-queue-options");
+
+	constructor(app) {
+		super();
+		this.app = app;
+
+		this.batchCountInput = $el("input", {
+			className: "comfyui-queue-batch-value",
+			type: "number",
+			min: "1",
+			value: "1",
+			oninput: () => (this.batchCount = +this.batchCountInput.value),
+		});
+
+		this.batchCountRange = $el("input", {
+			type: "range",
+			min: "1",
+			max: "100",
+			value: "1",
+			oninput: () => (this.batchCount = +this.batchCountRange.value),
+		});
+
+		this.element.append(
+			$el("div.comfyui-queue-batch", [
+				$el(
+					"label",
+					{
+						textContent: "Batch count: ",
+					},
+					this.batchCountInput
+				),
+				this.batchCountRange,
+			])
+		);
+
+		const createOption = (text, value, checked = false) =>
+			$el(
+				"label",
+				{ textContent: text },
+				$el("input", {
+					type: "radio",
+					name: "AutoQueueMode",
+					checked,
+					value,
+					oninput: (e) => (this.autoQueueMode = e.target["value"]),
+				})
+			);
+
+		this.autoQueueEl = $el("div.comfyui-queue-mode", [
+			$el("span", "Auto Queue:"),
+			createOption("Disabled", "", true),
+			createOption("Instant", "instant"),
+			createOption("On Change", "change"),
+		]);
+
+		this.element.append(this.autoQueueEl);
+
+		this.batchCount = prop(this, "batchCount", 1, () => {
+			this.batchCountInput.value = this.batchCount + "";
+			this.batchCountRange.value = this.batchCount + "";
+		});
+
+		this.autoQueueMode = prop(this, "autoQueueMode", "Disabled", () => {
+			this.dispatchEvent(
+				new CustomEvent("autoQueueMode", {
+					detail: this.autoQueueMode,
+				})
+			);
+		});
+	}
+}
diff --git a/web/scripts/ui/menu/viewHistory.js b/web/scripts/ui/menu/viewHistory.js
new file mode 100644
index 000000000..de6b343dc
--- /dev/null
+++ b/web/scripts/ui/menu/viewHistory.js
@@ -0,0 +1,27 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { ComfyViewList, ComfyViewListButton } from "./viewList.js";
+
+export class ComfyViewHistoryButton extends ComfyViewListButton {
+	constructor(app) {
+		super(app, {
+			button: new ComfyButton({
+				content: "View History",
+				icon: "history",
+				tooltip: "View history",
+				classList: "comfyui-button comfyui-history-button",
+			}),
+			list: ComfyViewHistoryList,
+			mode: "History",
+		});
+	}
+}
+
+export class ComfyViewHistoryList extends ComfyViewList {
+	async loadItems() {
+		const items = await super.loadItems();
+		items["History"].reverse();
+		return items;
+	}
+}
diff --git a/web/scripts/ui/menu/viewList.js b/web/scripts/ui/menu/viewList.js
new file mode 100644
index 000000000..693ca335c
--- /dev/null
+++ b/web/scripts/ui/menu/viewList.js
@@ -0,0 +1,203 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { $el } from "../../ui.js";
+import { api } from "../../api.js";
+import { ComfyPopup } from "../components/popup.js";
+
+export class ComfyViewListButton {
+	get open() {
+		return this.popup.open;
+	}
+
+	set open(open) {
+		this.popup.open = open;
+	}
+
+	constructor(app, { button, list, mode }) {
+		this.app = app;
+		this.button = button;
+		this.element = $el("div.comfyui-button-wrapper", this.button.element);
+		this.popup = new ComfyPopup({
+			target: this.element,
+			container: this.element,
+			horizontal: "right",
+		});
+		this.list = new (list ?? ComfyViewList)(app, mode, this.popup);
+		this.popup.children = [this.list.element];
+		this.popup.addEventListener("open", () => {
+			this.list.update();
+		});
+		this.popup.addEventListener("close", () => {
+			this.list.close();
+		});
+		this.button.withPopup(this.popup);
+
+		api.addEventListener("status", () => {
+			if (this.popup.open) {
+				this.popup.update();
+			}
+		});
+	}
+}
+
+export class ComfyViewList {
+	popup;
+
+	constructor(app, mode, popup) {
+		this.app = app;
+		this.mode = mode;
+		this.popup = popup;
+		this.type = mode.toLowerCase();
+
+		this.items = $el(`div.comfyui-${this.type}-items.comfyui-view-list-items`);
+		this.clear = new ComfyButton({
+			icon: "cancel",
+			content: "Clear",
+			action: async () => {
+				this.showSpinner(false);
+				await api.clearItems(this.type);
+				await this.update();
+			},
+		});
+
+		this.refresh = new ComfyButton({
+			icon: "refresh",
+			content: "Refresh",
+			action: async () => {
+				await this.update(false);
+			},
+		});
+
+		this.element = $el(`div.comfyui-${this.type}-popup.comfyui-view-list-popup`, [
+			$el("h3", mode),
+			$el("header", [this.clear.element, this.refresh.element]),
+			this.items,
+		]);
+
+		api.addEventListener("status", () => {
+			if (this.popup.open) {
+				this.update();
+			}
+		});
+	}
+
+	async close() {
+		this.items.replaceChildren();
+	}
+
+	async update(resize = true) {
+		this.showSpinner(resize);
+		const res = await this.loadItems();
+		let any = false;
+
+		const names = Object.keys(res);
+		const sections = names
+			.map((section) => {
+				const items = res[section];
+				if (items?.length) {
+					any = true;
+				} else {
+					return;
+				}
+
+				const rows = [];
+				if (names.length > 1) {
+					rows.push($el("h5", section));
+				}
+				rows.push(...items.flatMap((item) => this.createRow(item, section)));
+				return $el("section", rows);
+			})
+			.filter(Boolean);
+
+		if (any) {
+			this.items.replaceChildren(...sections);
+		} else {
+			this.items.replaceChildren($el("h5", "None"));
+		}
+
+		this.popup.update();
+		this.clear.enabled = this.refresh.enabled = true;
+		this.element.style.removeProperty("height");
+	}
+
+	showSpinner(resize = true) {
+		// if (!this.spinner) {
+		// 	this.spinner = createSpinner();
+		// }
+		// if (!resize) {
+		// 	this.element.style.height = this.element.clientHeight + "px";
+		// }
+		// this.clear.enabled = this.refresh.enabled = false;
+		// this.items.replaceChildren(
+		// 	$el(
+		// 		"div",
+		// 		{
+		// 			style: {
+		// 				fontSize: "18px",
+		// 			},
+		// 		},
+		// 		this.spinner
+		// 	)
+		// );
+		// this.popup.update();
+	}
+
+	async loadItems() {
+		return await api.getItems(this.type);
+	}
+
+	getRow(item, section) {
+		return {
+			text: item.prompt[0] + "",
+			actions: [
+				{
+					text: "Load",
+					action: async () => {
+						try {
+							await this.app.loadGraphData(item.prompt[3].extra_pnginfo.workflow);
+							if (item.outputs) {
+								this.app.nodeOutputs = item.outputs;
+							}
+						} catch (error) {
+							alert("Error loading workflow: " + error.message);
+							console.error(error);
+						}
+					},
+				},
+				{
+					text: "Delete",
+					action: async () => {
+						try {
+							await api.deleteItem(this.type, item.prompt[1]);
+							this.update();
+						} catch (error) {}
+					},
+				},
+			],
+		};
+	}
+
+	createRow = (item, section) => {
+		const row = this.getRow(item, section);
+		return [
+			$el("span", row.text),
+			...row.actions.map(
+				(a) =>
+					new ComfyButton({
+						content: a.text,
+						action: async (e, btn) => {
+							btn.enabled = false;
+							try {
+								await a.action();
+							} catch (error) {
+								throw error;
+							} finally {
+								btn.enabled = true;
+							}
+						},
+					}).element
+			),
+		];
+	};
+}
diff --git a/web/scripts/ui/menu/viewQueue.js b/web/scripts/ui/menu/viewQueue.js
new file mode 100644
index 000000000..97d012986
--- /dev/null
+++ b/web/scripts/ui/menu/viewQueue.js
@@ -0,0 +1,55 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { ComfyViewList, ComfyViewListButton } from "./viewList.js";
+import { api } from "../../api.js";
+
+export class ComfyViewQueueButton extends ComfyViewListButton {
+	constructor(app) {
+		super(app, {
+			button: new ComfyButton({
+				content: "View Queue",
+				icon: "format-list-numbered",
+				tooltip: "View queue",
+				classList: "comfyui-button comfyui-queue-button",
+			}),
+			list: ComfyViewQueueList,
+			mode: "Queue",
+		});
+	}
+}
+
+export class ComfyViewQueueList extends ComfyViewList {
+	getRow = (item, section) => {
+		if (section !== "Running") {
+			return super.getRow(item, section);
+		}
+		return {
+			text: item.prompt[0] + "",
+			actions: [
+				{
+					text: "Load",
+					action: async () => {
+						try {
+							await this.app.loadGraphData(item.prompt[3].extra_pnginfo.workflow);
+							if (item.outputs) {
+								this.app.nodeOutputs = item.outputs;
+							}
+						} catch (error) {
+							alert("Error loading workflow: " + error.message);
+							console.error(error);
+						}
+					},
+				},
+				{
+					text: "Cancel",
+					action: async () => {
+						try {
+							await api.interrupt();
+						} catch (error) {}
+					},
+				},
+			],
+		};
+	}
+}
diff --git a/web/scripts/ui/menu/workflows.js b/web/scripts/ui/menu/workflows.js
new file mode 100644
index 000000000..afdff538a
--- /dev/null
+++ b/web/scripts/ui/menu/workflows.js
@@ -0,0 +1,764 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { prop, getStorageValue, setStorageValue } from "../../utils.js";
+import { $el } from "../../ui.js";
+import { api } from "../../api.js";
+import { ComfyPopup } from "../components/popup.js";
+import { createSpinner } from "../spinner.js";
+import { ComfyWorkflow, trimJsonExt } from "../../workflows.js";
+import { ComfyAsyncDialog } from "../components/asyncDialog.js";
+
+export class ComfyWorkflowsMenu {
+	#first = true;
+	element = $el("div.comfyui-workflows");
+
+	get open() {
+		return this.popup.open;
+	}
+
+	set open(open) {
+		this.popup.open = open;
+	}
+
+	/**
+	 * @param {import("../../app.js").ComfyApp} app
+	 */
+	constructor(app) {
+		this.app = app;
+		this.#bindEvents();
+
+		const classList = {
+			"comfyui-workflows-button": true,
+			"comfyui-button": true,
+			unsaved: getStorageValue("Comfy.PreviousWorkflowUnsaved") === "true",
+			running: false,
+		};
+		this.buttonProgress = $el("div.comfyui-workflows-button-progress");
+		this.workflowLabel = $el("span.comfyui-workflows-label", "");
+		this.button = new ComfyButton({
+			content: $el("div.comfyui-workflows-button-inner", [$el("i.mdi.mdi-graph"), this.workflowLabel, this.buttonProgress]),
+			icon: "chevron-down",
+			classList,
+		});
+
+		this.element.append(this.button.element);
+
+		this.popup = new ComfyPopup({ target: this.element, classList: "comfyui-workflows-popup" });
+		this.content = new ComfyWorkflowsContent(app, this.popup);
+		this.popup.children = [this.content.element];
+		this.popup.addEventListener("change", () => {
+			this.button.icon = "chevron-" + (this.popup.open ? "up" : "down");
+		});
+		this.button.withPopup(this.popup);
+
+		this.unsaved = prop(this, "unsaved", classList.unsaved, (v) => {
+			classList.unsaved = v;
+			this.button.classList = classList;
+			setStorageValue("Comfy.PreviousWorkflowUnsaved", v);
+		});
+	}
+
+	#updateProgress = () => {
+		const prompt = this.app.workflowManager.activePrompt;
+		let percent = 0;
+		if (this.app.workflowManager.activeWorkflow === prompt?.workflow) {
+			const total = Object.values(prompt.nodes);
+			const done = total.filter(Boolean);
+			percent = (done.length / total.length) * 100;
+		}
+		this.buttonProgress.style.width = percent + "%";
+	};
+
+	#updateActive = () => {
+		const active = this.app.workflowManager.activeWorkflow;
+		this.button.tooltip = active.path;
+		this.workflowLabel.textContent = active.name;
+		this.unsaved = active.unsaved;
+
+		if (this.#first) {
+			this.#first = false;
+			this.content.load();
+		}
+
+		this.#updateProgress();
+	};
+
+	#bindEvents() {
+		this.app.workflowManager.addEventListener("changeWorkflow", this.#updateActive);
+		this.app.workflowManager.addEventListener("rename", this.#updateActive);
+		this.app.workflowManager.addEventListener("delete", this.#updateActive);
+
+		this.app.workflowManager.addEventListener("save", () => {
+			this.unsaved = this.app.workflowManager.activeWorkflow.unsaved;
+		});
+
+		this.app.workflowManager.addEventListener("execute", (e) => {
+			this.#updateProgress();
+		});
+
+		api.addEventListener("graphChanged", () => {
+			this.unsaved = true;
+		});
+	}
+
+	#getMenuOptions(callback) {
+		const menu = [];
+		const directories = new Map();
+		for (const workflow of this.app.workflowManager.workflows || []) {
+			const path = workflow.pathParts;
+			if (!path) continue;
+			let parent = menu;
+			let currentPath = "";
+			for (let i = 0; i < path.length - 1; i++) {
+				currentPath += "/" + path[i];
+				let newParent = directories.get(currentPath);
+				if (!newParent) {
+					newParent = {
+						title: path[i],
+						has_submenu: true,
+						submenu: {
+							options: [],
+						},
+					};
+					parent.push(newParent);
+					newParent = newParent.submenu.options;
+					directories.set(currentPath, newParent);
+				}
+				parent = newParent;
+			}
+			parent.push({
+				title: trimJsonExt(path[path.length - 1]),
+				callback: () => callback(workflow),
+			});
+		}
+		return menu;
+	}
+
+	#getFavoriteMenuOptions(callback) {
+		const menu = [];
+		for (const workflow of this.app.workflowManager.workflows || []) {
+			if (workflow.isFavorite) {
+				menu.push({
+					title: "⭐ " + workflow.name,
+					callback: () => callback(workflow),
+				});
+			}
+		}
+		return menu;
+	}
+
+	/**
+	 * @param {import("../../app.js").ComfyApp} app
+	 */
+	registerExtension(app) {
+		const self = this;
+		app.registerExtension({
+			name: "Comfy.Workflows",
+			async beforeRegisterNodeDef(nodeType) {
+				function getImageWidget(node) {
+					const inputs = { ...node.constructor?.nodeData?.input?.required, ...node.constructor?.nodeData?.input?.optional };
+					for (const input in inputs) {
+						if (inputs[input][0] === "IMAGEUPLOAD") {
+							const imageWidget = node.widgets.find((w) => w.name === (inputs[input]?.[1]?.widget ?? "image"));
+							if (imageWidget) return imageWidget;
+						}
+					}
+				}
+
+				function setWidgetImage(node, widget, img) {
+					const url = new URL(img.src);
+					const filename = url.searchParams.get("filename");
+					const subfolder = url.searchParams.get("subfolder");
+					const type = url.searchParams.get("type");
+					const imageId = `${subfolder ? subfolder + "/" : ""}${filename} [${type}]`;
+					widget.value = imageId;
+					node.imgs = [img];
+					app.graph.setDirtyCanvas(true, true);
+				}
+
+				/**
+				 * @param {HTMLImageElement} img
+				 * @param {ComfyWorkflow} workflow
+				 */
+				async function sendToWorkflow(img, workflow) {
+					await workflow.load();
+					let options = [];
+					const nodes = app.graph.computeExecutionOrder(false);
+					for (const node of nodes) {
+						const widget = getImageWidget(node);
+						if (widget == null) continue;
+
+						if (node.title?.toLowerCase().includes("input")) {
+							options = [{ widget, node }];
+							break;
+						} else {
+							options.push({ widget, node });
+						}
+					}
+
+					if (!options.length) {
+						alert("No image nodes have been found in this workflow!");
+						return;
+					} else if (options.length > 1) {
+						const dialog = new WidgetSelectionDialog(options);
+						const res = await dialog.show(app);
+						if (!res) return;
+						options = [res];
+					}
+
+					setWidgetImage(options[0].node, options[0].widget, img);
+				}
+
+				const getExtraMenuOptions = nodeType.prototype["getExtraMenuOptions"];
+				nodeType.prototype["getExtraMenuOptions"] = function (_, options) {
+					const r = getExtraMenuOptions?.apply?.(this, arguments);
+
+					if (app.ui.settings.getSettingValue("Comfy.UseNewMenu", false) === true) {
+						const t = /** @type { {imageIndex?: number, overIndex?: number, imgs: string[]} } */ /** @type {any} */ (this);
+						let img;
+						if (t.imageIndex != null) {
+							// An image is selected so select that
+							img = t.imgs?.[t.imageIndex];
+						} else if (t.overIndex != null) {
+							// No image is selected but one is hovered
+							img = t.img?.s[t.overIndex];
+						}
+
+						if (img) {
+							let pos = options.findIndex((o) => o.content === "Save Image");
+							if (pos === -1) {
+								pos = 0;
+							} else {
+								pos++;
+							}
+
+							options.splice(pos, 0, {
+								content: "Send to workflow",
+								has_submenu: true,
+								submenu: {
+									options: [
+										{
+											callback: () => sendToWorkflow(img, app.workflowManager.activeWorkflow),
+											title: "[Current workflow]",
+										},
+										...self.#getFavoriteMenuOptions(sendToWorkflow.bind(null, img)),
+										null,
+										...self.#getMenuOptions(sendToWorkflow.bind(null, img)),
+									],
+								},
+							});
+						}
+					}
+
+					return r;
+				};
+			},
+		});
+	}
+}
+
+export class ComfyWorkflowsContent {
+	element = $el("div.comfyui-workflows-panel");
+	treeState = {};
+	treeFiles = {};
+	/** @type { Map<ComfyWorkflow, WorkflowElement> } */
+	openFiles = new Map();
+	/** @type {WorkflowElement} */
+	activeElement = null;
+
+	/**
+	 * @param {import("../../app.js").ComfyApp} app
+	 * @param {ComfyPopup} popup
+	 */
+	constructor(app, popup) {
+		this.app = app;
+		this.popup = popup;
+		this.actions = $el("div.comfyui-workflows-actions", [
+			new ComfyButton({
+				content: "Default",
+				icon: "file-code",
+				iconSize: 18,
+				classList: "comfyui-button primary",
+				tooltip: "Load default workflow",
+				action: () => {
+					popup.open = false;
+					app.loadGraphData();
+					app.resetView();
+				},
+			}).element,
+			new ComfyButton({
+				content: "Browse",
+				icon: "folder",
+				iconSize: 18,
+				tooltip: "Browse for an image or exported workflow",
+				action: () => {
+					popup.open = false;
+					app.ui.loadFile();
+				},
+			}).element,
+			new ComfyButton({
+				content: "Blank",
+				icon: "plus-thick",
+				iconSize: 18,
+				tooltip: "Create a new blank workflow",
+				action: () => {
+					app.workflowManager.setWorkflow(null);
+					app.clean();
+					app.graph.clear();
+					app.workflowManager.activeWorkflow.track();
+					popup.open = false;
+				},
+			}).element,
+		]);
+
+		this.spinner = createSpinner();
+		this.element.replaceChildren(this.actions, this.spinner);
+
+		this.popup.addEventListener("open", () => this.load());
+		this.popup.addEventListener("close", () => this.element.replaceChildren(this.actions, this.spinner));
+
+		this.app.workflowManager.addEventListener("favorite", (e) => {
+			const workflow = e["detail"];
+			const button = this.treeFiles[workflow.path]?.primary;
+			if (!button) return; // Can happen when a workflow is renamed
+			button.icon = this.#getFavoriteIcon(workflow);
+			button.overIcon = this.#getFavoriteOverIcon(workflow);
+			this.updateFavorites();
+		});
+
+		for (const e of ["save", "open", "close", "changeWorkflow"]) {
+			// TODO: dont be lazy and just update the specific element
+			app.workflowManager.addEventListener(e, () => this.updateOpen());
+		}
+		this.app.workflowManager.addEventListener("rename", () => this.load());
+		this.app.workflowManager.addEventListener("execute", (e) => this.#updateActive());
+	}
+
+	async load() {
+		await this.app.workflowManager.loadWorkflows();
+		this.updateTree();
+		this.updateFavorites();
+		this.updateOpen();
+		this.element.replaceChildren(this.actions, this.openElement, this.favoritesElement, this.treeElement);
+	}
+
+	updateOpen() {
+		const current = this.openElement;
+		this.openFiles.clear();
+
+		this.openElement = $el("div.comfyui-workflows-open", [
+			$el("h3", "Open"),
+			...this.app.workflowManager.openWorkflows.map((w) => {
+				const wrapper = new WorkflowElement(this, w, {
+					primary: { element: $el("i.mdi.mdi-18px.mdi-progress-pencil") },
+					buttons: [
+						this.#getRenameButton(w),
+						new ComfyButton({
+							icon: "close",
+							iconSize: 18,
+							classList: "comfyui-button comfyui-workflows-file-action",
+							tooltip: "Close workflow",
+							action: (e) => {
+								e.stopImmediatePropagation();
+								this.app.workflowManager.closeWorkflow(w);
+							},
+						}),
+					],
+				});
+				if (w.unsaved) {
+					wrapper.element.classList.add("unsaved");
+				}
+				if(w === this.app.workflowManager.activeWorkflow) {
+					wrapper.element.classList.add("active");
+				}
+
+				this.openFiles.set(w, wrapper);
+				return wrapper.element;
+			}),
+		]);
+
+		this.#updateActive();
+		current?.replaceWith(this.openElement);
+	}
+
+	updateFavorites() {
+		const current = this.favoritesElement;
+		const favorites = [...this.app.workflowManager.workflows.filter((w) => w.isFavorite)];
+
+		this.favoritesElement = $el("div.comfyui-workflows-favorites", [
+			$el("h3", "Favorites"),
+			...favorites
+				.map((w) => {
+					return this.#getWorkflowElement(w).element;
+				})
+				.filter(Boolean),
+		]);
+
+		current?.replaceWith(this.favoritesElement);
+	}
+
+	filterTree() {
+		if (!this.filterText) {
+			this.treeRoot.classList.remove("filtered");
+			// Unfilter whole tree
+			for (const item of Object.values(this.treeFiles)) {
+				item.element.parentElement.style.removeProperty("display");
+				this.showTreeParents(item.element.parentElement);
+			}
+			return;
+		}
+		this.treeRoot.classList.add("filtered");
+		const searchTerms = this.filterText.toLocaleLowerCase().split(" ");
+		for (const item of Object.values(this.treeFiles)) {
+			const parts = item.workflow.pathParts;
+			let termIndex = 0;
+			let valid = false;
+			for (const part of parts) {
+				let currentIndex = 0;
+				do {
+					currentIndex = part.indexOf(searchTerms[termIndex], currentIndex);
+					if (currentIndex > -1) currentIndex += searchTerms[termIndex].length;
+				} while (currentIndex !== -1 && ++termIndex < searchTerms.length);
+
+				if (termIndex >= searchTerms.length) {
+					valid = true;
+					break;
+				}
+			}
+			if (valid) {
+				item.element.parentElement.style.removeProperty("display");
+				this.showTreeParents(item.element.parentElement);
+			} else {
+				item.element.parentElement.style.display = "none";
+				this.hideTreeParents(item.element.parentElement);
+			}
+		}
+	}
+
+	hideTreeParents(element) {
+		// Hide all parents if no children are visible
+		if (element.parentElement?.classList.contains("comfyui-workflows-tree") === false) {
+			for (let i = 1; i < element.parentElement.children.length; i++) {
+				const c = element.parentElement.children[i];
+				if (c.style.display !== "none") {
+					return;
+				}
+			}
+			element.parentElement.style.display = "none";
+			this.hideTreeParents(element.parentElement);
+		}
+	}
+
+	showTreeParents(element) {
+		if (element.parentElement?.classList.contains("comfyui-workflows-tree") === false) {
+			element.parentElement.style.removeProperty("display");
+			this.showTreeParents(element.parentElement);
+		}
+	}
+
+	updateTree() {
+		const current = this.treeElement;
+		const nodes = {};
+		let typingTimeout;
+
+		this.treeFiles = {};
+		this.treeRoot = $el("ul.comfyui-workflows-tree");
+		this.treeElement = $el("section", [
+			$el("header", [
+				$el("h3", "Browse"),
+				$el("div.comfy-ui-workflows-search", [
+					$el("i.mdi.mdi-18px.mdi-magnify"),
+					$el("input", {
+						placeholder: "Search",
+						value: this.filterText ?? "",
+						oninput: (e) => {
+							this.filterText = e.target["value"]?.trim();
+							clearTimeout(typingTimeout);
+							typingTimeout = setTimeout(() => this.filterTree(), 250);
+						},
+					}),
+				]),
+			]),
+			this.treeRoot,
+		]);
+
+		for (const workflow of this.app.workflowManager.workflows) {
+			if (!workflow.pathParts) continue;
+
+			let currentPath = "";
+			let currentRoot = this.treeRoot;
+
+			for (let i = 0; i < workflow.pathParts.length; i++) {
+				currentPath += (currentPath ? "\\" : "") + workflow.pathParts[i];
+				const parentNode = nodes[currentPath] ?? this.#createNode(currentPath, workflow, i, currentRoot);
+
+				nodes[currentPath] = parentNode;
+				currentRoot = parentNode;
+			}
+		}
+
+		current?.replaceWith(this.treeElement);
+		this.filterTree();
+	}
+
+	#expandNode(el, workflow, thisPath, i) {
+		const expanded = !el.classList.toggle("closed");
+		if (expanded) {
+			let c = "";
+			for (let j = 0; j <= i; j++) {
+				c += (c ? "\\" : "") + workflow.pathParts[j];
+				this.treeState[c] = true;
+			}
+		} else {
+			let c = thisPath;
+			for (let j = i + 1; j < workflow.pathParts.length; j++) {
+				c += (c ? "\\" : "") + workflow.pathParts[j];
+				delete this.treeState[c];
+			}
+			delete this.treeState[thisPath];
+		}
+	}
+
+	#updateActive() {
+		this.#removeActive();
+
+		const active = this.app.workflowManager.activePrompt;
+		if (!active?.workflow) return;
+
+		const open = this.openFiles.get(active.workflow);
+		if (!open) return;
+
+		this.activeElement = open;
+
+		const total = Object.values(active.nodes);
+		const done = total.filter(Boolean);
+		const percent = done.length / total.length;
+		open.element.classList.add("running");
+		open.element.style.setProperty("--progress", percent * 100 + "%");
+		open.primary.element.classList.remove("mdi-progress-pencil");
+		open.primary.element.classList.add("mdi-play");
+	}
+
+	#removeActive() {
+		if (!this.activeElement) return;
+		this.activeElement.element.classList.remove("running");
+		this.activeElement.element.style.removeProperty("--progress");
+		this.activeElement.primary.element.classList.add("mdi-progress-pencil");
+		this.activeElement.primary.element.classList.remove("mdi-play");
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getFavoriteIcon(workflow) {
+		return workflow.isFavorite ? "star" : "file-outline";
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getFavoriteOverIcon(workflow) {
+		return workflow.isFavorite ? "star-off" : "star-outline";
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getFavoriteTooltip(workflow) {
+		return workflow.isFavorite ? "Remove this workflow from your favorites" : "Add this workflow to your favorites";
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getFavoriteButton(workflow, primary) {
+		return new ComfyButton({
+			icon: this.#getFavoriteIcon(workflow),
+			overIcon: this.#getFavoriteOverIcon(workflow),
+			iconSize: 18,
+			classList: "comfyui-button comfyui-workflows-file-action-favorite" + (primary ? " comfyui-workflows-file-action-primary" : ""),
+			tooltip: this.#getFavoriteTooltip(workflow),
+			action: (e) => {
+				e.stopImmediatePropagation();
+				workflow.favorite(!workflow.isFavorite);
+			},
+		});
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getDeleteButton(workflow) {
+		const deleteButton = new ComfyButton({
+			icon: "delete",
+			tooltip: "Delete this workflow",
+			classList: "comfyui-button comfyui-workflows-file-action",
+			iconSize: 18,
+			action: async (e, btn) => {
+				e.stopImmediatePropagation();
+
+				if (btn.icon === "delete-empty") {
+					btn.enabled = false;
+					await workflow.delete();
+					await this.load();
+				} else {
+					btn.icon = "delete-empty";
+					btn.element.style.background = "red";
+				}
+			},
+		});
+		deleteButton.element.addEventListener("mouseleave", () => {
+			deleteButton.icon = "delete";
+			deleteButton.element.style.removeProperty("background");
+		});
+		return deleteButton;
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getInsertButton(workflow) {
+		return new ComfyButton({
+			icon: "file-move-outline",
+			iconSize: 18,
+			tooltip: "Insert this workflow into the current workflow",
+			classList: "comfyui-button comfyui-workflows-file-action",
+			action: (e) => {
+				if (!this.app.shiftDown) {
+					this.popup.open = false;
+				}
+				e.stopImmediatePropagation();
+				if (!this.app.shiftDown) {
+					this.popup.open = false;
+				}
+				workflow.insert();
+			},
+		});
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getRenameButton(workflow) {
+		return new ComfyButton({
+			icon: "pencil",
+			tooltip: workflow.path ? "Rename this workflow" : "This workflow can't be renamed as it hasn't been saved.",
+			classList: "comfyui-button comfyui-workflows-file-action",
+			iconSize: 18,
+			enabled: !!workflow.path,
+			action: async (e) => {
+				e.stopImmediatePropagation();
+				const newName = prompt("Enter new name", workflow.path);
+				if (newName) {
+					await workflow.rename(newName);
+				}
+			},
+		});
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getWorkflowElement(workflow) {
+		return new WorkflowElement(this, workflow, {
+			primary: this.#getFavoriteButton(workflow, true),
+			buttons: [this.#getInsertButton(workflow), this.#getRenameButton(workflow), this.#getDeleteButton(workflow)],
+		});
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#createLeafNode(workflow) {
+		const fileNode = this.#getWorkflowElement(workflow);
+		this.treeFiles[workflow.path] = fileNode;
+		return fileNode;
+	}
+
+	#createNode(currentPath, workflow, i, currentRoot) {
+		const part = workflow.pathParts[i];
+
+		const parentNode = $el("ul" + (this.treeState[currentPath] ? "" : ".closed"), {
+			$: (el) => {
+				el.onclick = (e) => {
+					this.#expandNode(el, workflow, currentPath, i);
+					e.stopImmediatePropagation();
+				};
+			},
+		});
+		currentRoot.append(parentNode);
+
+		// Create a node for the current part and an inner UL for its children if it isnt a leaf node
+		const leaf = i === workflow.pathParts.length - 1;
+		let nodeElement;
+		if (leaf) {
+			nodeElement = this.#createLeafNode(workflow).element;
+		} else {
+			nodeElement = $el("li", [$el("i.mdi.mdi-18px.mdi-folder"), $el("span", part)]);
+		}
+		parentNode.append(nodeElement);
+		return parentNode;
+	}
+}
+
+class WorkflowElement {
+	/**
+	 * @param { ComfyWorkflowsContent } parent
+	 * @param { ComfyWorkflow } workflow
+	 */
+	constructor(parent, workflow, { tagName = "li", primary, buttons }) {
+		this.parent = parent;
+		this.workflow = workflow;
+		this.primary = primary;
+		this.buttons = buttons;
+
+		this.element = $el(
+			tagName + ".comfyui-workflows-tree-file",
+			{
+				onclick: () => {
+					workflow.load();
+					this.parent.popup.open = false;
+				},
+				title: this.workflow.path,
+			},
+			[this.primary?.element, $el("span", workflow.name), ...buttons.map((b) => b.element)]
+		);
+	}
+}
+
+class WidgetSelectionDialog extends ComfyAsyncDialog {
+	#options;
+
+	/**
+	 * @param {Array<{widget: {name: string}, node: {pos: [number, number], title: string, id: string, type: string}}>} options
+	 */
+	constructor(options) {
+		super();
+		this.#options = options;
+	}
+
+	show(app) {
+		this.element.classList.add("comfy-widget-selection-dialog");
+		return super.show(
+			$el("div", [
+				$el("h2", "Select image target"),
+				$el(
+					"p",
+					"This workflow has multiple image loader nodes, you can rename a node to include 'input' in the title for it to be automatically selected, or select one below."
+				),
+				$el(
+					"section",
+					this.#options.map((opt) => {
+						return $el("div.comfy-widget-selection-item", [
+							$el("span", { dataset: { id: opt.node.id } }, `${opt.node.title ?? opt.node.type} ${opt.widget.name}`),
+							$el(
+								"button.comfyui-button",
+								{
+									onclick: () => {
+										app.canvas.ds.offset[0] = -opt.node.pos[0] + 50;
+										app.canvas.ds.offset[1] = -opt.node.pos[1] + 50;
+										app.canvas.selectNode(opt.node);
+										app.graph.setDirtyCanvas(true, true);
+									},
+								},
+								"Show"
+							),
+							$el(
+								"button.comfyui-button.primary",
+								{
+									onclick: () => {
+										this.close(opt);
+									},
+								},
+								"Select"
+							),
+						]);
+					})
+				),
+			])
+		);
+	}
+}
\ No newline at end of file
diff --git a/web/scripts/ui/settings.js b/web/scripts/ui/settings.js
index 9e9d13af0..819e4e7d6 100644
--- a/web/scripts/ui/settings.js
+++ b/web/scripts/ui/settings.js
@@ -47,6 +47,17 @@ export class ComfySettingsDialog extends ComfyDialog {
 		return Object.values(this.settingsLookup);
 	}
 
+	#dispatchChange(id, value, oldValue) {
+		this.dispatchEvent(
+			new CustomEvent(id + ".change", {
+				detail: {
+					value,
+					oldValue
+				},
+			})
+		);
+	}
+
 	async load() {
 		if (this.app.storageLocation === "browser") {
 			this.settingsValues = localStorage;
@@ -56,7 +67,9 @@ export class ComfySettingsDialog extends ComfyDialog {
 
 		// Trigger onChange for any settings added before load
 		for (const id in this.settingsLookup) {
-			this.settingsLookup[id].onChange?.(this.settingsValues[this.getId(id)]);
+			const value = this.settingsValues[this.getId(id)];
+			this.settingsLookup[id].onChange?.(value);
+			this.#dispatchChange(id, value);
 		}
 	}
 
@@ -90,6 +103,7 @@ export class ComfySettingsDialog extends ComfyDialog {
 		if (id in this.settingsLookup) {
 			this.settingsLookup[id].onChange?.(value, oldValue);
 		}
+		this.#dispatchChange(id, value, oldValue);
 
 		await api.storeSetting(id, value);
 	}
@@ -136,6 +150,8 @@ export class ComfySettingsDialog extends ComfyDialog {
 			onChange,
 			name,
 			render: () => {
+				if (type === "hidden") return;
+
 				const setter = (v) => {
 					if (onChange) {
 						onChange(v, value);
@@ -310,7 +326,7 @@ export class ComfySettingsDialog extends ComfyDialog {
 				},
 				[$el("th"), $el("th", { style: { width: "33%" } })]
 			),
-			...this.settings.sort((a, b) => a.name.localeCompare(b.name)).map((s) => s.render())
+			...this.settings.sort((a, b) => a.name.localeCompare(b.name)).map((s) => s.render()).filter(Boolean)
 		);
 		this.element.showModal();
 	}
diff --git a/web/scripts/ui/utils.js b/web/scripts/ui/utils.js
new file mode 100644
index 000000000..e37d8b41e
--- /dev/null
+++ b/web/scripts/ui/utils.js
@@ -0,0 +1,56 @@
+/**
+ * @typedef {  string | string[] | Record<string, boolean> } ClassList
+ */
+
+/**
+ * @param { HTMLElement } element
+ * @param { ClassList } classList
+ * @param { string[] } requiredClasses
+ */
+export function applyClasses(element, classList, ...requiredClasses) {
+	classList ??= "";
+
+	let str;
+	if (typeof classList === "string") {
+		str = classList;
+	} else if (classList instanceof Array) {
+		str = classList.join(" ");
+	} else {
+		str = Object.entries(classList).reduce((p, c) => {
+			if (c[1]) {
+				p += (p.length ? " " : "") + c[0];
+			}
+			return p;
+		}, "");
+	}
+	element.className = str;
+	if (requiredClasses) {
+		element.classList.add(...requiredClasses);
+	}
+}
+
+/**
+ * @param { HTMLElement } element
+ * @param { { onHide?: (el: HTMLElement) => void, onShow?: (el: HTMLElement, value) => void } } [param1]
+ * @returns
+ */
+export function toggleElement(element, { onHide, onShow } = {}) {
+	let placeholder;
+	let hidden;
+	return (value) => {
+		if (value) {
+			if (hidden) {
+				hidden = false;
+				placeholder.replaceWith(element);
+			}
+			onShow?.(element, value);
+		} else {
+			if (!placeholder) {
+				placeholder = document.createComment("");
+			}
+			hidden = true;
+			element.replaceWith(placeholder);
+			onHide?.(element);
+		}
+	};
+}
diff --git a/web/scripts/utils.js b/web/scripts/utils.js
index 01b988462..cda7600f6 100644
--- a/web/scripts/utils.js
+++ b/web/scripts/utils.js
@@ -1,4 +1,5 @@
 import { $el } from "./ui.js";
+import { api } from "./api.js";
 
 // Simple date formatter
 const parts = {
@@ -25,6 +26,19 @@ function formatDate(text, date) {
 	});
 }
 
+
+export function clone(obj) {
+	try {
+		if (typeof structuredClone !== "undefined") {
+			return structuredClone(obj);
+		}
+	} catch (error) {
+		// structuredClone is stricter than using JSON.parse/stringify so fallback to that
+	}
+
+	return JSON.parse(JSON.stringify(obj));
+}
+
 export function applyTextReplacements(app, value) {
 	return value.replace(/%([^%]+)%/g, function (match, text) {
 		const split = text.split(".");
@@ -86,3 +100,57 @@ export async function addStylesheet(urlOrFile, relativeTo) {
 		});
 	});
 }
+
+/**
+ * @param { string } filename
+ * @param { Blob } blob
+ */
+export function downloadBlob(filename, blob) {
+	const url = URL.createObjectURL(blob);
+	const a = $el("a", {
+		href: url,
+		download: filename,
+		style: { display: "none" },
+		parent: document.body,
+	});
+	a.click();
+	setTimeout(function () {
+		a.remove();
+		window.URL.revokeObjectURL(url);
+	}, 0);
+}
+
+/**
+ * @template T
+ * @param {string} name
+ * @param {T} [defaultValue]
+ * @param {(currentValue: any, previousValue: any)=>void} [onChanged]
+ * @returns {T}
+ */
+export function prop(target, name, defaultValue, onChanged) {
+	let currentValue;
+	Object.defineProperty(target, name, {
+		get() {
+			return currentValue;
+		},
+		set(newValue) {
+			const prevValue = currentValue;
+			currentValue = newValue;
+			onChanged?.(currentValue, prevValue, target, name);
+		},
+	});
+	return defaultValue;
+}
+
+export function getStorageValue(id) {
+	const clientId = api.clientId ?? api.initialClientId;
+	return (clientId && sessionStorage.getItem(`${id}:${clientId}`)) ?? localStorage.getItem(id);
+}
+
+export function setStorageValue(id, value) {
+	const clientId = api.clientId ?? api.initialClientId;
+	if (clientId) {
+		sessionStorage.setItem(`${id}:${clientId}`, value);
+	}
+	localStorage.setItem(id, value);
+}
\ No newline at end of file
diff --git a/web/scripts/workflows.js b/web/scripts/workflows.js
new file mode 100644
index 000000000..16bbc9976
--- /dev/null
+++ b/web/scripts/workflows.js
@@ -0,0 +1,450 @@
+// @ts-check
+
+import { api } from "./api.js";
+import { ChangeTracker } from "./changeTracker.js";
+import { ComfyAsyncDialog } from "./ui/components/asyncDialog.js";
+import { getStorageValue, setStorageValue } from "./utils.js";
+
+function appendJsonExt(path) {
+	if (!path.toLowerCase().endsWith(".json")) {
+		path += ".json";
+	}
+	return path;
+}
+
+export function trimJsonExt(path) {
+	return path?.replace(/\.json$/, "");
+}
+
+export class ComfyWorkflowManager extends EventTarget {
+	/** @type {string | null} */
+	#activePromptId = null;
+	#unsavedCount = 0;
+	#activeWorkflow;
+
+	/** @type {Record<string, ComfyWorkflow>} */
+	workflowLookup = {};
+	/** @type {Array<ComfyWorkflow>} */
+	workflows = [];
+	/** @type {Array<ComfyWorkflow>} */
+	openWorkflows = [];
+	/** @type {Record<string, {workflow?: ComfyWorkflow, nodes?: Record<string, boolean>}>} */
+	queuedPrompts = {};
+
+	get activeWorkflow() {
+		return this.#activeWorkflow ?? this.openWorkflows[0];
+	}
+
+	get activePromptId() {
+		return this.#activePromptId;
+	}
+
+	get activePrompt() {
+		return this.queuedPrompts[this.#activePromptId];
+	}
+
+	/**
+	 * @param {import("./app.js").ComfyApp} app
+	 */
+	constructor(app) {
+		super();
+		this.app = app;
+		ChangeTracker.init(app);
+
+		this.#bindExecutionEvents();
+	}
+
+	#bindExecutionEvents() {
+		// TODO: on reload, set active prompt based on the latest ws message
+
+		const emit = () => this.dispatchEvent(new CustomEvent("execute", { detail: this.activePrompt }));
+		let executing = null;
+		api.addEventListener("execution_start", (e) => {
+			this.#activePromptId = e.detail.prompt_id;
+
+			// This event can fire before the event is stored, so put a placeholder
+			this.queuedPrompts[this.#activePromptId] ??= { nodes: {} };
+			emit();
+		});
+		api.addEventListener("execution_cached", (e) => {
+			if (!this.activePrompt) return;
+			for (const n of e.detail.nodes) {
+				this.activePrompt.nodes[n] = true;
+			}
+			emit();
+		});
+		api.addEventListener("executed", (e) => {
+			if (!this.activePrompt) return;
+			this.activePrompt.nodes[e.detail.node] = true;
+			emit();
+		});
+		api.addEventListener("executing", (e) => {
+			if (!this.activePrompt) return;
+
+			if (executing) {
+				// Seems sometimes nodes that are cached fire executing but not executed
+				this.activePrompt.nodes[executing] = true;
+			}
+			executing = e.detail;
+			if (!executing) {
+				delete this.queuedPrompts[this.#activePromptId];
+				this.#activePromptId = null;
+			}
+			emit();
+		});
+	}
+
+	async loadWorkflows() {
+		try {
+			let favorites;
+			const resp = await api.getUserData("workflows/.index.json");
+			let info;
+			if (resp.status === 200) {
+				info = await resp.json();
+				favorites = new Set(info?.favorites ?? []);
+			} else {
+				favorites = new Set();
+			}
+
+			const workflows = (await api.listUserData("workflows", true, true)).map((w) => {
+				let workflow = this.workflowLookup[w[0]];
+				if (!workflow) {
+					workflow = new ComfyWorkflow(this, w[0], w.slice(1), favorites.has(w[0]));
+					this.workflowLookup[workflow.path] = workflow;
+				}
+				return workflow;
+			});
+
+			this.workflows = workflows;
+		} catch (error) {
+			alert("Error loading workflows: " + (error.message ?? error));
+			this.workflows = [];
+		}
+	}
+
+	async saveWorkflowMetadata() {
+		await api.storeUserData("workflows/.index.json", {
+			favorites: [...this.workflows.filter((w) => w.isFavorite).map((w) => w.path)],
+		});
+	}
+
+	/**
+	 * @param {string | ComfyWorkflow | null} workflow
+	 */
+	setWorkflow(workflow) {
+		if (workflow && typeof workflow === "string") {
+			// Selected by path, i.e. on reload of last workflow
+			const found = this.workflows.find((w) => w.path === workflow);
+			if (found) {
+				workflow = found;
+				workflow.unsaved = !workflow || getStorageValue("Comfy.PreviousWorkflowUnsaved") === "true";
+			}
+		}
+
+		if (!(workflow instanceof ComfyWorkflow)) {
+			// Still not found, either reloading a deleted workflow or blank
+			workflow = new ComfyWorkflow(this, workflow || "Unsaved Workflow" + (this.#unsavedCount++ ? ` (${this.#unsavedCount})` : ""));
+		}
+
+		const index = this.openWorkflows.indexOf(workflow);
+		if (index === -1) {
+			// Opening a new workflow
+			this.openWorkflows.push(workflow);
+		}
+
+		this.#activeWorkflow = workflow;
+
+		setStorageValue("Comfy.PreviousWorkflow", this.activeWorkflow.path ?? "");
+		this.dispatchEvent(new CustomEvent("changeWorkflow"));
+	}
+
+	storePrompt({ nodes, id }) {
+		this.queuedPrompts[id] ??= {};
+		this.queuedPrompts[id].nodes = {
+			...nodes.reduce((p, n) => {
+				p[n] = false;
+				return p;
+			}, {}),
+			...this.queuedPrompts[id].nodes,
+		};
+		this.queuedPrompts[id].workflow = this.activeWorkflow;
+	}
+
+	/**
+	 * @param {ComfyWorkflow} workflow
+	 */
+	async closeWorkflow(workflow, warnIfUnsaved = true) {
+		if (!workflow.isOpen) {
+			return true;
+		}
+		if (workflow.unsaved && warnIfUnsaved) {
+			const res = await ComfyAsyncDialog.prompt({
+				title: "Save Changes?",
+				message: `Do you want to save changes to "${workflow.path ?? workflow.name}" before closing?`,
+				actions: ["Yes", "No", "Cancel"],
+			});
+			if (res === "Yes") {
+				const active = this.activeWorkflow;
+				if (active !== workflow) {
+					// We need to switch to the workflow to save it
+					await workflow.load();
+				}
+
+				if (!(await workflow.save())) {
+					// Save was canceled, restore the previous workflow
+					if (active !== workflow) {
+						await active.load();
+					}
+					return;
+				}
+			} else if (res === "Cancel") {
+				return;
+			}
+		}
+		workflow.changeTracker = null;
+		this.openWorkflows.splice(this.openWorkflows.indexOf(workflow), 1);
+		if (this.openWorkflows.length) {
+			this.#activeWorkflow = this.openWorkflows[0];
+			await this.#activeWorkflow.load();
+		} else {
+			// Load default
+			await this.app.loadGraphData();
+		}
+	}
+}
+
+export class ComfyWorkflow {
+	#name;
+	#path;
+	#pathParts;
+	#isFavorite = false;
+	/** @type {ChangeTracker | null} */
+	changeTracker = null;
+	unsaved = false;
+
+	get name() {
+		return this.#name;
+	}
+
+	get path() {
+		return this.#path;
+	}
+
+	get pathParts() {
+		return this.#pathParts;
+	}
+
+	get isFavorite() {
+		return this.#isFavorite;
+	}
+
+	get isOpen() {
+		return !!this.changeTracker;
+	}
+
+	/**
+	 * @overload
+	 * @param {ComfyWorkflowManager} manager
+	 * @param {string} path
+	 */
+	/**
+	 * @overload
+	 * @param {ComfyWorkflowManager} manager
+	 * @param {string} path
+	 * @param {string[]} pathParts
+	 * @param {boolean} isFavorite
+	 */
+	/**
+	 * @param {ComfyWorkflowManager} manager
+	 * @param {string} path
+	 * @param {string[]} [pathParts]
+	 * @param {boolean} [isFavorite]
+	 */
+	constructor(manager, path, pathParts, isFavorite) {
+		this.manager = manager;
+		if (pathParts) {
+			this.#updatePath(path, pathParts);
+			this.#isFavorite = isFavorite;
+		} else {
+			this.#name = path;
+			this.unsaved = true;
+		}
+	}
+
+	/**
+	 * @param {string} path
+	 * @param {string[]} [pathParts]
+	 */
+	#updatePath(path, pathParts) {
+		this.#path = path;
+
+		if (!pathParts) {
+			if (!path.includes("\\")) {
+				pathParts = path.split("/");
+			} else {
+				pathParts = path.split("\\");
+			}
+		}
+
+		this.#pathParts = pathParts;
+		this.#name = trimJsonExt(pathParts[pathParts.length - 1]);
+	}
+
+	async getWorkflowData() {
+		const resp = await api.getUserData("workflows/" + this.path);
+		if (resp.status !== 200) {
+			alert(`Error loading workflow file '${this.path}': ${resp.status} ${resp.statusText}`);
+			return;
+		}
+		return await resp.json();
+	}
+
+	load = async () => {
+		if (this.isOpen) {
+			await this.manager.app.loadGraphData(this.changeTracker.activeState, true, this);
+		} else {
+			const data = await this.getWorkflowData();
+			if (!data) return;
+			await this.manager.app.loadGraphData(data, true, this);
+		}
+	};
+
+	async save(saveAs = false) {
+		if (!this.path || saveAs) {
+			return !!(await this.#save(null, false));
+		} else {
+			return !!(await this.#save(this.path, true));
+		}
+	}
+
+	/**
+	 * @param {boolean} value
+	 */
+	async favorite(value) {
+		try {
+			if (this.#isFavorite === value) return;
+			this.#isFavorite = value;
+			await this.manager.saveWorkflowMetadata();
+			this.manager.dispatchEvent(new CustomEvent("favorite", { detail: this }));
+		} catch (error) {
+			alert("Error favoriting workflow " + this.path + "\n" + (error.message ?? error));
+		}
+	}
+
+	/**
+	 * @param {string} path
+	 */
+	async rename(path) {
+		path = appendJsonExt(path);
+		let resp = await api.moveUserData("workflows/" + this.path, "workflows/" + path);
+
+		if (resp.status === 409) {
+			if (!confirm(`Workflow '${path}' already exists, do you want to overwrite it?`)) return resp;
+			resp = await api.moveUserData("workflows/" + this.path, "workflows/" + path, { overwrite: true });
+		}
+
+		if (resp.status !== 200) {
+			alert(`Error renaming workflow file '${this.path}': ${resp.status} ${resp.statusText}`);
+			return;
+		}
+
+		const isFav = this.isFavorite;
+		if (isFav) {
+			await this.favorite(false);
+		}
+		path = (await resp.json()).substring("workflows/".length);
+		this.#updatePath(path, null);
+		if (isFav) {
+			await this.favorite(true);
+		}
+		this.manager.dispatchEvent(new CustomEvent("rename", { detail: this }));
+		setStorageValue("Comfy.PreviousWorkflow", this.path ?? "");
+	}
+
+	async insert() {
+		const data = await this.getWorkflowData();
+		if (!data) return;
+
+		const old = localStorage.getItem("litegrapheditor_clipboard");
+		const graph = new LGraph(data);
+		const canvas = new LGraphCanvas(null, graph, { skip_events: true, skip_render: true });
+		canvas.selectNodes();
+		canvas.copyToClipboard();
+		this.manager.app.canvas.pasteFromClipboard();
+		localStorage.setItem("litegrapheditor_clipboard", old);
+	}
+
+	async delete() {
+		// TODO: fix delete of current workflow - should mark workflow as unsaved and when saving use old name by default
+
+		try {
+			if (this.isFavorite) {
+				await this.favorite(false);
+			}
+			await api.deleteUserData("workflows/" + this.path);
+			this.unsaved = true;
+			this.#path = null;
+			this.#pathParts = null;
+			this.manager.workflows.splice(this.manager.workflows.indexOf(this), 1);
+			this.manager.dispatchEvent(new CustomEvent("delete", { detail: this }));
+		} catch (error) {
+			alert(`Error deleting workflow: ${error.message || error}`);
+		}
+	}
+
+	track() {
+		if (this.changeTracker) {
+			this.changeTracker.restore();
+		} else {
+			this.changeTracker = new ChangeTracker(this);
+		}
+	}
+
+	/**
+	 * @param {string|null} path
+	 * @param {boolean} overwrite
+	 */
+	async #save(path, overwrite) {
+		if (!path) {
+			path = prompt("Save workflow as:", trimJsonExt(this.path) ?? this.name ?? "workflow");
+			if (!path) return;
+		}
+
+		path = appendJsonExt(path);
+
+		const p = await this.manager.app.graphToPrompt();
+		const json = JSON.stringify(p.workflow, null, 2);
+		let resp = await api.storeUserData("workflows/" + path, json, { stringify: false, throwOnError: false, overwrite });
+		if (resp.status === 409) {
+			if (!confirm(`Workflow '${path}' already exists, do you want to overwrite it?`)) return;
+			resp = await api.storeUserData("workflows/" + path, json, { stringify: false });
+		}
+
+		if (resp.status !== 200) {
+			alert(`Error saving workflow '${this.path}': ${resp.status} ${resp.statusText}`);
+			return;
+		}
+
+		path = (await resp.json()).substring("workflows/".length);
+
+		if (!this.path) {
+			// Saved new workflow, patch this instance
+			this.#updatePath(path, null);
+			await this.manager.loadWorkflows();
+			this.unsaved = false;
+			this.manager.dispatchEvent(new CustomEvent("rename", { detail: this }));
+			setStorageValue("Comfy.PreviousWorkflow", this.path ?? "");
+		} else if (path !== this.path) {
+			// Saved as, open the new copy
+			await this.manager.loadWorkflows();
+			const workflow = this.manager.workflowLookup[path];
+			await workflow.load();
+		} else {
+			// Normal save
+			this.unsaved = false;
+			this.manager.dispatchEvent(new CustomEvent("save", { detail: this }));
+		}
+
+		return true;
+	}
+}
diff --git a/web/style.css b/web/style.css
index cf7a8b9ea..8091a489f 100644
--- a/web/style.css
+++ b/web/style.css
@@ -1,3 +1,5 @@
+@import url("scripts/ui/menu/menu.css");
+
 :root {
 	--fg-color: #000;
 	--bg-color: #fff;
@@ -10,12 +12,24 @@
 	--border-color: #4e4e4e;
 	--tr-even-bg-color: #222;
 	--tr-odd-bg-color: #353535;
+	--primary-bg: #236692;
+	--primary-fg: #ffffff;
+	--primary-hover-bg: #3485bb;
+	--primary-hover-fg: #ffffff;
+	--content-bg: #e0e0e0;
+	--content-fg: #000;
+	--content-hover-bg: #adadad;
+	--content-hover-fg: #000;
 }
 
 @media (prefers-color-scheme: dark) {
 	:root {
 		--fg-color: #fff;
 		--bg-color: #202020;
+		--content-bg: #4e4e4e;
+		--content-fg: #fff;
+		--content-hover-bg: #222;
+		--content-hover-fg: #fff;
 	}
 }
 
@@ -26,11 +40,41 @@ body {
 	overflow: hidden;
 	background-color: var(--bg-color);
 	color: var(--fg-color);
+	grid-template-columns: auto 1fr auto;
+	grid-template-rows: auto auto 1fr auto;
+	min-height: -webkit-fill-available;
+	max-height: -webkit-fill-available;
+	min-width: -webkit-fill-available;
+	max-width: -webkit-fill-available;
+}
+
+.comfyui-body-top {
+	order: 0;
+	grid-column: 1/-1;
+	z-index: 10;
+}
+
+.comfyui-body-left {
+	order: 1;
+	z-index: 10;
 }
 
 #graph-canvas {
 	width: 100%;
 	height: 100%;
+	order: 2;
+	grid-column: 1/-1;
+}
+
+.comfyui-body-right {
+	order: 3;
+	z-index: 10;
+}
+
+.comfyui-body-bottom {
+	order: 4;
+	grid-column: 1/-1;
+	z-index: 10;
 }
 
 .comfy-multiline-input {
@@ -364,6 +408,37 @@ dialog::backdrop {
 	background: rgba(0, 0, 0, 0.5);
 }
 
+.comfy-dialog.comfyui-dialog {
+	top: 0;
+}
+
+.comfy-dialog.comfy-modal {
+	font-family: Arial, sans-serif;
+	border-color: var(--bg-color);
+	box-shadow: none;
+  	border: 2px solid var(--border-color);
+}
+
+.comfy-dialog .comfy-modal-content {
+	flex-direction: row;
+	flex-wrap: wrap;
+	gap: 10px;
+	color: var(--fg-color);
+}
+
+.comfy-dialog .comfy-modal-content h3 {
+	margin-top: 0;
+}
+
+.comfy-dialog .comfy-modal-content > p {
+	width: 100%;
+}
+
+.comfy-dialog .comfy-modal-content > .comfyui-button {
+	flex: 1;
+	justify-content: center;
+}
+
 #comfy-settings-dialog {
 	padding: 0;
 	width: 41rem;
diff --git a/web/types/comfy.d.ts b/web/types/comfy.d.ts
index f7129b555..9a338b349 100644
--- a/web/types/comfy.d.ts
+++ b/web/types/comfy.d.ts
@@ -10,24 +10,24 @@ export interface ComfyExtension {
 	 * Allows any initialisation, e.g. loading resources. Called after the canvas is created but before nodes are added
 	 * @param app The ComfyUI app instance
 	 */
-	init(app: ComfyApp): Promise<void>;
+	init?(app: ComfyApp): Promise<void>;
 	/**
 	 * Allows any additonal setup, called after the application is fully set up and running
 	 * @param app The ComfyUI app instance
 	 */
-	setup(app: ComfyApp): Promise<void>;
+	setup?(app: ComfyApp): Promise<void>;
 	/**
 	 * Called before nodes are registered with the graph
 	 * @param defs The collection of node definitions, add custom ones or edit existing ones
 	 * @param app The ComfyUI app instance
 	 */
-	addCustomNodeDefs(defs: Record<string, ComfyObjectInfo>, app: ComfyApp): Promise<void>;
+	addCustomNodeDefs?(defs: Record<string, ComfyObjectInfo>, app: ComfyApp): Promise<void>;
 	/**
 	 * Allows the extension to add custom widgets
 	 * @param app The ComfyUI app instance
 	 * @returns An array of {[widget name]: widget data}
 	 */
-	getCustomWidgets(
+	getCustomWidgets?(
 		app: ComfyApp
 	): Promise<
 		Record<string, (node, inputName, inputData, app) => { widget?: IWidget; minWidth?: number; minHeight?: number }>
@@ -38,12 +38,12 @@ export interface ComfyExtension {
 	 * @param nodeData The original node object info config object
 	 * @param app The ComfyUI app instance
 	 */
-	beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyObjectInfo, app: ComfyApp): Promise<void>;
+	beforeRegisterNodeDef?(nodeType: typeof LGraphNode, nodeData: ComfyObjectInfo, app: ComfyApp): Promise<void>;
 	/**
 	 * Allows the extension to register additional nodes with LGraph after standard nodes are added
 	 * @param app The ComfyUI app instance
 	 */
-	registerCustomNodes(app: ComfyApp): Promise<void>;
+	registerCustomNodes?(app: ComfyApp): Promise<void>;
 	/**
 	 * Allows the extension to modify a node that has been reloaded onto the graph.
 	 * If you break something in the backend and want to patch workflows in the frontend
@@ -51,13 +51,13 @@ export interface ComfyExtension {
 	 * @param node The node that has been loaded
 	 * @param app The ComfyUI app instance
 	 */
-	loadedGraphNode(node: LGraphNode, app: ComfyApp);
+	loadedGraphNode?(node: LGraphNode, app: ComfyApp);
 	/**
 	 * Allows the extension to run code after the constructor of the node
 	 * @param node The node that has been created
 	 * @param app The ComfyUI app instance
 	 */
-	nodeCreated(node: LGraphNode, app: ComfyApp);
+	nodeCreated?(node: LGraphNode, app: ComfyApp);
 }
 
 export type ComfyObjectInfo = {

From 69d710e40f43b87dd7124b9ee1e8982f987827e9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 25 Jun 2024 07:41:52 -0400
Subject: [PATCH 117/315] Implement my alternative take on CFG++ as the
 euler_pp sampler.

Add euler_ancestral_pp which is the ancestral version of euler with the
same modification.
---
 comfy/k_diffusion/sampling.py           | 55 ++++++++++++++++++++++++-
 comfy/samplers.py                       |  2 +-
 comfy_extras/nodes_advanced_samplers.py | 25 +----------
 3 files changed, 56 insertions(+), 26 deletions(-)

diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 78896b8b5..9b58cb1dc 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -7,7 +7,7 @@ import torchsde
 from tqdm.auto import trange, tqdm
 
 from . import utils
-
+import comfy.model_patcher
 
 def append_zero(x):
     return torch.cat([x, x.new_zeros([1])])
@@ -945,3 +945,56 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
             buffer_model.append(d_cur.detach())
 
     return x_next
+
+
+@torch.no_grad()
+def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        sigma_hat = sigmas[i]
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x - denoised + temp[0], sigma_hat, denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        # Euler method
+        x = x + d * dt
+    return x
+
+@torch.no_grad()
+def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with Euler method steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        d = to_d(x - denoised + temp[0], sigmas[i], denoised)
+        # Euler method
+        dt = sigma_down - sigmas[i]
+        x = x + d * dt
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+    return x
diff --git a/comfy/samplers.py b/comfy/samplers.py
index 55b1486b4..c3a80dde0 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -537,7 +537,7 @@ class Sampler:
         sigma = float(sigmas[0])
         return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
 
-KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+KSAMPLER_NAMES = ["euler", "euler_pp", "euler_ancestral", "euler_ancestral_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
                   "ipndm", "ipndm_v"]
diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py
index 7aa89c644..cee3a10c4 100644
--- a/comfy_extras/nodes_advanced_samplers.py
+++ b/comfy_extras/nodes_advanced_samplers.py
@@ -82,29 +82,6 @@ def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable
         x = denoised + sigmas[i + 1] * d
     return x
 
-@torch.no_grad()
-def sample_euler_cfgpp_alt(model, x, sigmas, extra_args=None, callback=None, disable=None):
-    extra_args = {} if extra_args is None else extra_args
-
-    temp = [0]
-    def post_cfg_function(args):
-        temp[0] = args["uncond_denoised"]
-        return args["denoised"]
-
-    model_options = extra_args.get("model_options", {}).copy()
-    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
-
-    s_in = x.new_ones([x.shape[0]])
-    for i in trange(len(sigmas) - 1, disable=disable):
-        sigma_hat = sigmas[i]
-        denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x - denoised + temp[0], sigma_hat, denoised)
-        if callback is not None:
-            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
-        dt = sigmas[i + 1] - sigma_hat
-        # Euler method
-        x = x + d * dt
-    return x
 
 class SamplerEulerCFGpp:
     @classmethod
@@ -122,7 +99,7 @@ class SamplerEulerCFGpp:
         if version == "regular":
             sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp)
         else:
-            sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp_alt)
+            sampler = comfy.samplers.ksampler("euler_pp")
         return (sampler, )
 
 NODE_CLASS_MAPPINGS = {

From e99d97a9d943c3601ccdf411608bffb771db5bf1 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 26 Jun 2024 01:23:55 -0400
Subject: [PATCH 118/315] Remove duplicated Reset View button (#3865)

* Remove duplicated Reset View button

* Disable flaky test
---
 .github/workflows/test-browser.yml | 2 +-
 web/scripts/ui.js                  | 1 -
 2 files changed, 1 insertion(+), 2 deletions(-)

diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml
index 049b1cde2..323c96cf3 100644
--- a/.github/workflows/test-browser.yml
+++ b/.github/workflows/test-browser.yml
@@ -24,7 +24,7 @@ jobs:
       with:
         repository: "huchenlei/ComfyUI_frontend"
         path: "ComfyUI_frontend"
-        ref: "23a4ee83144d9bf22e7218b9c78c71f8544c8b27"
+        ref: "577aea9dfb680a14b3ae4fa626a85098c3207030"
     - uses: actions/setup-node@v3
       with:
         node-version: lts/*
diff --git a/web/scripts/ui.js b/web/scripts/ui.js
index 87b4887eb..bb094d8b4 100644
--- a/web/scripts/ui.js
+++ b/web/scripts/ui.js
@@ -603,7 +603,6 @@ export class ComfyUI {
 				onclick: () => app.refreshComboInNodes()
 			}),
 			$el("button", {id: "comfy-clipspace-button", textContent: "Clipspace", onclick: () => app.openClipspace()}),
-			$el("button", {id: "comfy-reset-view-button", textContent: "Reset View", onclick: () => app.resetView()}),
 			$el("button", {
 				id: "comfy-clear-button", textContent: "Clear", onclick: () => {
 					if (!confirmClear.value || confirm("Clear workflow?")) {

From edfce78c86c7615039dd8d74e89957a0eebbdc03 Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin"
 <4000772+mcmonkey4eva@users.noreply.github.com>
Date: Tue, 25 Jun 2024 22:37:27 -0700
Subject: [PATCH 119/315] add issue templates for ComfyUI Issues Page (#3868)

---
 .github/ISSUE_TEMPLATE/bug-report.yml      | 45 ++++++++++++++++++++++
 .github/ISSUE_TEMPLATE/config.yml          |  8 ++++
 .github/ISSUE_TEMPLATE/feature-request.yml | 32 +++++++++++++++
 .github/ISSUE_TEMPLATE/user-support.yml    | 32 +++++++++++++++
 4 files changed, 117 insertions(+)
 create mode 100644 .github/ISSUE_TEMPLATE/bug-report.yml
 create mode 100644 .github/ISSUE_TEMPLATE/config.yml
 create mode 100644 .github/ISSUE_TEMPLATE/feature-request.yml
 create mode 100644 .github/ISSUE_TEMPLATE/user-support.yml

diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml
new file mode 100644
index 000000000..87bef3681
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -0,0 +1,45 @@
+name: Bug Report
+description: "Something is broken inside of ComfyUI. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)"
+labels: [ "Potential Bug" ]
+body:
+    - type: markdown
+      attributes:
+        value: |
+                Before submitting a **Bug Report**, please ensure the following:
+
+                **1:** You are running the latest version of ComfyUI.
+                **2:** You have looked at the existing bug reports and made sure this isn't already reported.
+                **3:** This is an actual bug in ComfyUI, not just a support question and not caused by an custom node. A bug is when you can specify exact steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
+
+                If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: textarea
+      attributes:
+            label: Expected Behavior
+            description: "What you expected to happen."
+      validations:
+            required: true
+    - type: textarea
+      attributes:
+                label: Actual Behavior
+                description: "What actually happened. Please include a screenshot of the issue if possible."
+      validations:
+                required: true
+    - type: textarea
+      attributes:
+                label: Steps to Reproduce
+                description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author."
+      validations:
+                required: true
+    - type: textarea
+      attributes:
+                label: Debug Logs
+                description: "Please copy the output from your terminal logs here."
+                render: powershell
+      validations:
+                required: true
+    - type: textarea
+      attributes:
+                label: Other
+                description: "Any other additional information you think might be helpful."
+      validations:
+                required: false
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 000000000..2c519ede5
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,8 @@
+blank_issues_enabled: true
+contact_links:
+  - name: ComfyUI Matrix Space
+    url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
+    about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source).
+  - name: Comfy Org Discord
+    url: https://discord.gg/comfyorg
+    about: The Comfy Org Discord is available for support and general discussion related to ComfyUI.
diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml
new file mode 100644
index 000000000..419721b63
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature-request.yml
@@ -0,0 +1,32 @@
+name: Feature Request
+description: "You have an idea for something new you would like to see added to ComfyUI's core."
+labels: [ "Feature" ]
+body:
+    - type: markdown
+      attributes:
+        value: |
+                Before submitting a **Feature Request**, please ensure the following:
+
+                **1:** You are running the latest version of ComfyUI.
+                **2:** You have looked to make sure there is not already a feature that does what you need, and there is not already a Feature Request listed for the same idea.
+                **3:** This is something that makes sense to add to ComfyUI Core, and wouldn't make more sense as a custom node.
+
+                If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: textarea
+      attributes:
+            label: Feature Idea
+            description: "Describe the feature you want to see."
+      validations:
+            required: true
+    - type: textarea
+      attributes:
+                label: Existing Solutions
+                description: "Please search through available custom nodes / extensions to see if there are existing custom solutions for this. If so, please link the options you found here as a reference."
+      validations:
+                required: false
+    - type: textarea
+      attributes:
+                label: Other
+                description: "Any other additional information you think might be helpful."
+      validations:
+                required: false
diff --git a/.github/ISSUE_TEMPLATE/user-support.yml b/.github/ISSUE_TEMPLATE/user-support.yml
new file mode 100644
index 000000000..df28804c6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/user-support.yml
@@ -0,0 +1,32 @@
+name: User Support
+description: "Use this if you need help with something, or you're experiencing an issue."
+labels: [ "User Support" ]
+body:
+    - type: markdown
+      attributes:
+        value: |
+            Before submitting a **User Report** issue, please ensure the following:
+
+            **1:** You are running the latest version of ComfyUI.
+            **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
+
+                If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: textarea
+      attributes:
+            label: Your question
+            description: "Post your question here. Please be as detailed as possible."
+      validations:
+            required: true
+    - type: textarea
+      attributes:
+                label: Logs
+                description: "If your question relates to an issue you're experiencing, please go to `Server` -> `Logs` -> potentially set `View Type` to `Debug` as well, then copypaste all the text into here."
+                render: powershell
+      validations:
+                required: false
+    - type: textarea
+      attributes:
+                label: Other
+                description: "Any other additional information you think might be helpful."
+      validations:
+                required: false

From e3579f33600fea742611cc62503b446833d45735 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Wed, 26 Jun 2024 14:08:48 +0100
Subject: [PATCH 120/315] Fix merge issue breaking api json loading (#3876)

---
 web/scripts/app.js | 1 -
 1 file changed, 1 deletion(-)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index 725538563..a354ff805 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -2321,7 +2321,6 @@ export class ComfyApp {
 			node.id = isNaN(+id) ? id : +id;
 			node.title = data._meta?.title ?? node.title
 			app.graph.add(node);
-			graph.add(node);
 		}
 
 		this.changeWorkflow(() => {

From f12fa1d8d7b42888b6491aa6aa4956aefca3aba5 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 26 Jun 2024 09:09:21 -0400
Subject: [PATCH 121/315] Enable browser tests on push (#3878)

---
 .github/workflows/test-browser.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml
index 323c96cf3..987a72ae3 100644
--- a/.github/workflows/test-browser.yml
+++ b/.github/workflows/test-browser.yml
@@ -7,6 +7,8 @@
 name: Playwright Browser Tests CI
 
 on:
+  push:
+    branches: [ main, master ]
   pull_request:
     branches: [ main, master ]
 

From a3e83f695d6588bc06fdd40e039cd1e5b5e169dd Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 26 Jun 2024 19:58:56 -0400
Subject: [PATCH 122/315] Update test ref (#3882)

* Update ref

* Disable some tests
---
 .github/workflows/test-browser.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml
index 987a72ae3..65483b00f 100644
--- a/.github/workflows/test-browser.yml
+++ b/.github/workflows/test-browser.yml
@@ -26,7 +26,7 @@ jobs:
       with:
         repository: "huchenlei/ComfyUI_frontend"
         path: "ComfyUI_frontend"
-        ref: "577aea9dfb680a14b3ae4fa626a85098c3207030"
+        ref: "fcc54d803e5b6a9b08a462a1d94899318c96dcbb"
     - uses: actions/setup-node@v3
       with:
         node-version: lts/*

From bc5a0f10db34a5c942fa3c531864da9665faaaa5 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 26 Jun 2024 19:59:09 -0400
Subject: [PATCH 123/315] Ignore *.log (#3880)

---
 .gitignore | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 9f0389241..9473f43ab 100644
--- a/.gitignore
+++ b/.gitignore
@@ -15,4 +15,5 @@ venv/
 !/web/extensions/logging.js.example
 !/web/extensions/core/
 /tests-ui/data/object_info.json
-/user/
\ No newline at end of file
+/user/
+*.log
\ No newline at end of file

From 175fe02522751c9f5e9a8309b3aaa5d07a542296 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 26 Jun 2024 19:59:19 -0400
Subject: [PATCH 124/315] Ignore .vscode/ (#3879)

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 9473f43ab..a9beebe73 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,6 +9,7 @@ __pycache__/
 !custom_nodes/example_node.py.example
 extra_model_paths.yaml
 /.vs
+.vscode/
 .idea/
 venv/
 /web/extensions/*

From 44947e7ad489eaadb38007ca7aabd71aef7fa10e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 26 Jun 2024 22:40:05 -0400
Subject: [PATCH 125/315] Add DEIS order 3 sampler.

Order 4 seems to give bad results.
---
 comfy/k_diffusion/deis.py     | 122 ++++++++++++++++++++++++++++++++++
 comfy/k_diffusion/sampling.py |  50 ++++++++++++++
 comfy/samplers.py             |   2 +-
 3 files changed, 173 insertions(+), 1 deletion(-)
 create mode 100644 comfy/k_diffusion/deis.py

diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py
new file mode 100644
index 000000000..0be2036c3
--- /dev/null
+++ b/comfy/k_diffusion/deis.py
@@ -0,0 +1,122 @@
+#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py
+#under Apache 2 license
+import torch
+import numpy as np
+
+# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis).
+#############################
+### Utils for DEIS solver ###
+#############################
+#----------------------------------------------------------------------------
+# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS.
+
+def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80):
+    vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t ** 2) + beta_min * t) - 1) ** 0.5
+    vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d
+    vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1)
+    vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d
+    t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu())
+    return t_steps, vp_beta_min, vp_beta_d + vp_beta_min
+
+#----------------------------------------------------------------------------
+
+def cal_poly(prev_t, j, taus):
+    poly = 1
+    for k in range(prev_t.shape[0]):
+        if k == j:
+            continue
+        poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k])
+    return poly
+
+#----------------------------------------------------------------------------
+# Transfer from t to alpha_t.
+
+def t2alpha_fn(beta_0, beta_1, t):
+    return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0)
+
+#----------------------------------------------------------------------------
+
+def cal_intergrand(beta_0, beta_1, taus):
+    with torch.inference_mode(mode=False):
+        taus = taus.clone()
+        beta_0 = beta_0.clone()
+        beta_1 = beta_1.clone()
+        with torch.enable_grad():
+            taus.requires_grad_(True)
+            alpha = t2alpha_fn(beta_0, beta_1, taus)
+            log_alpha = alpha.log()
+            log_alpha.sum().backward()
+            d_log_alpha_dtau = taus.grad
+    integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha))
+    return integrand
+
+#----------------------------------------------------------------------------
+
+def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):
+    """
+    Get the coefficient list for DEIS sampling.
+
+    Args:
+        t_steps: A pytorch tensor. The time steps for sampling.
+        max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4
+        N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'.
+        deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS.
+    Returns:
+        A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True.
+    """
+    if deis_mode == 'tab':
+        t_steps, beta_0, beta_1 = edm2t(t_steps)
+        C = []
+        for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+            order = min(i+1, max_order)
+            if order == 1:
+                C.append([])
+            else:
+                taus = torch.linspace(t_cur, t_next, N)   # split the interval for integral appximation
+                dtau = (t_next - t_cur) / N
+                prev_t = t_steps[[i - k for k in range(order)]]
+                coeff_temp = []
+                integrand = cal_intergrand(beta_0, beta_1, taus)
+                for j in range(order):
+                    poly = cal_poly(prev_t, j, taus)
+                    coeff_temp.append(torch.sum(integrand * poly) * dtau)
+                C.append(coeff_temp)
+
+    elif deis_mode == 'rhoab':
+        # Analytical solution, second order
+        def get_def_intergral_2(a, b, start, end, c):
+            coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b
+            return coeff / ((c - a) * (c - b))
+
+        # Analytical solution, third order
+        def get_def_intergral_3(a, b, c, start, end, d):
+            coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \
+                    + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c
+            return coeff / ((d - a) * (d - b) * (d - c))
+
+        C = []
+        for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+            order = min(i, max_order)
+            if order == 0:
+                C.append([])
+            else:
+                prev_t = t_steps[[i - k for k in range(order+1)]]
+                if order == 1:
+                    coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1]))
+                    coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur))
+                    coeff_temp = [coeff_cur, coeff_prev1]
+                elif order == 2:
+                    coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur)
+                    coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1])
+                    coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2])
+                    coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2]
+                elif order == 3:
+                    coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur)
+                    coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1])
+                    coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2])
+                    coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3])
+                    coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3]
+                C.append(coeff_temp)
+    print(C)
+    return C
+
diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 9b58cb1dc..f8091bb3f 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -7,6 +7,7 @@ import torchsde
 from tqdm.auto import trange, tqdm
 
 from . import utils
+from . import deis
 import comfy.model_patcher
 
 def append_zero(x):
@@ -946,6 +947,55 @@ def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=Non
 
     return x_next
 
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+@torch.no_grad()
+def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    x_next = x
+    t_steps = sigmas
+
+    coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode)
+
+    buffer_model = []
+    for i in trange(len(sigmas) - 1, disable=disable):
+        t_cur = sigmas[i]
+        t_next = sigmas[i + 1]
+
+        x_cur = x_next
+
+        denoised = model(x_cur, t_cur * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        d_cur = (x_cur - denoised) / t_cur
+
+        order = min(max_order, i+1)
+        if t_next <= 0:
+            order = 1
+
+        if order == 1:          # First Euler step.
+            x_next = x_cur + (t_next - t_cur) * d_cur
+        elif order == 2:        # Use one history point.
+            coeff_cur, coeff_prev1 = coeff_list[i]
+            x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1]
+        elif order == 3:        # Use two history points.
+            coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i]
+            x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2]
+        elif order == 4:        # Use three history points.
+            coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i]
+            x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3]
+
+        if len(buffer_model) == max_order - 1:
+            for k in range(max_order - 2):
+                buffer_model[k] = buffer_model[k+1]
+            buffer_model[-1] = d_cur.detach()
+        else:
+            buffer_model.append(d_cur.detach())
+
+    return x_next
 
 @torch.no_grad()
 def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
diff --git a/comfy/samplers.py b/comfy/samplers.py
index c3a80dde0..7f7114dbb 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -540,7 +540,7 @@ class Sampler:
 KSAMPLER_NAMES = ["euler", "euler_pp", "euler_ancestral", "euler_ancestral_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
-                  "ipndm", "ipndm_v"]
+                  "ipndm", "ipndm_v", "deis"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

From 3b423afccab877b57fc24141690426ca1660e4bb Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Thu, 27 Jun 2024 00:22:55 -0400
Subject: [PATCH 126/315] Add audio widget (#3863)

* Add audio widget

* Fix audio bugs

* Add CSS

* Populate audio widget when load history
---
 comfy_extras/nodes_audio.py        |  11 +-
 web/extensions/core/uploadAudio.js | 176 +++++++++++++++++++++++++++++
 web/scripts/app.js                 |  11 +-
 web/style.css                      |   4 +
 4 files changed, 199 insertions(+), 3 deletions(-)
 create mode 100644 web/extensions/core/uploadAudio.js

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 57d0a20ab..8efa7c0a8 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -93,11 +93,18 @@ class SaveAudio:
         return { "ui": { "audio": results } }
 
 class LoadAudio:
+    SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif')
+
     @classmethod
     def INPUT_TYPES(s):
         input_dir = folder_paths.get_input_directory()
-        files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
-        return {"required": {"audio": [sorted(files), ]}, }
+        files = [
+            f for f in os.listdir(input_dir)
+            if (os.path.isfile(os.path.join(input_dir, f))
+                and f.endswith(LoadAudio.SUPPORTED_FORMATS)
+            )
+        ]
+        return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
 
     CATEGORY = "_for_testing/audio"
 
diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js
new file mode 100644
index 000000000..d3dfef4a7
--- /dev/null
+++ b/web/extensions/core/uploadAudio.js
@@ -0,0 +1,176 @@
+import { app } from "../../scripts/app.js"
+import { api } from "../../scripts/api.js"
+
+function splitFilePath(path) {
+  const folder_separator = path.lastIndexOf("/")
+  if (folder_separator === -1) {
+    return ["", path]
+  }
+  return [
+    path.substring(0, folder_separator),
+    path.substring(folder_separator + 1)
+  ]
+}
+
+function getResourceURL(subfolder, filename, type = "input") {
+  const params = [
+    "filename=" + encodeURIComponent(filename),
+    "type=" + type,
+    "subfolder=" + subfolder,
+    app.getPreviewFormatParam().substring(1),
+    app.getRandParam().substring(1)
+  ].join("&")
+
+  return `/view?${params}`
+}
+
+async function uploadFile(
+  audioWidget,
+  audioUIWidget,
+  file,
+  updateNode,
+  pasted = false
+) {
+  try {
+    // Wrap file in formdata so it includes filename
+    const body = new FormData()
+    body.append("image", file)
+    if (pasted) body.append("subfolder", "pasted")
+    const resp = await api.fetchApi("/upload/image", {
+      method: "POST",
+      body
+    })
+
+    if (resp.status === 200) {
+      const data = await resp.json()
+      // Add the file to the dropdown list and update the widget value
+      let path = data.name
+      if (data.subfolder) path = data.subfolder + "/" + path
+
+      if (!audioWidget.options.values.includes(path)) {
+        audioWidget.options.values.push(path)
+      }
+
+      if (updateNode) {
+        audioUIWidget.element.src = api.apiURL(
+          getResourceURL(...splitFilePath(path))
+        )
+        audioWidget.value = path
+      }
+    } else {
+      alert(resp.status + " - " + resp.statusText)
+    }
+  } catch (error) {
+    alert(error)
+  }
+}
+
+// AudioWidget MUST be registered first, as AUDIOUPLOAD depends on AUDIO_UI to be
+// present.
+app.registerExtension({
+  name: "Comfy.AudioWidget",
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (["LoadAudio", "SaveAudio"].includes(nodeType.comfyClass)) {
+      nodeData.input.required.audioUI = ["AUDIO_UI"]
+    }
+  },
+  getCustomWidgets() {
+    return {
+      AUDIO_UI(node, inputName) {
+        const audio = document.createElement("audio")
+        audio.controls = true
+        audio.classList.add("comfy-audio")
+        audio.setAttribute("name", "media")
+
+        const audioUIWidget = node.addDOMWidget(
+          inputName,
+          /* name=*/ "audioUI",
+          audio
+        )
+        // @ts-ignore
+        // TODO: Sort out the DOMWidget type.
+        audioUIWidget.serialize = false
+
+        const isOutputNode = node.constructor.nodeData.output_node
+        if (isOutputNode) {
+          // Hide the audio widget when there is no audio initially.
+          audioUIWidget.element.classList.add("empty-audio-widget")
+          // Populate the audio widget UI on node execution.
+          const onExecuted = node.onExecuted
+          node.onExecuted = function(message) {
+            onExecuted?.apply(this, arguments)
+            const audios = message.audio
+            if (!audios) return
+            const audio = audios[0]
+            audioUIWidget.element.src = api.apiURL(
+              getResourceURL(audio.subfolder, audio.filename, "output")
+            )
+            audioUIWidget.element.classList.remove("empty-audio-widget")
+          }
+        }
+        return { widget: audioUIWidget }
+      }
+    }
+  },
+  onNodeOutputsUpdated(nodeOutputs) {
+    for (const [nodeId, output] of Object.entries(nodeOutputs)) {
+      const node = app.graph.getNodeById(Number.parseInt(nodeId));
+      if ("audio" in output) {
+        const audioUIWidget = node.widgets.find((w) => w.name === "audioUI");
+        const audio = output.audio[0];
+        audioUIWidget.element.src = api.apiURL(getResourceURL(audio.subfolder, audio.filename, "output"));
+        audioUIWidget.element.classList.remove("empty-audio-widget");
+      }
+    }
+  },
+})
+
+app.registerExtension({
+  name: "Comfy.UploadAudio",
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.input?.required?.audio?.[1]?.audio_upload === true) {
+      nodeData.input.required.upload = ["AUDIOUPLOAD"]
+    }
+  },
+  getCustomWidgets() {
+    return {
+      AUDIOUPLOAD(node, inputName) {
+        // The widget that allows user to select file.
+        const audioWidget = node.widgets.find(w => w.name === "audio")
+        const audioUIWidget = node.widgets.find(w => w.name === "audioUI")
+
+        const onAudioWidgetUpdate = () => {
+          audioUIWidget.element.src = api.apiURL(
+            getResourceURL(...splitFilePath(audioWidget.value))
+          )
+        }
+        // Initially load default audio file to audioUIWidget.
+        onAudioWidgetUpdate()
+        audioWidget.callback = onAudioWidgetUpdate
+
+        const fileInput = document.createElement("input")
+        fileInput.type = "file"
+        fileInput.accept = "audio/*"
+        fileInput.style.display = "none"
+        fileInput.onchange = () => {
+          if (fileInput.files.length) {
+            uploadFile(audioWidget, audioUIWidget, fileInput.files[0], true)
+          }
+        }
+        // The widget to pop up the upload dialog.
+        const uploadWidget = node.addWidget(
+          "button",
+          inputName,
+          /* value=*/ "",
+          () => {
+            fileInput.click()
+          }
+        )
+        uploadWidget.label = "choose file to upload"
+        uploadWidget.serialize = false
+
+        return { widget: uploadWidget }
+      }
+    }
+  }
+})
diff --git a/web/scripts/app.js b/web/scripts/app.js
index a354ff805..5fc9b9c6c 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -71,7 +71,7 @@ export class ComfyApp {
 		 * Stores the execution output data for each node
 		 * @type {Record<string, any>}
 		 */
-		this.nodeOutputs = {};
+		this._nodeOutputs = {};
 
 		/**
 		 * Stores the preview image data for each node
@@ -86,6 +86,15 @@ export class ComfyApp {
 		this.shiftDown = false;
 	}
 
+	get nodeOutputs() {
+		return this._nodeOutputs;
+	}
+
+	set nodeOutputs(value) {
+		this._nodeOutputs = value;
+		this.#invokeExtensions("onNodeOutputsUpdated", value);
+	}
+
 	getPreviewFormatParam() {
 		let preview_format = this.ui.settings.getSettingValue("Comfy.PreviewFormat");
 		if(preview_format)
diff --git a/web/style.css b/web/style.css
index 8091a489f..e983b652a 100644
--- a/web/style.css
+++ b/web/style.css
@@ -632,3 +632,7 @@ dialog::backdrop {
 		border-top: none;
 	}
 }
+
+audio.comfy-audio.empty-audio-widget {
+	display: none;
+}

From 4650e7d6e9e846160906b94d575705151d59fdd1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 27 Jun 2024 01:28:59 -0400
Subject: [PATCH 127/315] Save and load workflow from the flac files output by
 SaveAudio.

---
 comfy_extras/nodes_audio.py | 79 ++++++++++++++++++++++++++++++++++++-
 web/scripts/app.js          | 15 ++++++-
 web/scripts/pnginfo.js      | 72 +++++++++++++++++++++++++++++++++
 web/scripts/ui.js           |  2 +-
 4 files changed, 164 insertions(+), 4 deletions(-)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 8efa7c0a8..34bcfa96d 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -3,6 +3,10 @@ import torch
 import comfy.model_management
 import folder_paths
 import os
+import io
+import json
+import struct
+from comfy.cli_args import args
 
 class EmptyLatentAudio:
     def __init__(self):
@@ -53,6 +57,61 @@ class VAEDecodeAudio:
         audio = vae.decode(samples["samples"]).movedim(-1, 1)
         return ({"waveform": audio, "sample_rate": 44100}, )
 
+
+def create_vorbis_comment_block(comment_dict, last_block):
+    vendor_string = b'ComfyUI'
+    vendor_length = len(vendor_string)
+
+    comments = []
+    for key, value in comment_dict.items():
+        comment = f"{key}={value}".encode('utf-8')
+        comments.append(struct.pack('<I', len(comment)) + comment)
+
+    user_comment_list_length = len(comments)
+    user_comments = b''.join(comments)
+
+    comment_data = struct.pack('<I', vendor_length) + vendor_string + struct.pack('<I', user_comment_list_length) + user_comments
+    if last_block:
+        id = b'\x84'
+    else:
+        id = b'\x04'
+    comment_block = id + struct.pack('>I', len(comment_data))[1:] + comment_data
+
+    return comment_block
+
+def insert_or_replace_vorbis_comment(flac_io, comment_dict):
+    if len(comment_dict) == 0:
+        return flac_io
+
+    flac_io.seek(4)
+
+    blocks = []
+    last_block = False
+
+    while not last_block:
+        header = flac_io.read(4)
+        last_block = (header[0] & 0x80) != 0
+        block_type = header[0] & 0x7F
+        block_length = struct.unpack('>I', b'\x00' + header[1:])[0]
+        block_data = flac_io.read(block_length)
+
+        if block_type == 4 or block_type == 1:
+            pass
+        else:
+            header = bytes([(header[0] & (~0x80))]) + header[1:]
+            blocks.append(header + block_data)
+
+    blocks.append(create_vorbis_comment_block(comment_dict, last_block=True))
+
+    new_flac_io = io.BytesIO()
+    new_flac_io.write(b'fLaC')
+    for block in blocks:
+        new_flac_io.write(block)
+
+    new_flac_io.write(flac_io.read())
+    return new_flac_io
+
+
 class SaveAudio:
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
@@ -78,11 +137,27 @@ class SaveAudio:
         filename_prefix += self.prefix_append
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
         results = list()
+
+        metadata = {}
+        if not args.disable_metadata:
+            if prompt is not None:
+                metadata["prompt"] = json.dumps(prompt)
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata[x] = json.dumps(extra_pnginfo[x])
+
         for (batch_number, waveform) in enumerate(audio["waveform"]):
-            #TODO: metadata
             filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
             file = f"{filename_with_batch_num}_{counter:05}_.flac"
-            torchaudio.save(os.path.join(full_output_folder, file), waveform, audio["sample_rate"], format="FLAC")
+
+            buff = io.BytesIO()
+            torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC")
+
+            buff = insert_or_replace_vorbis_comment(buff, metadata)
+
+            with open(os.path.join(full_output_folder, file), 'wb') as f:
+                f.write(buff.getbuffer())
+
             results.append({
                 "filename": file,
                 "subfolder": subfolder,
diff --git a/web/scripts/app.js b/web/scripts/app.js
index 5fc9b9c6c..43df61065 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -3,7 +3,7 @@ import { ComfyWidgets, initWidgets } from "./widgets.js";
 import { ComfyUI, $el } from "./ui.js";
 import { api } from "./api.js";
 import { defaultGraph } from "./defaultGraph.js";
-import { getPngMetadata, getWebpMetadata, importA1111, getLatentMetadata } from "./pnginfo.js";
+import { getPngMetadata, getWebpMetadata, getFlacMetadata, importA1111, getLatentMetadata } from "./pnginfo.js";
 import { addDomClippingSetting } from "./domWidget.js";
 import { createImageHost, calculateImageGrid } from "./ui/imagePreview.js";
 import { ComfyAppMenu } from "./ui/menu/index.js";
@@ -2277,6 +2277,19 @@ export class ComfyApp {
 			const workflow = pngInfo?.workflow || pngInfo?.Workflow;
 			const prompt = pngInfo?.prompt || pngInfo?.Prompt;
 
+			if (workflow) {
+				this.loadGraphData(JSON.parse(workflow), true, true, fileName);
+			} else if (prompt) {
+				this.loadApiJson(JSON.parse(prompt), fileName);
+			} else {
+				this.showErrorOnFileLoad(file);
+			}
+		} else if (file.type === "audio/flac") {
+			const pngInfo = await getFlacMetadata(file);
+			// Support loading workflows from that webp custom node.
+			const workflow = pngInfo?.workflow;
+			const prompt = pngInfo?.prompt;
+
 			if (workflow) {
 				this.loadGraphData(JSON.parse(workflow), true, true, fileName);
 			} else if (prompt) {
diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js
index 7132fb60f..2c03cf74a 100644
--- a/web/scripts/pnginfo.js
+++ b/web/scripts/pnginfo.js
@@ -163,6 +163,78 @@ export function getLatentMetadata(file) {
 	});
 }
 
+
+function getString(dataView, offset, length) {
+	let string = '';
+	for (let i = 0; i < length; i++) {
+		string += String.fromCharCode(dataView.getUint8(offset + i));
+	}
+	return string;
+}
+
+// Function to parse the Vorbis Comment block
+function parseVorbisComment(dataView) {
+	let offset = 0;
+	const vendorLength = dataView.getUint32(offset, true);
+	offset += 4;
+	const vendorString = getString(dataView, offset, vendorLength);
+	offset += vendorLength;
+
+	const userCommentListLength = dataView.getUint32(offset, true);
+	offset += 4;
+	const comments = {};
+	for (let i = 0; i < userCommentListLength; i++) {
+		const commentLength = dataView.getUint32(offset, true);
+		offset += 4;
+		const comment = getString(dataView, offset, commentLength);
+		offset += commentLength;
+
+		const [key, value] = comment.split('=');
+
+		comments[key] = value;
+	}
+
+	return comments;
+}
+
+// Function to read a FLAC file and parse Vorbis comments
+export function getFlacMetadata(file) {
+	return new Promise((r) => {
+		const reader = new FileReader();
+		reader.onload = function(event) {
+			const arrayBuffer = event.target.result;
+			const dataView = new DataView(arrayBuffer);
+
+			// Verify the FLAC signature
+			const signature = String.fromCharCode(...new Uint8Array(arrayBuffer, 0, 4));
+			if (signature !== 'fLaC') {
+				console.error('Not a valid FLAC file');
+				return;
+			}
+
+			// Parse metadata blocks
+			let offset = 4;
+			let vorbisComment = null;
+			while (offset < dataView.byteLength) {
+				const isLastBlock = dataView.getUint8(offset) & 0x80;
+				const blockType = dataView.getUint8(offset) & 0x7F;
+				const blockSize = dataView.getUint32(offset, false) & 0xFFFFFF;
+				offset += 4;
+
+				if (blockType === 4) { // Vorbis Comment block type
+					vorbisComment = parseVorbisComment(new DataView(arrayBuffer, offset, blockSize));
+				}
+
+				offset += blockSize;
+				if (isLastBlock) break;
+			}
+
+			r(vorbisComment);
+		};
+		reader.readAsArrayBuffer(file);
+	});
+}
+
 export async function importA1111(graph, parameters) {
 	const p = parameters.lastIndexOf("\nSteps:");
 	if (p > -1) {
diff --git a/web/scripts/ui.js b/web/scripts/ui.js
index bb094d8b4..2c47412c9 100644
--- a/web/scripts/ui.js
+++ b/web/scripts/ui.js
@@ -373,7 +373,7 @@ export class ComfyUI {
 		const fileInput = $el("input", {
 			id: "comfy-file-input",
 			type: "file",
-			accept: ".json,image/png,.latent,.safetensors,image/webp",
+			accept: ".json,image/png,.latent,.safetensors,image/webp,audio/flac",
 			style: {display: "none"},
 			parent: document.body,
 			onchange: () => {

From 4f9d2b057c97be22aa46b91ed8954a3cee424fb4 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 27 Jun 2024 02:54:15 -0400
Subject: [PATCH 128/315] Remove print.

---
 comfy/k_diffusion/deis.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py
index 0be2036c3..607410656 100644
--- a/comfy/k_diffusion/deis.py
+++ b/comfy/k_diffusion/deis.py
@@ -117,6 +117,5 @@ def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):
                     coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3])
                     coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3]
                 C.append(coeff_temp)
-    print(C)
     return C
 

From 5ff3d4eb3a91d1e66d35a919dd38c02b4652e96a Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Thu, 27 Jun 2024 09:13:52 -0400
Subject: [PATCH 129/315] Fix audio upload when no audio in input dir (#3891)

---
 web/extensions/core/uploadAudio.js | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js
index d3dfef4a7..12a2a196b 100644
--- a/web/extensions/core/uploadAudio.js
+++ b/web/extensions/core/uploadAudio.js
@@ -145,7 +145,9 @@ app.registerExtension({
           )
         }
         // Initially load default audio file to audioUIWidget.
-        onAudioWidgetUpdate()
+        if (audioWidget.value) {
+          onAudioWidgetUpdate()
+        }
         audioWidget.callback = onAudioWidgetUpdate
 
         const fileInput = document.createElement("input")

From 8ceb5a02a3182e6551690d9e56934bdca1cebcca Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 27 Jun 2024 11:06:52 -0400
Subject: [PATCH 130/315] Support saving stable audio checkpoint that can be
 loaded back.

---
 comfy/model_base.py       | 9 +++++++++
 comfy/sd.py               | 2 +-
 comfy/supported_models.py | 5 ++++-
 3 files changed, 14 insertions(+), 2 deletions(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index f45b375de..80f6667ec 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -627,3 +627,12 @@ class StableAudio1(BaseModel):
             cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
+
+    def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
+        sd = super().state_dict_for_saving(clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict)
+        d = {"conditioner.conditioners.seconds_start.": self.seconds_start_embedder.state_dict(), "conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()}
+        for k in d:
+            s = d[k]
+            for l in s:
+                sd["{}{}".format(k, l)] = s[l]
+        return sd
diff --git a/comfy/sd.py b/comfy/sd.py
index ea6e9b663..dda0887ba 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -236,7 +236,7 @@ class VAE:
                     self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
                                                                 encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
                                                                 decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
-            elif "decoder.layers.0.weight_v" in sd:
+            elif "decoder.layers.1.layers.0.beta" in sd:
                 self.first_stage_model = AudioOobleckVAE()
                 self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
                 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 761498dbc..21fdb7ec7 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -543,13 +543,16 @@ class StableAudio(supported_models_base.BASE):
         seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
         return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)
 
-
     def process_unet_state_dict(self, state_dict):
         for k in list(state_dict.keys()):
             if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero
                 state_dict.pop(k)
         return state_dict
 
+    def process_unet_state_dict_for_saving(self, state_dict):
+        replace_prefix = {"": "model.model."}
+        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
     def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
 

From 449bf52923896236ed645da2ff05aadbf2b0d536 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Thu, 27 Jun 2024 13:08:26 -0700
Subject: [PATCH 131/315] Add integration test for Linux with Nvidia GPU. #3884
 (#3895)

* Add linux integration test.

* Fix directory path.

* Add paths ignore.

* Fix conda env directory path.
---
 .../conda-environments/linux-environment.yml  | 28 +++++++++++
 .github/workflows/linux-integration-test.yml  | 50 +++++++++++++++++++
 2 files changed, 78 insertions(+)
 create mode 100644 .github/conda-environments/linux-environment.yml
 create mode 100644 .github/workflows/linux-integration-test.yml

diff --git a/.github/conda-environments/linux-environment.yml b/.github/conda-environments/linux-environment.yml
new file mode 100644
index 000000000..c9ebd6406
--- /dev/null
+++ b/.github/conda-environments/linux-environment.yml
@@ -0,0 +1,28 @@
+name: comfyui
+channels:
+  - pytorch
+  - nvidia
+  - defaults
+dependencies:
+  - python>=3.9
+  - pip
+  - pytorch
+  - torchvision
+  - torchaudio
+  - pytorch-cuda=12.1
+  - pip:
+    # comfyui requirements
+    - einops
+    - transformers>=4.25.1
+    - safetensors>=0.4.2
+    - aiohttp
+    - pyyaml
+    - Pillow
+    - scipy
+    - tqdm
+    - psutil
+    # comfy-action requirements
+    - requests
+    - google-cloud-storage
+    - comfy-cli
+    - charset-normalizer
\ No newline at end of file
diff --git a/.github/workflows/linux-integration-test.yml b/.github/workflows/linux-integration-test.yml
new file mode 100644
index 000000000..eed84a77c
--- /dev/null
+++ b/.github/workflows/linux-integration-test.yml
@@ -0,0 +1,50 @@
+name: (Linux) ComfyUI Integration Tests
+on:
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'app/**'
+      - 'input/**'
+      - 'output/**'
+      - 'model/**'
+      - 'notebook/**'
+      - 'script_example/**'
+      - 'tests/**'
+      - 'tests-ui/**'
+      - '.github/**'
+      - '.ci/**'
+      - 'web/**'
+  workflow_dispatch:
+  pull_request:
+    branches:
+      - master
+    paths-ignore:
+      - 'app/**'
+      - 'input/**'
+      - 'output/**'
+      - 'model/**'
+      - 'notebook/**'
+      - 'script_example/**'
+      - 'tests/**'
+      - 'tests-ui/**'
+      - '.github/**'
+      - '.ci/**'
+      - 'web/**'
+
+jobs:
+  test-workflows:
+    runs-on: [self-hosted, Linux, t4] 
+    steps:
+    - name: Test ComfyUI Workflows
+      uses: comfy-org/comfy-action@main
+      with:
+        os: linux
+        cuda_version: 12.1
+        models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}'
+        workflow_filenames: "default.json"
+        gcs_bucket_name: 'comfy-ci-results'
+        google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+        output_prefix: 'ComfyUI'
+        conda_env_file: '.github/conda-environments/linux-environment.yml'
+        timeout: 50

From 97b409cd486b1a3b1114ae204802c81f3727d1a2 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Thu, 27 Jun 2024 13:10:16 -0700
Subject: [PATCH 132/315] Add macOs integration test for default workflow.
 (#3898)

---
 .../conda-environments/mac-environment.yml    | 26 ++++++++++
 .github/workflows/mac-integration-test.yml    | 50 +++++++++++++++++++
 2 files changed, 76 insertions(+)
 create mode 100644 .github/conda-environments/mac-environment.yml
 create mode 100644 .github/workflows/mac-integration-test.yml

diff --git a/.github/conda-environments/mac-environment.yml b/.github/conda-environments/mac-environment.yml
new file mode 100644
index 000000000..33832b854
--- /dev/null
+++ b/.github/conda-environments/mac-environment.yml
@@ -0,0 +1,26 @@
+name: comfyui
+channels:
+  - pytorch-nightly
+  - defaults
+dependencies:
+  - python>=3.9
+  - pytorch-nightly::pytorch
+  - torchvision
+  - torchaudio
+  - pip:
+    - pip
+    # comfyui requirements
+    - einops
+    - transformers>=4.25.1
+    - safetensors>=0.4.2
+    - aiohttp
+    - pyyaml
+    - Pillow
+    - scipy
+    - tqdm
+    - psutil
+    # comfy-action requirements
+    - requests
+    - google-cloud-storage
+    - comfy-cli
+    - charset-normalizer
diff --git a/.github/workflows/mac-integration-test.yml b/.github/workflows/mac-integration-test.yml
new file mode 100644
index 000000000..6a0482b1b
--- /dev/null
+++ b/.github/workflows/mac-integration-test.yml
@@ -0,0 +1,50 @@
+name: (macOS) ComfyUI Integration Tests
+on:
+  push:
+    branches:
+      - master
+    paths-ignore:
+      - 'app/**'
+      - 'input/**'
+      - 'output/**'
+      - 'model/**'
+      - 'notebook/**'
+      - 'script_example/**'
+      - 'tests/**'
+      - 'tests-ui/**'
+      - '.github/**'
+      - '.ci/**'
+      - 'web/**'
+  workflow_dispatch:
+  pull_request:
+    branches:
+      - master
+    paths-ignore:
+      - 'app/**'
+      - 'input/**'
+      - 'output/**'
+      - 'model/**'
+      - 'notebook/**'
+      - 'script_example/**'
+      - 'tests/**'
+      - 'tests-ui/**'
+      - '.github/**'
+      - '.ci/**'
+      - 'web/**'
+
+jobs:
+  test-workflows:
+    runs-on: [self-hosted, m2] 
+    steps:
+    - name: Test ComfyUI Workflows
+      uses: comfy-org/comfy-action@main
+      with:
+        os: mac
+        models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}'
+        workflow_filenames: "default.json"
+        gcs_bucket_name: 'comfy-ci-results'
+        google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
+        output_prefix: 'ComfyUI'
+        conda_env_file: '.github/conda-environments/mac-environment.yml'
+        timeout: 50
+

From 66aaa14001be9f7cf2b52f84c0dff588e36aabbf Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 25 Jun 2024 17:02:05 -0400
Subject: [PATCH 133/315] Controlnet refactor.

---
 comfy/cldm/cldm.py              |  9 +++++----
 comfy/controlnet.py             | 35 ++++++++++-----------------------
 comfy/ldm/cascade/controlnet.py |  2 +-
 comfy/t2i_adapter/adapter.py    | 10 ++++++++--
 4 files changed, 24 insertions(+), 32 deletions(-)

diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index 28076dd92..f8a161594 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -289,7 +289,8 @@ class ControlNet(nn.Module):
 
         guided_hint = self.input_hint_block(hint, emb, context)
 
-        outs = []
+        out_output = []
+        out_middle = []
 
         hs = []
         if self.num_classes is not None:
@@ -304,10 +305,10 @@ class ControlNet(nn.Module):
                 guided_hint = None
             else:
                 h = module(h, emb, context)
-            outs.append(zero_conv(h, emb, context))
+            out_output.append(zero_conv(h, emb, context))
 
         h = self.middle_block(h, emb, context)
-        outs.append(self.middle_block_out(h, emb, context))
+        out_middle.append(self.middle_block_out(h, emb, context))
 
-        return outs
+        return {"middle": out_middle, "output": out_output}
 
diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index 8cf4a61a6..f50df6835 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -89,27 +89,12 @@ class ControlBase:
             return self.previous_controlnet.inference_memory_requirements(dtype)
         return 0
 
-    def control_merge(self, control_input, control_output, control_prev, output_dtype):
+    def control_merge(self, control, control_prev, output_dtype):
         out = {'input':[], 'middle':[], 'output': []}
 
-        if control_input is not None:
-            for i in range(len(control_input)):
-                key = 'input'
-                x = control_input[i]
-                if x is not None:
-                    x *= self.strength
-                    if x.dtype != output_dtype:
-                        x = x.to(output_dtype)
-                out[key].insert(0, x)
-
-        if control_output is not None:
+        for key in control:
+            control_output = control[key]
             for i in range(len(control_output)):
-                if i == (len(control_output) - 1):
-                    key = 'middle'
-                    index = 0
-                else:
-                    key = 'output'
-                    index = i
                 x = control_output[i]
                 if x is not None:
                     if self.global_average_pooling:
@@ -120,6 +105,7 @@ class ControlBase:
                         x = x.to(output_dtype)
 
                 out[key].append(x)
+
         if control_prev is not None:
             for x in ['input', 'middle', 'output']:
                 o = out[x]
@@ -182,7 +168,7 @@ class ControlNet(ControlBase):
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
         control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
-        return self.control_merge(None, control, control_prev, output_dtype)
+        return self.control_merge(control, control_prev, output_dtype)
 
     def copy(self):
         c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
@@ -490,12 +476,11 @@ class T2IAdapter(ControlBase):
             self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
             self.t2i_model.cpu()
 
-        control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
-        mid = None
-        if self.t2i_model.xl == True:
-            mid = control_input[-1:]
-            control_input = control_input[:-1]
-        return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
+        control_input = {}
+        for k in self.control_input:
+            control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k]))
+
+        return self.control_merge(control_input, control_prev, x_noisy.dtype)
 
     def copy(self):
         c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)
diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py
index 5dac59394..7a52c3c26 100644
--- a/comfy/ldm/cascade/controlnet.py
+++ b/comfy/ldm/cascade/controlnet.py
@@ -90,4 +90,4 @@ class ControlNet(nn.Module):
         proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)]
         for i, idx in enumerate(self.proj_blocks):
             proj_outputs[idx] = self.projections[i](x)
-        return proj_outputs
+        return {"input": proj_outputs[::-1]}
diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py
index e9a606b1c..10ea18e32 100644
--- a/comfy/t2i_adapter/adapter.py
+++ b/comfy/t2i_adapter/adapter.py
@@ -153,7 +153,13 @@ class Adapter(nn.Module):
                 features.append(None)
             features.append(x)
 
-        return features
+        features = features[::-1]
+
+        if self.xl:
+            return {"input": features[1:], "middle": features[:1]}
+        else:
+            return {"input": features}
+
 
 
 class LayerNorm(nn.LayerNorm):
@@ -290,4 +296,4 @@ class Adapter_light(nn.Module):
             features.append(None)
             features.append(x)
 
-        return features
+        return {"input": features[::-1]}

From f8f7568d031a61f5d21ca0ed56ff56d7d83c9115 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 25 Jun 2024 23:40:44 -0400
Subject: [PATCH 134/315] Basic SD3 controlnet implementation.

Still missing the node to properly use it.
---
 comfy/cldm/mmdit.py                         | 91 +++++++++++++++++++++
 comfy/controlnet.py                         | 45 +++++++++-
 comfy/ldm/modules/diffusionmodules/mmdit.py | 30 +++++--
 comfy/model_detection.py                    | 11 ++-
 comfy/utils.py                              |  3 +-
 5 files changed, 165 insertions(+), 15 deletions(-)
 create mode 100644 comfy/cldm/mmdit.py

diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py
new file mode 100644
index 000000000..6e72474ce
--- /dev/null
+++ b/comfy/cldm/mmdit.py
@@ -0,0 +1,91 @@
+import torch
+from typing import Dict, Optional
+import comfy.ldm.modules.diffusionmodules.mmdit
+import comfy.latent_formats
+
+class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
+    def __init__(
+        self,
+        num_blocks = None,
+        dtype = None,
+        device = None,
+        operations = None,
+        **kwargs,
+    ):
+        super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs)
+        # controlnet_blocks
+        self.controlnet_blocks = torch.nn.ModuleList([])
+        for _ in range(len(self.joint_blocks)):
+            self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
+
+        self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
+            None,
+            self.patch_size,
+            self.in_channels,
+            self.hidden_size,
+            bias=True,
+            strict_img_size=False,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+
+        self.latent_format = comfy.latent_formats.SD3()
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: Optional[torch.Tensor] = None,
+        context: Optional[torch.Tensor] = None,
+        hint = None,
+    ) -> torch.Tensor:
+
+        #weird sd3 controlnet specific stuff
+        hint = hint * self.latent_format.scale_factor # self.latent_format.process_in(hint)
+        y = torch.zeros_like(y)
+
+
+        if self.context_processor is not None:
+            context = self.context_processor(context)
+
+        hw = x.shape[-2:]
+        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
+        x += self.pos_embed_input(hint)
+
+        c = self.t_embedder(timesteps, dtype=x.dtype)
+        if y is not None and self.y_embedder is not None:
+            y = self.y_embedder(y)
+            c = c + y
+
+        if context is not None:
+            context = self.context_embedder(context)
+
+        if self.register_length > 0:
+            context = torch.cat(
+                (
+                    repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
+                    default(context, torch.Tensor([]).type_as(x)),
+                ),
+                1,
+            )
+
+        output = []
+
+        blocks = len(self.joint_blocks)
+        for i in range(blocks):
+            context, x = self.joint_blocks[i](
+                context,
+                x,
+                c=c,
+                use_checkpoint=self.use_checkpoint,
+            )
+
+            out = self.controlnet_blocks[i](x)
+            count = self.depth // blocks
+            if i == blocks - 1:
+                count -= 1
+            for j in range(count):
+                output.append(out)
+
+        return {"output": output}
diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index f50df6835..9202c3194 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -11,6 +11,7 @@ import comfy.ops
 import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
 import comfy.ldm.cascade.controlnet
+import comfy.cldm.mmdit
 
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
@@ -94,13 +95,17 @@ class ControlBase:
 
         for key in control:
             control_output = control[key]
+            applied_to = set()
             for i in range(len(control_output)):
                 x = control_output[i]
                 if x is not None:
                     if self.global_average_pooling:
                         x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
 
-                    x *= self.strength
+                    if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once
+                        applied_to.add(x)
+                        x *= self.strength
+
                     if x.dtype != output_dtype:
                         x = x.to(output_dtype)
 
@@ -120,17 +125,18 @@ class ControlBase:
                             if o[i].shape[0] < prev_val.shape[0]:
                                 o[i] = prev_val + o[i]
                             else:
-                                o[i] += prev_val
+                                o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue
         return out
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, device=None, load_device=None, manual_cast_dtype=None):
         super().__init__(device)
         self.control_model = control_model
         self.load_device = load_device
         if control_model is not None:
             self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
 
+        self.compression_ratio = compression_ratio
         self.global_average_pooling = global_average_pooling
         self.model_sampling_current = None
         self.manual_cast_dtype = manual_cast_dtype
@@ -308,6 +314,37 @@ class ControlLora(ControlNet):
     def inference_memory_requirements(self, dtype):
         return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
 
+def load_controlnet_mmdit(sd):
+    new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
+    model_config = comfy.model_detection.model_config_from_unet(new_sd, "", True)
+    num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
+    for k in sd:
+        new_sd[k] = sd[k]
+
+    supported_inference_dtypes = model_config.supported_inference_dtypes
+
+    controlnet_config = model_config.unet_config
+    unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
+    load_device = comfy.model_management.get_torch_device()
+    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+    if manual_cast_dtype is not None:
+        operations = comfy.ops.manual_cast
+    else:
+        operations = comfy.ops.disable_weight_init
+
+    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=load_device, dtype=unet_dtype, **controlnet_config)
+    missing, unexpected = control_model.load_state_dict(new_sd, strict=False)
+
+    if len(missing) > 0:
+        logging.warning("missing controlnet keys: {}".format(missing))
+
+    if len(unexpected) > 0:
+        logging.debug("unexpected controlnet keys: {}".format(unexpected))
+
+    control = ControlNet(control_model, compression_ratio=1, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    return control
+
+
 def load_controlnet(ckpt_path, model=None):
     controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
     if "lora_controlnet" in controlnet_data:
@@ -360,6 +397,8 @@ def load_controlnet(ckpt_path, model=None):
         if len(leftover_keys) > 0:
             logging.warning("leftover keys: {}".format(leftover_keys))
         controlnet_data = new_sd
+    elif "controlnet_blocks.0.weight" in controlnet_data: #SD3 diffusers format
+        return load_controlnet_mmdit(controlnet_data)
 
     pth_key = 'control_model.zero_convs.0.0.weight'
     pth = False
diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
index 20d3a321a..927451534 100644
--- a/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -745,6 +745,8 @@ class MMDiT(nn.Module):
         qkv_bias: bool = True,
         context_processor_layers = None,
         context_size = 4096,
+        num_blocks = None,
+        final_layer = True,
         dtype = None, #TODO
         device = None,
         operations = None,
@@ -766,7 +768,10 @@ class MMDiT(nn.Module):
         # apply magic --> this defines a head_size of 64
         self.hidden_size = 64 * depth
         num_heads = depth
+        if num_blocks is None:
+            num_blocks = depth
 
+        self.depth = depth
         self.num_heads = num_heads
 
         self.x_embedder = PatchEmbed(
@@ -821,7 +826,7 @@ class MMDiT(nn.Module):
                     mlp_ratio=mlp_ratio,
                     qkv_bias=qkv_bias,
                     attn_mode=attn_mode,
-                    pre_only=i == depth - 1,
+                    pre_only=(i == num_blocks - 1) and final_layer,
                     rmsnorm=rmsnorm,
                     scale_mod_only=scale_mod_only,
                     swiglu=swiglu,
@@ -830,11 +835,12 @@ class MMDiT(nn.Module):
                     device=device,
                     operations=operations
                 )
-                for i in range(depth)
+                for i in range(num_blocks)
             ]
         )
 
-        self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+        if final_layer:
+            self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
 
         if compile_core:
             assert False
@@ -893,6 +899,7 @@ class MMDiT(nn.Module):
         x: torch.Tensor,
         c_mod: torch.Tensor,
         context: Optional[torch.Tensor] = None,
+        control = None,
     ) -> torch.Tensor:
         if self.register_length > 0:
             context = torch.cat(
@@ -905,13 +912,20 @@ class MMDiT(nn.Module):
 
         # context is B, L', D
         # x is B, L, D
-        for block in self.joint_blocks:
-            context, x = block(
+        blocks = len(self.joint_blocks)
+        for i in range(blocks):
+            context, x = self.joint_blocks[i](
                 context,
                 x,
                 c=c_mod,
                 use_checkpoint=self.use_checkpoint,
             )
+            if control is not None:
+                control_o = control.get("output")
+                if i < len(control_o):
+                    add = control_o[i]
+                    if add is not None:
+                        x += add
 
         x = self.final_layer(x, c_mod)  # (N, T, patch_size ** 2 * out_channels)
         return x
@@ -922,6 +936,7 @@ class MMDiT(nn.Module):
         t: torch.Tensor,
         y: Optional[torch.Tensor] = None,
         context: Optional[torch.Tensor] = None,
+        control = None,
     ) -> torch.Tensor:
         """
         Forward pass of DiT.
@@ -943,7 +958,7 @@ class MMDiT(nn.Module):
         if context is not None:
             context = self.context_embedder(context)
 
-        x = self.forward_core_with_concat(x, c, context)
+        x = self.forward_core_with_concat(x, c, context, control)
 
         x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
         return x[:,:,:hw[-2],:hw[-1]]
@@ -956,7 +971,8 @@ class OpenAISignatureMMDITWrapper(MMDiT):
         timesteps: torch.Tensor,
         context: Optional[torch.Tensor] = None,
         y: Optional[torch.Tensor] = None,
+        control = None,
         **kwargs,
     ) -> torch.Tensor:
-        return super().forward(x, timesteps, context=context, y=y)
+        return super().forward(x, timesteps, context=context, y=y, control=control)
 
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index e09dd381a..0b678480f 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -41,7 +41,9 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
         patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
         unet_config["patch_size"] = patch_size
-        unet_config["out_channels"] = state_dict['{}final_layer.linear.weight'.format(key_prefix)].shape[0] // (patch_size * patch_size)
+        final_layer = '{}final_layer.linear.weight'.format(key_prefix)
+        if final_layer in state_dict:
+            unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)
 
         unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
         unet_config["input_size"] = None
@@ -435,10 +437,11 @@ def model_config_from_diffusers_unet(state_dict):
     return None
 
 def convert_diffusers_mmdit(state_dict, output_prefix=""):
-    depth = count_blocks(state_dict, 'transformer_blocks.{}.')
-    if depth > 0:
+    num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
+    if num_blocks > 0:
+        depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
         out_sd = {}
-        sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth}, output_prefix=output_prefix)
+        sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
         for k in sd_map:
             weight = state_dict.get(k, None)
             if weight is not None:
diff --git a/comfy/utils.py b/comfy/utils.py
index ed6c58a64..48618e076 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -298,7 +298,8 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
     key_map = {}
 
     depth = mmdit_config.get("depth", 0)
-    for i in range(depth):
+    num_blocks = mmdit_config.get("num_blocks", depth)
+    for i in range(num_blocks):
         block_from = "transformer_blocks.{}".format(i)
         block_to = "{}joint_blocks.{}".format(output_prefix, i)
 

From 264caca20e1e051f3bcbec59d7209513ea99d4ed Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 26 Jun 2024 16:14:47 -0400
Subject: [PATCH 135/315] ControlNetApplySD3 node can now be used to use SD3
 controlnets.

---
 comfy/cldm/mmdit.py       |  5 -----
 comfy/controlnet.py       | 28 ++++++++++++++++++++++++----
 comfy_extras/nodes_sd3.py | 15 +++++++++++++++
 nodes.py                  |  4 ++--
 4 files changed, 41 insertions(+), 11 deletions(-)

diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py
index 6e72474ce..ee0282bc7 100644
--- a/comfy/cldm/mmdit.py
+++ b/comfy/cldm/mmdit.py
@@ -1,7 +1,6 @@
 import torch
 from typing import Dict, Optional
 import comfy.ldm.modules.diffusionmodules.mmdit
-import comfy.latent_formats
 
 class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
     def __init__(
@@ -30,8 +29,6 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
             operations=operations
         )
 
-        self.latent_format = comfy.latent_formats.SD3()
-
     def forward(
         self,
         x: torch.Tensor,
@@ -42,10 +39,8 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
     ) -> torch.Tensor:
 
         #weird sd3 controlnet specific stuff
-        hint = hint * self.latent_format.scale_factor # self.latent_format.process_in(hint)
         y = torch.zeros_like(y)
 
-
         if self.context_processor is not None:
             context = self.context_processor(context)
 
diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index 9202c3194..d00395139 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -7,6 +7,7 @@ import comfy.model_management
 import comfy.model_detection
 import comfy.model_patcher
 import comfy.ops
+import comfy.latent_formats
 
 import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
@@ -38,6 +39,8 @@ class ControlBase:
         self.cond_hint = None
         self.strength = 1.0
         self.timestep_percent_range = (0.0, 1.0)
+        self.latent_format = None
+        self.vae = None
         self.global_average_pooling = False
         self.timestep_range = None
         self.compression_ratio = 8
@@ -48,10 +51,12 @@ class ControlBase:
         self.device = device
         self.previous_controlnet = None
 
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)):
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None):
         self.cond_hint_original = cond_hint
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range
+        if self.latent_format is not None:
+            self.vae = vae
         return self
 
     def pre_run(self, model, percent_to_timestep_function):
@@ -84,6 +89,8 @@ class ControlBase:
         c.global_average_pooling = self.global_average_pooling
         c.compression_ratio = self.compression_ratio
         c.upscale_algorithm = self.upscale_algorithm
+        c.latent_format = self.latent_format
+        c.vae = self.vae
 
     def inference_memory_requirements(self, dtype):
         if self.previous_controlnet is not None:
@@ -129,7 +136,7 @@ class ControlBase:
         return out
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, device=None, load_device=None, manual_cast_dtype=None):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None):
         super().__init__(device)
         self.control_model = control_model
         self.load_device = load_device
@@ -140,6 +147,7 @@ class ControlNet(ControlBase):
         self.global_average_pooling = global_average_pooling
         self.model_sampling_current = None
         self.manual_cast_dtype = manual_cast_dtype
+        self.latent_format = latent_format
 
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
@@ -162,7 +170,17 @@ class ControlNet(ControlBase):
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio, self.upscale_algorithm, "center").to(dtype).to(self.device)
+            compression_ratio = self.compression_ratio
+            if self.vae is not None:
+                compression_ratio *= self.vae.downscale_ratio
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
+            if self.vae is not None:
+                loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+                self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
+                comfy.model_management.load_models_gpu(loaded_models)
+            if self.latent_format is not None:
+                self.cond_hint = self.latent_format.process_in(self.cond_hint)
+            self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
@@ -341,7 +359,9 @@ def load_controlnet_mmdit(sd):
     if len(unexpected) > 0:
         logging.debug("unexpected controlnet keys: {}".format(unexpected))
 
-    control = ControlNet(control_model, compression_ratio=1, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    latent_format = comfy.latent_formats.SD3()
+    latent_format.shift_factor = 0 #SD3 controlnet weirdness
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
     return control
 
 
diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py
index d0303aec5..548b1ad6a 100644
--- a/comfy_extras/nodes_sd3.py
+++ b/comfy_extras/nodes_sd3.py
@@ -80,8 +80,23 @@ class CLIPTextEncodeSD3:
         return ([[cond, {"pooled_output": pooled}]], )
 
 
+class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "vae": ("VAE", ),
+                             "image": ("IMAGE", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             }}
+    CATEGORY = "_for_testing/sd3"
+
 NODE_CLASS_MAPPINGS = {
     "TripleCLIPLoader": TripleCLIPLoader,
     "EmptySD3LatentImage": EmptySD3LatentImage,
     "CLIPTextEncodeSD3": CLIPTextEncodeSD3,
+    "ControlNetApplySD3": ControlNetApplySD3,
 }
diff --git a/nodes.py b/nodes.py
index 99645b81c..04775d290 100644
--- a/nodes.py
+++ b/nodes.py
@@ -783,7 +783,7 @@ class ControlNetApplyAdvanced:
 
     CATEGORY = "conditioning"
 
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent):
+    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None):
         if strength == 0:
             return (positive, negative)
 
@@ -800,7 +800,7 @@ class ControlNetApplyAdvanced:
                 if prev_cnet in cnets:
                     c_net = cnets[prev_cnet]
                 else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae)
                     c_net.set_previous_controlnet(prev_cnet)
                     cnets[prev_cnet] = c_net
 

From 0d9009c96ea5a1922a1c187deef27c7f133ee946 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Fri, 28 Jun 2024 06:07:19 +0100
Subject: [PATCH 136/315] New menu/workflows fixes (#3900)

* Fix auto queue

* Detect added nodes via search

* Fix loading workflows

* Add button click style
---
 web/scripts/changeTracker.js       | 12 +++++++++++-
 web/scripts/ui/menu/menu.css       |  4 ++++
 web/scripts/ui/menu/queueButton.js |  4 ++--
 web/scripts/workflows.js           |  4 ++--
 4 files changed, 19 insertions(+), 5 deletions(-)

diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js
index 59901d5fc..041c83122 100644
--- a/web/scripts/changeTracker.js
+++ b/web/scripts/changeTracker.js
@@ -3,7 +3,6 @@
 import { api } from "./api.js";
 import { clone } from "./utils.js";
 
-
 export class ChangeTracker {
 	static MAX_HISTORY = 50;
 	#app;
@@ -170,6 +169,17 @@ export class ChangeTracker {
 			return v;
 		};
 
+		// Detects nodes being added via the node search dialog
+		const onNodeAdded = LiteGraph.LGraph.prototype.onNodeAdded;
+		LiteGraph.LGraph.prototype.onNodeAdded = function () {
+			const v = onNodeAdded?.apply(this, arguments);
+			const ct = changeTracker();
+			if (!ct.isOurLoad) {
+				ct.checkState();
+			}
+			return v;
+		};
+
 		// Store node outputs
 		api.addEventListener("executed", ({ detail }) => {
 			const prompt = app.workflowManager.queuedPrompts[detail.prompt_id];
diff --git a/web/scripts/ui/menu/menu.css b/web/scripts/ui/menu/menu.css
index 20eeab2cf..afaed3fb0 100644
--- a/web/scripts/ui/menu/menu.css
+++ b/web/scripts/ui/menu/menu.css
@@ -19,8 +19,12 @@
 	padding: 4px 8px;
 	box-sizing: border-box;
 	margin: 0;
+	transition: box-shadow 0.1s;
 }
 
+.comfyui-button:active {
+	box-shadow: inset 1px 1px 10px rgba(0, 0, 0, 0.5);
+}
 .comfyui-button:disabled {
 	opacity: 0.5;
 	cursor: not-allowed;
diff --git a/web/scripts/ui/menu/queueButton.js b/web/scripts/ui/menu/queueButton.js
index 3c29ab090..608f4cc9b 100644
--- a/web/scripts/ui/menu/queueButton.js
+++ b/web/scripts/ui/menu/queueButton.js
@@ -13,8 +13,8 @@ export class ComfyQueueButton {
 
 	queuePrompt = async (e) => {
 		this.#internalQueueSize += this.queueOptions.batchCount;
-		// Hold shift to queue front
-		await this.app.queuePrompt(-e.shiftKey, this.queueOptions.batchCount);
+		// Hold shift to queue front, event is undefined when auto-queue is enabled
+		await this.app.queuePrompt(e?.shiftKey ? -1 : 0, this.queueOptions.batchCount);
 	};
 
 	constructor(app) {
diff --git a/web/scripts/workflows.js b/web/scripts/workflows.js
index 16bbc9976..d38b6f5fc 100644
--- a/web/scripts/workflows.js
+++ b/web/scripts/workflows.js
@@ -301,11 +301,11 @@ export class ComfyWorkflow {
 
 	load = async () => {
 		if (this.isOpen) {
-			await this.manager.app.loadGraphData(this.changeTracker.activeState, true, this);
+			await this.manager.app.loadGraphData(this.changeTracker.activeState, true, true, this);
 		} else {
 			const data = await this.getWorkflowData();
 			if (!data) return;
-			await this.manager.app.loadGraphData(data, true, this);
+			await this.manager.app.loadGraphData(data, true, true, this);
 		}
 	};
 

From 7ecb2ec1699dc988f93188c04733eeb8a41e8196 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 28 Jun 2024 02:55:36 -0400
Subject: [PATCH 137/315] Audio second setting in EmptyLatentAudio.

---
 comfy_extras/nodes_audio.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 34bcfa96d..6481ed3ba 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -14,15 +14,16 @@ class EmptyLatentAudio:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {}}
+        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1})}}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
 
     CATEGORY = "_for_testing/audio"
 
-    def generate(self):
+    def generate(self, seconds):
         batch_size = 1
-        latent = torch.zeros([batch_size, 64, 1024], device=self.device)
+        length = round((seconds * 44100 / 2048) / 2) * 2
+        latent = torch.zeros([batch_size, 64, length], device=self.device)
         return ({"samples":latent, "type": "audio"}, )
 
 class VAEEncodeAudio:

From 02cac1d48783725d5994d37ed03e9f3dc32ec504 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Fri, 28 Jun 2024 13:09:39 -0700
Subject: [PATCH 138/315] Revert "Add macOs integration test for default
 workflow. (#3898)" (#3904)

This reverts commit 97b409cd486b1a3b1114ae204802c81f3727d1a2.
---
 .../conda-environments/mac-environment.yml    | 26 ----------
 .github/workflows/mac-integration-test.yml    | 50 -------------------
 2 files changed, 76 deletions(-)
 delete mode 100644 .github/conda-environments/mac-environment.yml
 delete mode 100644 .github/workflows/mac-integration-test.yml

diff --git a/.github/conda-environments/mac-environment.yml b/.github/conda-environments/mac-environment.yml
deleted file mode 100644
index 33832b854..000000000
--- a/.github/conda-environments/mac-environment.yml
+++ /dev/null
@@ -1,26 +0,0 @@
-name: comfyui
-channels:
-  - pytorch-nightly
-  - defaults
-dependencies:
-  - python>=3.9
-  - pytorch-nightly::pytorch
-  - torchvision
-  - torchaudio
-  - pip:
-    - pip
-    # comfyui requirements
-    - einops
-    - transformers>=4.25.1
-    - safetensors>=0.4.2
-    - aiohttp
-    - pyyaml
-    - Pillow
-    - scipy
-    - tqdm
-    - psutil
-    # comfy-action requirements
-    - requests
-    - google-cloud-storage
-    - comfy-cli
-    - charset-normalizer
diff --git a/.github/workflows/mac-integration-test.yml b/.github/workflows/mac-integration-test.yml
deleted file mode 100644
index 6a0482b1b..000000000
--- a/.github/workflows/mac-integration-test.yml
+++ /dev/null
@@ -1,50 +0,0 @@
-name: (macOS) ComfyUI Integration Tests
-on:
-  push:
-    branches:
-      - master
-    paths-ignore:
-      - 'app/**'
-      - 'input/**'
-      - 'output/**'
-      - 'model/**'
-      - 'notebook/**'
-      - 'script_example/**'
-      - 'tests/**'
-      - 'tests-ui/**'
-      - '.github/**'
-      - '.ci/**'
-      - 'web/**'
-  workflow_dispatch:
-  pull_request:
-    branches:
-      - master
-    paths-ignore:
-      - 'app/**'
-      - 'input/**'
-      - 'output/**'
-      - 'model/**'
-      - 'notebook/**'
-      - 'script_example/**'
-      - 'tests/**'
-      - 'tests-ui/**'
-      - '.github/**'
-      - '.ci/**'
-      - 'web/**'
-
-jobs:
-  test-workflows:
-    runs-on: [self-hosted, m2] 
-    steps:
-    - name: Test ComfyUI Workflows
-      uses: comfy-org/comfy-action@main
-      with:
-        os: mac
-        models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}'
-        workflow_filenames: "default.json"
-        gcs_bucket_name: 'comfy-ci-results'
-        google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
-        output_prefix: 'ComfyUI'
-        conda_env_file: '.github/conda-environments/mac-environment.yml'
-        timeout: 50
-

From c39cf7fff09c728d573835a48106e2133c446e11 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Fri, 28 Jun 2024 13:09:55 -0700
Subject: [PATCH 139/315] Revert "Add integration test for Linux with Nvidia
 GPU. #3884 (#3895)" (#3905)

This reverts commit 449bf52923896236ed645da2ff05aadbf2b0d536.
---
 .../conda-environments/linux-environment.yml  | 28 -----------
 .github/workflows/linux-integration-test.yml  | 50 -------------------
 2 files changed, 78 deletions(-)
 delete mode 100644 .github/conda-environments/linux-environment.yml
 delete mode 100644 .github/workflows/linux-integration-test.yml

diff --git a/.github/conda-environments/linux-environment.yml b/.github/conda-environments/linux-environment.yml
deleted file mode 100644
index c9ebd6406..000000000
--- a/.github/conda-environments/linux-environment.yml
+++ /dev/null
@@ -1,28 +0,0 @@
-name: comfyui
-channels:
-  - pytorch
-  - nvidia
-  - defaults
-dependencies:
-  - python>=3.9
-  - pip
-  - pytorch
-  - torchvision
-  - torchaudio
-  - pytorch-cuda=12.1
-  - pip:
-    # comfyui requirements
-    - einops
-    - transformers>=4.25.1
-    - safetensors>=0.4.2
-    - aiohttp
-    - pyyaml
-    - Pillow
-    - scipy
-    - tqdm
-    - psutil
-    # comfy-action requirements
-    - requests
-    - google-cloud-storage
-    - comfy-cli
-    - charset-normalizer
\ No newline at end of file
diff --git a/.github/workflows/linux-integration-test.yml b/.github/workflows/linux-integration-test.yml
deleted file mode 100644
index eed84a77c..000000000
--- a/.github/workflows/linux-integration-test.yml
+++ /dev/null
@@ -1,50 +0,0 @@
-name: (Linux) ComfyUI Integration Tests
-on:
-  push:
-    branches:
-      - master
-    paths-ignore:
-      - 'app/**'
-      - 'input/**'
-      - 'output/**'
-      - 'model/**'
-      - 'notebook/**'
-      - 'script_example/**'
-      - 'tests/**'
-      - 'tests-ui/**'
-      - '.github/**'
-      - '.ci/**'
-      - 'web/**'
-  workflow_dispatch:
-  pull_request:
-    branches:
-      - master
-    paths-ignore:
-      - 'app/**'
-      - 'input/**'
-      - 'output/**'
-      - 'model/**'
-      - 'notebook/**'
-      - 'script_example/**'
-      - 'tests/**'
-      - 'tests-ui/**'
-      - '.github/**'
-      - '.ci/**'
-      - 'web/**'
-
-jobs:
-  test-workflows:
-    runs-on: [self-hosted, Linux, t4] 
-    steps:
-    - name: Test ComfyUI Workflows
-      uses: comfy-org/comfy-action@main
-      with:
-        os: linux
-        cuda_version: 12.1
-        models-json: '{"v1-5-pruned-emaonly.ckpt": {"url": "https://huggingface.co/runwayml/stable-diffusion-v1-5/resolve/main/v1-5-pruned-emaonly.ckpt", "directory": "checkpoints"}}'
-        workflow_filenames: "default.json"
-        gcs_bucket_name: 'comfy-ci-results'
-        google_credentials: ${{ secrets.GCS_SERVICE_ACCOUNT_JSON }}
-        output_prefix: 'ComfyUI'
-        conda_env_file: '.github/conda-environments/linux-environment.yml'
-        timeout: 50

From fbb7a1f1b663b170d49718590aa1cb4093586f20 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 29 Jun 2024 01:33:22 -0400
Subject: [PATCH 140/315] PreviewAudio node.

---
 comfy_extras/nodes_audio.py        | 16 +++++++++++++++-
 web/extensions/core/uploadAudio.js |  6 +++---
 2 files changed, 18 insertions(+), 4 deletions(-)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 6481ed3ba..9950d58ed 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -6,6 +6,7 @@ import os
 import io
 import json
 import struct
+import random
 from comfy.cli_args import args
 
 class EmptyLatentAudio:
@@ -118,7 +119,6 @@ class SaveAudio:
         self.output_dir = folder_paths.get_output_directory()
         self.type = "output"
         self.prefix_append = ""
-        self.compress_level = 4
 
     @classmethod
     def INPUT_TYPES(s):
@@ -168,6 +168,19 @@ class SaveAudio:
 
         return { "ui": { "audio": results } }
 
+class PreviewAudio(SaveAudio):
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"audio": ("AUDIO", ), },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
 class LoadAudio:
     SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif')
 
@@ -214,4 +227,5 @@ NODE_CLASS_MAPPINGS = {
     "VAEDecodeAudio": VAEDecodeAudio,
     "SaveAudio": SaveAudio,
     "LoadAudio": LoadAudio,
+    "PreviewAudio": PreviewAudio,
 }
diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js
index 12a2a196b..0ac9cb807 100644
--- a/web/extensions/core/uploadAudio.js
+++ b/web/extensions/core/uploadAudio.js
@@ -70,7 +70,7 @@ async function uploadFile(
 app.registerExtension({
   name: "Comfy.AudioWidget",
   async beforeRegisterNodeDef(nodeType, nodeData) {
-    if (["LoadAudio", "SaveAudio"].includes(nodeType.comfyClass)) {
+    if (["LoadAudio", "SaveAudio", "PreviewAudio"].includes(nodeType.comfyClass)) {
       nodeData.input.required.audioUI = ["AUDIO_UI"]
     }
   },
@@ -103,7 +103,7 @@ app.registerExtension({
             if (!audios) return
             const audio = audios[0]
             audioUIWidget.element.src = api.apiURL(
-              getResourceURL(audio.subfolder, audio.filename, "output")
+              getResourceURL(audio.subfolder, audio.filename, audio.type)
             )
             audioUIWidget.element.classList.remove("empty-audio-widget")
           }
@@ -118,7 +118,7 @@ app.registerExtension({
       if ("audio" in output) {
         const audioUIWidget = node.widgets.find((w) => w.name === "audioUI");
         const audio = output.audio[0];
-        audioUIWidget.element.src = api.apiURL(getResourceURL(audio.subfolder, audio.filename, "output"));
+        audioUIWidget.element.src = api.apiURL(getResourceURL(audio.subfolder, audio.filename, audio.type));
         audioUIWidget.element.classList.remove("empty-audio-widget");
       }
     }

From 05e831697a03984032863b2aa5d6443e422713e6 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 29 Jun 2024 11:59:48 -0400
Subject: [PATCH 141/315] Switch to the real cfg++ method in the samplers.

The old _pp ones will be updated automatically to the regular ones with 2x
the cfg.

My fault for not checking what the "_pp" samplers actually did.
---
 comfy/k_diffusion/sampling.py | 12 ++++++------
 comfy/samplers.py             |  2 +-
 web/scripts/app.js            |  8 ++++++++
 3 files changed, 15 insertions(+), 7 deletions(-)

diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index f8091bb3f..763d8cc78 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -998,7 +998,7 @@ def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None,
     return x_next
 
 @torch.no_grad()
-def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
     extra_args = {} if extra_args is None else extra_args
 
     temp = [0]
@@ -1013,16 +1013,16 @@ def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=No
     for i in trange(len(sigmas) - 1, disable=disable):
         sigma_hat = sigmas[i]
         denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x - denoised + temp[0], sigma_hat, denoised)
+        d = to_d(x, sigma_hat, temp[0])
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
         dt = sigmas[i + 1] - sigma_hat
         # Euler method
-        x = x + d * dt
+        x = denoised + d * sigmas[i + 1]
     return x
 
 @torch.no_grad()
-def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """Ancestral sampling with Euler method steps."""
     extra_args = {} if extra_args is None else extra_args
     noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
@@ -1041,10 +1041,10 @@ def sample_euler_ancestral_pp(model, x, sigmas, extra_args=None, callback=None,
         sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
-        d = to_d(x - denoised + temp[0], sigmas[i], denoised)
+        d = to_d(x, sigmas[i], temp[0])
         # Euler method
         dt = sigma_down - sigmas[i]
-        x = x + d * dt
+        x = denoised + d * sigma_down
         if sigmas[i + 1] > 0:
             x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
     return x
diff --git a/comfy/samplers.py b/comfy/samplers.py
index 7f7114dbb..c0aa12916 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -537,7 +537,7 @@ class Sampler:
         sigma = float(sigmas[0])
         return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
 
-KSAMPLER_NAMES = ["euler", "euler_pp", "euler_ancestral", "euler_ancestral_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
                   "ipndm", "ipndm_v", "deis"]
diff --git a/web/scripts/app.js b/web/scripts/app.js
index 43df61065..3a91aa239 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -1966,6 +1966,14 @@ export class ComfyApp {
 							if (widget.value.startsWith("sample_")) {
 								widget.value = widget.value.slice(7);
 							}
+							if (widget.value === "euler_pp" || widget.value === "euler_ancestral_pp") {
+								widget.value = widget.value.slice(0, -3);
+								for (let w of node.widgets) {
+									if (w.name == "cfg") {
+										w.value *= 2.0;
+									}
+								}
+							}
 						}
 					}
 					if (node.type == "KSampler" || node.type == "KSamplerAdvanced" || node.type == "PrimitiveNode") {

From dbb7dd3b5eb6672e1141a5fc7006d3bee08b0d21 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 30 Jun 2024 00:15:49 -0400
Subject: [PATCH 142/315] Add to readme that Stable Audio is supported.

---
 README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index a40dd07dd..2ce7c3e6b 100644
--- a/README.md
+++ b/README.md
@@ -11,16 +11,16 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
-- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/) and [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
+- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
-- Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram)
+- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
 - Works even if you don't have a GPU with: ```--cpu``` (slow)
 - Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
 - Embeddings/Textual inversion
 - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
 - [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
-- Loading full workflows (with seeds) from generated PNG files.
+- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
 - Saving/Loading workflows as Json files.
 - Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.
 - [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)

From 521421f53ee1ba74304dfaa138b0f851093e1595 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 30 Jun 2024 15:51:54 -0400
Subject: [PATCH 143/315] Fix workflow not importing from flac files on some
 systems.

---
 web/scripts/app.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index 3a91aa239..776fc197a 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -2292,7 +2292,7 @@ export class ComfyApp {
 			} else {
 				this.showErrorOnFileLoad(file);
 			}
-		} else if (file.type === "audio/flac") {
+		} else if (file.type === "audio/flac" || file.type === "audio/x-flac") {
 			const pngInfo = await getFlacMetadata(file);
 			// Support loading workflows from that webp custom node.
 			const workflow = pngInfo?.workflow;

From 7c5fa7f4a2046c1f4bb77ed5e480918ffb8a10aa Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Mon, 1 Jul 2024 12:10:44 -0400
Subject: [PATCH 144/315] Fix loadGraphData func call (#3918)

---
 web/scripts/app.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index 776fc197a..b8889a4cf 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -2314,14 +2314,14 @@ export class ComfyApp {
 				} else if(this.isApiJson(jsonContent)) {
 					this.loadApiJson(jsonContent, fileName);
 				} else {
-					await this.loadGraphData(jsonContent, true, fileName);
+					await this.loadGraphData(jsonContent, true, true, fileName);
 				}
 			};
 			reader.readAsText(file);
 		} else if (file.name?.endsWith(".latent") || file.name?.endsWith(".safetensors")) {
 			const info = await getLatentMetadata(file);
 			if (info.workflow) {
-				await this.loadGraphData(JSON.parse(info.workflow), true, fileName);
+				await this.loadGraphData(JSON.parse(info.workflow), true, true, fileName);
 			} else if (info.prompt) {
 				this.loadApiJson(JSON.parse(info.prompt));
 			} else {

From e53b1592ba8426e2c01cd9278bc19c2e4264065b Mon Sep 17 00:00:00 2001
From: ruucm <ruucm.a@gmail.com>
Date: Mon, 1 Jul 2024 10:45:34 -0700
Subject: [PATCH 145/315] enable cmd shortcuts for mac (mute & bypass) (#3792)

---
 web/scripts/app.js | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index b8889a4cf..67e488bf9 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -1084,7 +1084,7 @@ export class ComfyApp {
 			if (e.type == "keydown" && !e.repeat) {
 
 				// Ctrl + M mute/unmute
-				if (e.key === 'm' && e.ctrlKey) {
+				if (e.key === 'm' && (e.metaKey || e.ctrlKey)) {
 					if (this.selected_nodes) {
 						for (var i in this.selected_nodes) {
 							if (this.selected_nodes[i].mode === 2) { // never
@@ -1098,7 +1098,7 @@ export class ComfyApp {
 				}
 
 				// Ctrl + B bypass
-				if (e.key === 'b' && e.ctrlKey) {
+				if (e.key === 'b' && (e.metaKey || e.ctrlKey)) {
 					if (this.selected_nodes) {
 						for (var i in this.selected_nodes) {
 							if (this.selected_nodes[i].mode === 4) { // never

From 601b4b63e1c0af2deef8630449497b9e84aa00f8 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Mon, 1 Jul 2024 10:51:00 -0700
Subject: [PATCH 146/315] Add CONTRIBUTING.md (#3910)

* Create CONTRIBUTING.md

* Add feature-request channel link.

* Remove discord links for channels.
---
 CONTRIBUTING.md | 41 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 41 insertions(+)
 create mode 100644 CONTRIBUTING.md

diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 000000000..048f127e7
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,41 @@
+# Contributing to ComfyUI
+
+Welcome, and thank you for your interest in contributing to ComfyUI!
+
+There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
+
+## Asking Questions
+
+Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you.
+
+## Providing Feedback
+
+Your comments and feedback are welcome, and the development team is available via a handful of different channels.
+
+See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord.
+
+## Reporting Issues
+
+Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.
+
+
+### Look For an Existing Issue
+
+Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed.
+
+If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
+
+* 👍 - upvote
+* 👎 - downvote
+
+If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues.
+
+
+### Creating Pull Requests
+
+* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project.
+
+
+## Thank You
+
+Your contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute.

From 0cd4a6a5e5101172a1496c337e67f15ad266fb98 Mon Sep 17 00:00:00 2001
From: YAN Wenkun <yanwenkun@foxmail.com>
Date: Tue, 2 Jul 2024 05:15:49 +0800
Subject: [PATCH 147/315] Fine-tuning GitHub Actions (#3169)

* Bumping GitHub Actions versions

* Using LZMA2 for 7zip compression in Windows packaging
---
 .github/workflows/windows_release_dependencies.yml    | 6 +++---
 .github/workflows/windows_release_nightly_pytorch.yml | 4 ++--
 .github/workflows/windows_release_package.yml         | 6 +++---
 3 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml
index ffd3e2216..5aa57e7d7 100644
--- a/.github/workflows/windows_release_dependencies.yml
+++ b/.github/workflows/windows_release_dependencies.yml
@@ -33,8 +33,8 @@ jobs:
   build_dependencies:
     runs-on: windows-latest
     steps:
-        - uses: actions/checkout@v3
-        - uses: actions/setup-python@v4
+        - uses: actions/checkout@v4
+        - uses: actions/setup-python@v5
           with:
             python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
 
@@ -58,7 +58,7 @@ jobs:
             mv temp_wheel_dir cu${{ inputs.cu }}_python_deps
             tar cf cu${{ inputs.cu }}_python_deps.tar cu${{ inputs.cu }}_python_deps
 
-        - uses: actions/cache/save@v3
+        - uses: actions/cache/save@v4
           with:
             path: |
               cu${{ inputs.cu }}_python_deps.tar
diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml
index fa24a985c..d27c56b30 100644
--- a/.github/workflows/windows_release_nightly_pytorch.yml
+++ b/.github/workflows/windows_release_nightly_pytorch.yml
@@ -32,11 +32,11 @@ jobs:
         pull-requests: "read"
     runs-on: windows-latest
     steps:
-        - uses: actions/checkout@v3
+        - uses: actions/checkout@v4
           with:
             fetch-depth: 0
             persist-credentials: false
-        - uses: actions/setup-python@v4
+        - uses: actions/setup-python@v5
           with:
             python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
         - shell: bash
diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml
index 4e3cdabd2..020741c41 100644
--- a/.github/workflows/windows_release_package.yml
+++ b/.github/workflows/windows_release_package.yml
@@ -32,7 +32,7 @@ jobs:
         pull-requests: "read"
     runs-on: windows-latest
     steps:
-        - uses: actions/cache/restore@v3
+        - uses: actions/cache/restore@v4
           id: cache
           with:
             path: |
@@ -48,7 +48,7 @@ jobs:
             pwd
             ls
 
-        - uses: actions/checkout@v3
+        - uses: actions/checkout@v4
           with:
             fetch-depth: 0
             persist-credentials: false
@@ -82,7 +82,7 @@ jobs:
 
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
             mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
 
             cd ComfyUI_windows_portable

From 5dccfefe8d4ca9b33dfe6dfabcc6c666853c73b9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 1 Jul 2024 17:17:25 -0400
Subject: [PATCH 148/315] Switch nightly pytorch standalone package to lzma2.

---
 .github/workflows/windows_release_nightly_pytorch.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml
index d27c56b30..e68011b64 100644
--- a/.github/workflows/windows_release_nightly_pytorch.yml
+++ b/.github/workflows/windows_release_nightly_pytorch.yml
@@ -73,7 +73,7 @@ jobs:
             pause" > ./update/update_comfyui_and_python_dependencies.bat
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
             mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
 
             cd ComfyUI_windows_portable_nightly_pytorch

From 755c48d78ea2381b3554de729e36acfb381ebc67 Mon Sep 17 00:00:00 2001
From: Hayden Reeve <39004735+HaydenReeve@users.noreply.github.com>
Date: Tue, 2 Jul 2024 05:21:12 +0800
Subject: [PATCH 149/315] Fix several typos in example_node.py.example (#3204)

This change includes corrections for several spelling errors in the
documentation of example_node.py.example file.

These were previously raised by #3157, but they missed a few.
---
 custom_nodes/example_node.py.example | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example
index f06632593..57c9cbedf 100644
--- a/custom_nodes/example_node.py.example
+++ b/custom_nodes/example_node.py.example
@@ -12,9 +12,9 @@ class Example:
     Attributes
     ----------
     RETURN_TYPES (`tuple`): 
-        The type of each element in the output tulple.
+        The type of each element in the output tuple.
     RETURN_NAMES (`tuple`):
-        Optional: The name of each output in the output tulple.
+        Optional: The name of each output in the output tuple.
     FUNCTION (`str`):
         The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
     OUTPUT_NODE ([`bool`]):
@@ -44,7 +44,7 @@ class Example:
                     * Key field_name (`string`): Name of a entry-point method's argument
                     * Value field_config (`tuple`):
                         + First value is a string indicate the type of field or a list for selection.
-                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
+                        + Second value is a config for type "INT", "STRING" or "FLOAT".
         """
         return {
             "required": {
@@ -61,7 +61,7 @@ class Example:
                     "min": 0.0,
                     "max": 10.0,
                     "step": 0.01,
-                    "round": 0.001, #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                    "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
                     "display": "number"}),
                 "print_to_screen": (["enable", "disable"],),
                 "string_field": ("STRING", {

From b82d67d5bfa9a1f8c6d0167bf871cbd151c1005f Mon Sep 17 00:00:00 2001
From: Peter Crabtree <peter.crabtree@gmail.com>
Date: Mon, 1 Jul 2024 17:42:17 -0400
Subject: [PATCH 150/315] Add SamplerEulerAncestralCFG++ custom sampler node
 (#3901)

(for eta and s_noise)
---
 comfy_extras/nodes_custom_sampler.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
index 69f1b9418..64a8c0631 100644
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -293,6 +293,25 @@ class SamplerEulerAncestral:
         sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
         return (sampler, )
 
+class SamplerEulerAncestralCFGPP:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}),
+                "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}),
+            }}
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta, s_noise):
+        sampler = comfy.samplers.ksampler(
+            "euler_ancestral_cfg_pp",
+            {"eta": eta, "s_noise": s_noise})
+        return (sampler, )
+
 class SamplerLMS:
     @classmethod
     def INPUT_TYPES(s):
@@ -622,6 +641,7 @@ NODE_CLASS_MAPPINGS = {
     "SDTurboScheduler": SDTurboScheduler,
     "KSamplerSelect": KSamplerSelect,
     "SamplerEulerAncestral": SamplerEulerAncestral,
+    "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP,
     "SamplerLMS": SamplerLMS,
     "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE,
     "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
@@ -639,3 +659,7 @@ NODE_CLASS_MAPPINGS = {
     "AddNoise": AddNoise,
     "SamplerCustomAdvanced": SamplerCustomAdvanced,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++",
+}
\ No newline at end of file

From 9dd549e253f0e2c5e6360f9349b0872af18d8962 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Mon, 1 Jul 2024 17:54:03 -0400
Subject: [PATCH 151/315] Add `--no-custom-node` cmd flag (#3903)

* Add --no-custom-node cmd flag

* nit
---
 comfy/cli_args.py |  1 +
 folder_paths.py   |  8 +++++---
 main.py           | 13 +++++++++++--
 nodes.py          | 24 ++++++++++++++++++++----
 4 files changed, 37 insertions(+), 9 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index fb0d37ce7..b72bf3998 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -118,6 +118,7 @@ parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test
 parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
 
 parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
+parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
 
diff --git a/folder_paths.py b/folder_paths.py
index 234b73409..2cf45f12a 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -2,9 +2,11 @@ import os
 import time
 import logging
 
-supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
+supported_pt_extensions: set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
 
-folder_names_and_paths = {}
+SupportedFileExtensionsType = set[str]
+ScanPathType = list[str]
+folder_names_and_paths: dict[str, tuple[ScanPathType, SupportedFileExtensionsType]] = {}
 
 base_path = os.path.dirname(os.path.realpath(__file__))
 models_dir = os.path.join(base_path, "models")
@@ -26,7 +28,7 @@ folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], suppor
 
 folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
 
-folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], [])
+folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set())
 
 folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
 
diff --git a/main.py b/main.py
index a374f2b12..2957dd2ff 100644
--- a/main.py
+++ b/main.py
@@ -5,6 +5,8 @@ import os
 import importlib.util
 import folder_paths
 import time
+from comfy.cli_args import args
+
 
 def execute_prestartup_script():
     def execute_script(script_path):
@@ -18,6 +20,9 @@ def execute_prestartup_script():
             print(f"Failed to execute startup-script: {script_path} / {e}")
         return False
 
+    if args.disable_all_custom_nodes:
+        return
+
     node_paths = folder_paths.get_folder_paths("custom_nodes")
     for custom_node_path in node_paths:
         possible_modules = os.listdir(custom_node_path)
@@ -76,7 +81,7 @@ import yaml
 import execution
 import server
 from server import BinaryEventTypes
-from nodes import init_custom_nodes
+from nodes import init_builtin_extra_nodes, init_external_custom_nodes
 import comfy.model_management
 
 def cuda_malloc_warning():
@@ -214,7 +219,11 @@ if __name__ == "__main__":
         for config_path in itertools.chain(*args.extra_model_paths_config):
             load_extra_path_config(config_path)
 
-    init_custom_nodes()
+    init_builtin_extra_nodes()
+    if not args.disable_all_custom_nodes:
+        init_external_custom_nodes()
+    else:
+        logging.info("Skipping loading of custom nodes")
 
     cuda_malloc_warning()
 
diff --git a/nodes.py b/nodes.py
index 04775d290..a0d2178f4 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1925,7 +1925,16 @@ def load_custom_node(module_path, ignore=set()):
         logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
         return False
 
-def load_custom_nodes():
+def init_external_custom_nodes():
+    """
+    Initializes the external custom nodes.
+
+    This function loads custom nodes from the specified folder paths and imports them into the application.
+    It measures the import times for each custom node and logs the results.
+
+    Returns:
+        None
+    """
     base_node_names = set(NODE_CLASS_MAPPINGS.keys())
     node_paths = folder_paths.get_folder_paths("custom_nodes")
     node_import_times = []
@@ -1952,7 +1961,16 @@ def load_custom_nodes():
             logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
         logging.info("")
 
-def init_custom_nodes():
+def init_builtin_extra_nodes():
+    """
+    Initializes the built-in extra nodes in ComfyUI.
+
+    This function loads the extra node files located in the "comfy_extras" directory and imports them into ComfyUI.
+    If any of the extra node files fail to import, a warning message is logged.
+
+    Returns:
+        None
+    """
     extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras")
     extras_files = [
         "nodes_latent.py",
@@ -1999,8 +2017,6 @@ def init_custom_nodes():
         if not load_custom_node(os.path.join(extras_dir, node_file)):
             import_failed.append(node_file)
 
-    load_custom_nodes()
-
     if len(import_failed) > 0:
         logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n")
         for node in import_failed:

From 1ef66b095586981633cd11906e6d2e555deab9f6 Mon Sep 17 00:00:00 2001
From: Bob Du <i@bobdu.cc>
Date: Tue, 2 Jul 2024 06:02:42 +0800
Subject: [PATCH 152/315] Add example for how to add custom API routes (#3597)

---
 custom_nodes/example_node.py.example | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example
index 57c9cbedf..72ca3688c 100644
--- a/custom_nodes/example_node.py.example
+++ b/custom_nodes/example_node.py.example
@@ -106,6 +106,16 @@ class Example:
 # Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension
 # WEB_DIRECTORY = "./somejs"
 
+
+# Add custom API routes, using router
+from aiohttp import web
+from server import PromptServer
+
+@PromptServer.instance.routes.get("/hello")
+async def get_hello(request):
+    return web.json_response("hello")
+
+
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique
 NODE_CLASS_MAPPINGS = {

From 52aaee251fcd87b7c554f4e9c1386b184236f81f Mon Sep 17 00:00:00 2001
From: shawnington <88048838+shawnington@users.noreply.github.com>
Date: Tue, 2 Jul 2024 01:30:33 -0400
Subject: [PATCH 153/315] Fix to #3465. Prevent, resaving of duplicate images
 if overwrite not specified (#3472)

* Fix to #3465. Prevent the, resaving of duplicate images if overwrite not specified

This is a fix to #3465

Adds function compare_image_hash to do a sha256 hash comparison between an uploaded image and existing images with matching file names.

This changes the behavior so that only images having the same filename that are actually different are saved to input, existing images are instead now opened instead of resaved with increment.

Currently, exact duplicates with the same filename are resave saved with an incremented filename in the format:

<filename> (n).ext

with the code:

```
while os.path.exists(filepath):
                        filename = f"{split[0]} ({i}){split[1]}"
                        filepath = os.path.join(full_output_folder, filename)
                        i += 1
```

This commit changes this to:

```
while os.path.exists(filepath):
                        if compare_image_hash(filepath, image):
                            image_is_duplicate = True
                            break
                        filename = f"{split[0]} ({i}){split[1]}"
                        filepath = os.path.join(full_output_folder, filename)
                        i += 1
```

a check for if image_is_duplicate = False is done before saving the file.

Currently, if you load the same image of a cat named cat.jpg into the LoadImage node 3 times, you will get 3 new files in your input folder with incremented file names.

With this change, you will now only have the single copy of cat.jpg, that will be re-opened instead of re-saved.

However if you load 3 different images of cats named cat.jpg, you will get the expected behavior of having:
cat.jpg
cat (1).jpg
cat (2).jpg

This saves space and clutter. After checking my own input folder, I have 800+ images that are duplicates that were resaved with incremented file names amounting to more than 5GB of duplicated data.

* fixed typo in expression
---
 server.py | 31 +++++++++++++++++++++++++------
 1 file changed, 25 insertions(+), 6 deletions(-)

diff --git a/server.py b/server.py
index 30ea90c6c..5b98620ad 100644
--- a/server.py
+++ b/server.py
@@ -12,6 +12,7 @@ import json
 import glob
 import struct
 import ssl
+import hashlib
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 from io import BytesIO
@@ -153,10 +154,24 @@ class PromptServer():
                 type_dir = folder_paths.get_output_directory()
 
             return type_dir, dir_type
-
+            
+        def compare_image_hash(filepath, image):
+            # function to compare hashes of two images to see if it already exists, fix to #3465
+            if os.path.exists(filepath):
+                a = hashlib.sha256()
+                b = hashlib.sha256()
+                with open(filepath, "rb") as f:
+                    a.update(f.read())
+                    b.update(image.file.read())
+                    image.file.seek(0)
+                    f.close()
+                return a.hexdigest() == b.hexdigest()
+            return False
+                    
         def image_upload(post, image_save_function=None):
             image = post.get("image")
             overwrite = post.get("overwrite")
+            image_is_duplicate = False
 
             image_upload_type = post.get("type")
             upload_dir, image_upload_type = get_dir_by_type(image_upload_type)
@@ -183,15 +198,19 @@ class PromptServer():
                 else:
                     i = 1
                     while os.path.exists(filepath):
+                        if compare_image_hash(filepath, image): #compare hash to prevent saving of duplicates with same name, fix for #3465
+                            image_is_duplicate = True
+                            break
                         filename = f"{split[0]} ({i}){split[1]}"
                         filepath = os.path.join(full_output_folder, filename)
                         i += 1
 
-                if image_save_function is not None:
-                    image_save_function(image, post, filepath)
-                else:
-                    with open(filepath, "wb") as f:
-                        f.write(image.file.read())
+                if not image_is_duplicate: 
+                    if image_save_function is not None:
+                        image_save_function(image, post, filepath)
+                    else:
+                        with open(filepath, "wb") as f:
+                            f.write(image.file.read())
 
                 return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type})
             else:

From 2f03201690e0fb8a3ec551a125b20d89c9019a02 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 2 Jul 2024 01:32:23 -0400
Subject: [PATCH 154/315] Remove some empty lines.

---
 server.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/server.py b/server.py
index 5b98620ad..38b1bab80 100644
--- a/server.py
+++ b/server.py
@@ -111,7 +111,7 @@ class PromptServer():
                 # On reconnect if we are the currently executing client send the current node
                 if self.client_id == sid and self.last_node_id is not None:
                     await self.send("executing", { "node": self.last_node_id }, sid)
-                    
+
                 async for msg in ws:
                     if msg.type == aiohttp.WSMsgType.ERROR:
                         logging.warning('ws connection closed with exception %s' % ws.exception())
@@ -132,9 +132,9 @@ class PromptServer():
         async def get_extensions(request):
             files = glob.glob(os.path.join(
                 glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True)
-            
+
             extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files))
-            
+
             for name, dir in nodes.EXTENSION_WEB_DIRS.items():
                 files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True)
                 extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote(
@@ -154,7 +154,7 @@ class PromptServer():
                 type_dir = folder_paths.get_output_directory()
 
             return type_dir, dir_type
-            
+
         def compare_image_hash(filepath, image):
             # function to compare hashes of two images to see if it already exists, fix to #3465
             if os.path.exists(filepath):
@@ -167,7 +167,7 @@ class PromptServer():
                     f.close()
                 return a.hexdigest() == b.hexdigest()
             return False
-                    
+
         def image_upload(post, image_save_function=None):
             image = post.get("image")
             overwrite = post.get("overwrite")
@@ -205,7 +205,7 @@ class PromptServer():
                         filepath = os.path.join(full_output_folder, filename)
                         i += 1
 
-                if not image_is_duplicate: 
+                if not image_is_duplicate:
                     if image_save_function is not None:
                         image_save_function(image, post, filepath)
                     else:

From 01991f72ce1bdce31ff4bd6fc2fa64454b1526ad Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 2 Jul 2024 12:21:08 -0400
Subject: [PATCH 155/315] Fix SamplerEulerCFGpp node.

---
 comfy_extras/nodes_advanced_samplers.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py
index cee3a10c4..820c250ef 100644
--- a/comfy_extras/nodes_advanced_samplers.py
+++ b/comfy_extras/nodes_advanced_samplers.py
@@ -60,7 +60,7 @@ from comfy.k_diffusion.sampling import to_d
 import comfy.model_patcher
 
 @torch.no_grad()
-def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
     extra_args = {} if extra_args is None else extra_args
 
     temp = [0]
@@ -75,11 +75,11 @@ def sample_euler_cfgpp(model, x, sigmas, extra_args=None, callback=None, disable
     for i in trange(len(sigmas) - 1, disable=disable):
         sigma_hat = sigmas[i]
         denoised = model(x, sigma_hat * s_in, **extra_args)
-        d = to_d(x, sigma_hat, temp[0])
+        d = to_d(x - denoised + temp[0], sigmas[i], denoised)
         if callback is not None:
             callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
         dt = sigmas[i + 1] - sigma_hat
-        x = denoised + sigmas[i + 1] * d
+        x = x + d * dt
     return x
 
 
@@ -96,10 +96,10 @@ class SamplerEulerCFGpp:
     FUNCTION = "get_sampler"
 
     def get_sampler(self, version):
-        if version == "regular":
-            sampler = comfy.samplers.KSAMPLER(sample_euler_cfgpp)
+        if version == "alternative":
+            sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
         else:
-            sampler = comfy.samplers.ksampler("euler_pp")
+            sampler = comfy.samplers.ksampler("euler_cfg_pp")
         return (sampler, )
 
 NODE_CLASS_MAPPINGS = {

From 3f46362d227fa0ca578a69aeb194951b9f87aea0 Mon Sep 17 00:00:00 2001
From: "Alex \"mcmonkey\" Goodwin"
 <4000772+mcmonkey4eva@users.noreply.github.com>
Date: Tue, 2 Jul 2024 17:16:33 -0700
Subject: [PATCH 156/315] fix non-contiguous tensor saving (from channels-last)
 (#3932)

---
 comfy/sd.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy/sd.py b/comfy/sd.py
index dda0887ba..b28bfe358 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -627,5 +627,7 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m
     sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd)
     for k in extra_keys:
         sd[k] = extra_keys[k]
+    for k in sd:
+        sd[k] = sd[k].contiguous()
 
     comfy.utils.save_torch_file(sd, output_path, metadata=metadata)

From 537f35c7bc76e611b53102d3b5a2926e46ec7362 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 2 Jul 2024 20:21:51 -0400
Subject: [PATCH 157/315] Don't update dict if contiguous.

---
 comfy/sd.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index b28bfe358..4ba00a62c 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -627,7 +627,10 @@ def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, m
     sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd)
     for k in extra_keys:
         sd[k] = extra_keys[k]
+
     for k in sd:
-        sd[k] = sd[k].contiguous()
+        t = sd[k]
+        if not t.is_contiguous():
+            sd[k] = t.contiguous()
 
     comfy.utils.save_torch_file(sd, output_path, metadata=metadata)

From d7484ef30c97c077dde1c7ae07a16689bec86dd3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 3 Jul 2024 11:34:32 -0400
Subject: [PATCH 158/315] Support loading checkpoints with the UNETLoader node.

---
 comfy/sd.py | 24 ++++++++++++++++--------
 1 file changed, 16 insertions(+), 8 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 4ba00a62c..e343e1fa8 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -563,24 +563,32 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_unet_state_dict(sd): #load unet in diffusers format
+def load_unet_state_dict(sd): #load unet in diffusers or regular format
+
+    #Allow loading unets from checkpoint files
+    checkpoint = False
+    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
+    temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
+    if len(temp_sd) > 0:
+        sd = temp_sd
+        checkpoint = True
+
     parameters = comfy.utils.calculate_parameters(sd)
     unet_dtype = model_management.unet_dtype(model_params=parameters)
     load_device = model_management.get_torch_device()
 
-    if 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
+    if checkpoint or "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
+        model_config = model_detection.model_config_from_unet(sd, "")
+        if model_config is None:
+            return None
+        new_sd = sd
+    elif 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
         new_sd = model_detection.convert_diffusers_mmdit(sd, "")
         if new_sd is None:
             return None
         model_config = model_detection.model_config_from_unet(new_sd, "")
         if model_config is None:
             return None
-    elif "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
-        model_config = model_detection.model_config_from_unet(sd, "")
-        if model_config is None:
-            return None
-        new_sd = sd
-
     else: #diffusers
         model_config = model_detection.model_config_from_diffusers_unet(sd)
         if model_config is None:

From 086ac75228af3fea96be58ec45c2f6c17286e75d Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 3 Jul 2024 19:31:46 -0400
Subject: [PATCH 159/315] 3.8 Compatible type annotation (#3938)

---
 folder_paths.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/folder_paths.py b/folder_paths.py
index 2cf45f12a..2baf8ce1c 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -1,12 +1,13 @@
 import os
 import time
 import logging
+from typing import Set, List, Dict, Tuple
 
-supported_pt_extensions: set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
+supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
 
-SupportedFileExtensionsType = set[str]
-ScanPathType = list[str]
-folder_names_and_paths: dict[str, tuple[ScanPathType, SupportedFileExtensionsType]] = {}
+SupportedFileExtensionsType = Set[str]
+ScanPathType = List[str]
+folder_names_and_paths: Dict[str, Tuple[ScanPathType, SupportedFileExtensionsType]] = {}
 
 base_path = os.path.dirname(os.path.realpath(__file__))
 models_dir = os.path.join(base_path, "models")

From 24b969d3dace581ed3fb547eedfbdf502ad0f7e9 Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Wed, 3 Jul 2024 17:30:07 -0700
Subject: [PATCH 160/315] Skip state check hook on first load (#3915)

---
 web/scripts/changeTracker.js | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js
index 041c83122..39bc4a810 100644
--- a/web/scripts/changeTracker.js
+++ b/web/scripts/changeTracker.js
@@ -173,9 +173,11 @@ export class ChangeTracker {
 		const onNodeAdded = LiteGraph.LGraph.prototype.onNodeAdded;
 		LiteGraph.LGraph.prototype.onNodeAdded = function () {
 			const v = onNodeAdded?.apply(this, arguments);
-			const ct = changeTracker();
-			if (!ct.isOurLoad) {
-				ct.checkState();
+			if (!app?.configuringGraph) {
+				const ct = changeTracker();
+				if (!ct.isOurLoad) {
+					ct.checkState();
+				}
 			}
 			return v;
 		};

From 739b76630e0298561c39d0d3341bc2856e55d995 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 4 Jul 2024 15:14:13 -0400
Subject: [PATCH 161/315] Remove useless code.

---
 comfy/cldm/mmdit.py | 9 ---------
 1 file changed, 9 deletions(-)

diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py
index ee0282bc7..025c2fb5d 100644
--- a/comfy/cldm/mmdit.py
+++ b/comfy/cldm/mmdit.py
@@ -56,15 +56,6 @@ class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
         if context is not None:
             context = self.context_embedder(context)
 
-        if self.register_length > 0:
-            context = torch.cat(
-                (
-                    repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
-                    default(context, torch.Tensor([]).type_as(x)),
-                ),
-                1,
-            )
-
         output = []
 
         blocks = len(self.joint_blocks)

From 0e3dfd9e343d4aee7826fbc188e2af8ceb1bdde9 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Thu, 4 Jul 2024 20:49:07 -0400
Subject: [PATCH 162/315] Use relative path for custom/extra node module name
 (#3944)

* Fix module name for comfy extra nodes

* Use module name relative to root dir
---
 nodes.py | 30 ++++++++++++++++++++++++++----
 1 file changed, 26 insertions(+), 4 deletions(-)

diff --git a/nodes.py b/nodes.py
index a0d2178f4..81efecf65 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1887,11 +1887,33 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 
 EXTENSION_WEB_DIRS = {}
 
-def load_custom_node(module_path, ignore=set()):
-    module_name = os.path.basename(module_path)
+
+def get_module_name(module_path: str) -> str:
+    """
+    Returns the module name based on the given module path.
+    Examples:
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes.my
+
+    Args:
+        module_path (str): The path of the module.
+
+    Returns:
+        str: The module name.
+    """
+    relative_path = os.path.relpath(module_path, folder_paths.base_path)
     if os.path.isfile(module_path):
-        sp = os.path.splitext(module_path)
-        module_name = sp[0]
+        relative_path = os.path.splitext(relative_path)[0]
+    return relative_path.replace(os.sep, '.')
+
+
+def load_custom_node(module_path, ignore=set()):
+    module_name = get_module_name(module_path)
     try:
         logging.debug("Trying to load custom node {}".format(module_path))
         if os.path.isfile(module_path):

From 720b17442d39b5991e1d3c336f9d16a0f75c929c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 4 Jul 2024 21:09:05 -0400
Subject: [PATCH 163/315] Temporary revert.

---
 nodes.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/nodes.py b/nodes.py
index 81efecf65..03544be02 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1913,7 +1913,7 @@ def get_module_name(module_path: str) -> str:
 
 
 def load_custom_node(module_path, ignore=set()):
-    module_name = get_module_name(module_path)
+    module_name = os.path.basename(module_path)
     try:
         logging.debug("Trying to load custom node {}".format(module_path))
         if os.path.isfile(module_path):

From bd2d3e27d786b89e9ef89d6eb083e94172126306 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 4 Jul 2024 21:43:23 -0400
Subject: [PATCH 164/315] Show comfy_extras warning at the end.

Remove code.
---
 main.py  |  8 ++------
 nodes.py | 35 +++++++++++------------------------
 2 files changed, 13 insertions(+), 30 deletions(-)

diff --git a/main.py b/main.py
index 2957dd2ff..9650703a8 100644
--- a/main.py
+++ b/main.py
@@ -81,7 +81,7 @@ import yaml
 import execution
 import server
 from server import BinaryEventTypes
-from nodes import init_builtin_extra_nodes, init_external_custom_nodes
+import nodes
 import comfy.model_management
 
 def cuda_malloc_warning():
@@ -219,11 +219,7 @@ if __name__ == "__main__":
         for config_path in itertools.chain(*args.extra_model_paths_config):
             load_extra_path_config(config_path)
 
-    init_builtin_extra_nodes()
-    if not args.disable_all_custom_nodes:
-        init_external_custom_nodes()
-    else:
-        logging.info("Skipping loading of custom nodes")
+    nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes)
 
     cuda_malloc_warning()
 
diff --git a/nodes.py b/nodes.py
index 03544be02..8f8abb494 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1888,30 +1888,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 EXTENSION_WEB_DIRS = {}
 
 
-def get_module_name(module_path: str) -> str:
-    """
-    Returns the module name based on the given module path.
-    Examples:
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes.my
-
-    Args:
-        module_path (str): The path of the module.
-
-    Returns:
-        str: The module name.
-    """
-    relative_path = os.path.relpath(module_path, folder_paths.base_path)
-    if os.path.isfile(module_path):
-        relative_path = os.path.splitext(relative_path)[0]
-    return relative_path.replace(os.sep, '.')
-
-
 def load_custom_node(module_path, ignore=set()):
     module_name = os.path.basename(module_path)
     try:
@@ -2039,6 +2015,17 @@ def init_builtin_extra_nodes():
         if not load_custom_node(os.path.join(extras_dir, node_file)):
             import_failed.append(node_file)
 
+    return import_failed
+
+
+def init_extra_nodes(init_custom_nodes=True):
+    import_failed = init_external_custom_nodes()
+
+    if init_custom_nodes:
+        init_external_custom_nodes()
+    else:
+        logging.info("Skipping loading of custom nodes")
+
     if len(import_failed) > 0:
         logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n")
         for node in import_failed:

From cedbc94cc0551cfdeae01c48b922d546c816f898 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 4 Jul 2024 21:49:50 -0400
Subject: [PATCH 165/315] Forgot this in last commit.

---
 nodes.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/nodes.py b/nodes.py
index 8f8abb494..9c541eba8 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1890,6 +1890,9 @@ EXTENSION_WEB_DIRS = {}
 
 def load_custom_node(module_path, ignore=set()):
     module_name = os.path.basename(module_path)
+    if os.path.isfile(module_path):
+        sp = os.path.splitext(module_path)
+        module_name = sp[0]
     try:
         logging.debug("Trying to load custom node {}".format(module_path))
         if os.path.isfile(module_path):
@@ -2019,7 +2022,7 @@ def init_builtin_extra_nodes():
 
 
 def init_extra_nodes(init_custom_nodes=True):
-    import_failed = init_external_custom_nodes()
+    import_failed = init_builtin_extra_nodes()
 
     if init_custom_nodes:
         init_external_custom_nodes()

From 1dc87df4c5f4925f8eb286236e71c9dd38941dd6 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 4 Jul 2024 22:03:37 -0400
Subject: [PATCH 166/315] Readme changes.

---
 README.md | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index 2ce7c3e6b..35e3238a1 100644
--- a/README.md
+++ b/README.md
@@ -225,12 +225,11 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w
 
 [Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
 
+See also: [https://www.comfy.org/](https://www.comfy.org/)
+
 # QA
 
-### Why did you make this?
+### Which GPU should I buy for this?
 
-I wanted to learn how Stable Diffusion worked in detail. I also wanted something clean and powerful that would let me experiment with SD without restrictions.
+[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
 
-### Who is this for?
-
-This is for anyone that wants to make complex workflows with SD or that wants to learn more how SD works. The interface follows closely how SD works and the code should be much more simple to understand than other SD UIs.

From b4c2d03d476f97d9d91c0d7d0af7fde382084385 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 5 Jul 2024 12:10:22 -0400
Subject: [PATCH 167/315] Remove duplicate import.

---
 main.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/main.py b/main.py
index 9650703a8..196351a3d 100644
--- a/main.py
+++ b/main.py
@@ -58,7 +58,6 @@ import shutil
 import threading
 import gc
 
-from comfy.cli_args import args
 import logging
 
 if os.name == "nt":

From ce649d61c05ab0e905155b4a010d4f0459a0db8d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 5 Jul 2024 23:48:17 -0400
Subject: [PATCH 168/315] Allow zeroing out of embeds with unused attention
 mask.

---
 comfy/sd1_clip.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 78e556b56..3b812b44c 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -169,7 +169,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         tokens = torch.LongTensor(tokens).to(device)
 
         attention_mask = None
-        if self.enable_attention_masks:
+        if self.enable_attention_masks or self.zero_out_masked:
             attention_mask = torch.zeros_like(tokens)
             end_token = self.special_tokens.get("end", -1)
             for x in range(attention_mask.shape[0]):
@@ -178,7 +178,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
                     if tokens[x, y] == end_token:
                         break
 
-        outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
+        attention_mask_model = None
+        if self.enable_attention_masks:
+            attention_mask_model = attention_mask
+
+        outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
         self.transformer.set_input_embeddings(backup_embeds)
 
         if self.layer == "last":
@@ -186,7 +190,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         else:
             z = outputs[1].float()
 
-        if self.zero_out_masked and attention_mask is not None:
+        if self.zero_out_masked:
             z *= attention_mask.unsqueeze(-1).float()
 
         pooled_output = None

From 80c45909985aa37e6803ed59b6753fc289ee365f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 00:06:49 -0400
Subject: [PATCH 169/315] Allow specifying the padding token for the tokenizer.

---
 comfy/sd1_clip.py | 20 ++++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 3b812b44c..ed3dc2298 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -364,7 +364,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
@@ -380,6 +380,14 @@ class SDTokenizer:
             self.tokens_start = 0
             self.start_token = None
             self.end_token = empty[0]
+
+        if pad_token is not None:
+            self.pad_token = pad_token
+        elif pad_with_end:
+            self.pad_token = self.end_token
+        else:
+            self.pad_token = 0
+
         self.pad_with_end = pad_with_end
         self.pad_to_max_length = pad_to_max_length
 
@@ -412,10 +420,6 @@ class SDTokenizer:
         Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
         Returned list has the dimensions NxM where M is the input size of CLIP
         '''
-        if self.pad_with_end:
-            pad_token = self.end_token
-        else:
-            pad_token = 0
 
         text = escape_important(text)
         parsed_weights = token_weights(text, 1.0)
@@ -467,7 +471,7 @@ class SDTokenizer:
                     else:
                         batch.append((self.end_token, 1.0, 0))
                         if self.pad_to_max_length:
-                            batch.extend([(pad_token, 1.0, 0)] * (remaining_length))
+                            batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
                     #start new batch
                     batch = []
                     if self.start_token is not None:
@@ -480,9 +484,9 @@ class SDTokenizer:
         #fill last batch
         batch.append((self.end_token, 1.0, 0))
         if self.pad_to_max_length:
-            batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch)))
+            batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
         if self.min_length is not None and len(batch) < self.min_length:
-            batch.extend([(pad_token, 1.0, 0)] * (self.min_length - len(batch)))
+            batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch)))
 
         if not return_word_ids:
             batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]

From b8e58a939463be85877c1244ee00763368723c07 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 00:33:25 -0400
Subject: [PATCH 170/315] Cleanup T5 code a bit.

---
 comfy/t5.py               | 35 +++++++++++++++++++++--------------
 comfy/t5_config_base.json |  1 +
 comfy/t5_config_xxl.json  |  1 +
 3 files changed, 23 insertions(+), 14 deletions(-)

diff --git a/comfy/t5.py b/comfy/t5.py
index 06dfe4766..d00b560a3 100644
--- a/comfy/t5.py
+++ b/comfy/t5.py
@@ -13,29 +13,36 @@ class T5LayerNorm(torch.nn.Module):
         x = x * torch.rsqrt(variance + self.variance_epsilon)
         return self.weight.to(device=x.device, dtype=x.dtype) * x
 
+activations = {
+    "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
+    "relu": torch.nn.functional.relu,
+}
+
 class T5DenseActDense(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, dtype, device, operations):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
         super().__init__()
         self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
         self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
         # self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = activations[ff_activation]
 
     def forward(self, x):
-        x = torch.nn.functional.relu(self.wi(x))
+        x = self.act(self.wi(x))
         # x = self.dropout(x)
         x = self.wo(x)
         return x
 
 class T5DenseGatedActDense(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, dtype, device, operations):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
         super().__init__()
         self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
         self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
         self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
         # self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = activations[ff_activation]
 
     def forward(self, x):
-        hidden_gelu = torch.nn.functional.gelu(self.wi_0(x), approximate="tanh")
+        hidden_gelu = self.act(self.wi_0(x))
         hidden_linear = self.wi_1(x)
         x = hidden_gelu * hidden_linear
         # x = self.dropout(x)
@@ -43,12 +50,12 @@ class T5DenseGatedActDense(torch.nn.Module):
         return x
 
 class T5LayerFF(torch.nn.Module):
-    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+    def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations):
         super().__init__()
-        if ff_activation == "gelu_pytorch_tanh":
-            self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, dtype, device, operations)
-        elif ff_activation == "relu":
-            self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, dtype, device, operations)
+        if gated_act:
+            self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+        else:
+            self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
 
         self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
         # self.dropout = nn.Dropout(config.dropout_rate)
@@ -171,11 +178,11 @@ class T5LayerSelfAttention(torch.nn.Module):
         return x, past_bias
 
 class T5Block(torch.nn.Module):
-    def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias, dtype, device, operations):
+    def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations):
         super().__init__()
         self.layer = torch.nn.ModuleList()
         self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
-        self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, dtype, device, operations))
+        self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations))
 
     def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
         x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
@@ -183,11 +190,11 @@ class T5Block(torch.nn.Module):
         return x, past_bias
 
 class T5Stack(torch.nn.Module):
-    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, num_heads, dtype, device, operations):
+    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, dtype, device, operations):
         super().__init__()
 
         self.block = torch.nn.ModuleList(
-            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
+            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
         )
         self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
         # self.dropout = nn.Dropout(config.dropout_rate)
@@ -216,7 +223,7 @@ class T5(torch.nn.Module):
         self.num_layers = config_dict["num_layers"]
         model_dim = config_dict["d_model"]
 
-        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["num_heads"], dtype, device, operations)
+        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], dtype, device, operations)
         self.dtype = dtype
         self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
 
diff --git a/comfy/t5_config_base.json b/comfy/t5_config_base.json
index facd85ef3..71f68327c 100644
--- a/comfy/t5_config_base.json
+++ b/comfy/t5_config_base.json
@@ -8,6 +8,7 @@
   "dense_act_fn": "relu",
   "initializer_factor": 1.0,
   "is_encoder_decoder": true,
+  "is_gated_act": false,
   "layer_norm_epsilon": 1e-06,
   "model_type": "t5",
   "num_decoder_layers": 12,
diff --git a/comfy/t5_config_xxl.json b/comfy/t5_config_xxl.json
index bf4feadcf..28283b51a 100644
--- a/comfy/t5_config_xxl.json
+++ b/comfy/t5_config_xxl.json
@@ -8,6 +8,7 @@
   "dense_act_fn": "gelu_pytorch_tanh",
   "initializer_factor": 1.0,
   "is_encoder_decoder": true,
+  "is_gated_act": true,
   "layer_norm_epsilon": 1e-06,
   "model_type": "t5",
   "num_decoder_layers": 24,

From 404049114913f1a134656769a538779d7c927249 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 00:53:33 -0400
Subject: [PATCH 171/315] Better T5xxl detection.

---
 comfy/sd.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index e343e1fa8..b39230bd7 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -432,9 +432,11 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             clip_target.clip = sd2_clip.SD2ClipModel
             clip_target.tokenizer = sd2_clip.SD2Tokenizer
         elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
-            dtype_t5 = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"].dtype
-            clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
-            clip_target.tokenizer = sd3_clip.SD3Tokenizer
+            weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
+            dtype_t5 = weight.dtype
+            if weight.shape[-1] == 4096:
+                clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
+                clip_target.tokenizer = sd3_clip.SD3Tokenizer
         elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
             clip_target.clip = sa_t5.SAT5Model
             clip_target.tokenizer = sa_t5.SAT5Tokenizer

From ff63893d103d9c3168eaf9101fb3cebbc2abd8fa Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 02:42:53 -0400
Subject: [PATCH 172/315] Support other types of T5 models.

---
 comfy/t5.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/comfy/t5.py b/comfy/t5.py
index d00b560a3..448c5aad3 100644
--- a/comfy/t5.py
+++ b/comfy/t5.py
@@ -190,11 +190,11 @@ class T5Block(torch.nn.Module):
         return x, past_bias
 
 class T5Stack(torch.nn.Module):
-    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, dtype, device, operations):
+    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations):
         super().__init__()
 
         self.block = torch.nn.ModuleList(
-            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=(i == 0), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
+            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
         )
         self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
         # self.dropout = nn.Dropout(config.dropout_rate)
@@ -223,7 +223,7 @@ class T5(torch.nn.Module):
         self.num_layers = config_dict["num_layers"]
         model_dim = config_dict["d_model"]
 
-        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], dtype, device, operations)
+        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] == "t5", dtype, device, operations)
         self.dtype = dtype
         self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
 

From 2dc84d14447782683862616eaf8c19c0c1feacf3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 04:06:03 -0400
Subject: [PATCH 173/315] Add a way to set the timestep multiplier in the flow
 sampling.

---
 comfy/model_sampling.py | 11 ++++++-----
 1 file changed, 6 insertions(+), 5 deletions(-)

diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index 6bd3a5d79..2d95a83dc 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -190,11 +190,12 @@ class ModelSamplingDiscreteFlow(torch.nn.Module):
         else:
             sampling_settings = {}
 
-        self.set_parameters(shift=sampling_settings.get("shift", 1.0))
+        self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000))
 
-    def set_parameters(self, shift=1.0, timesteps=1000):
+    def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000):
         self.shift = shift
-        ts = self.sigma(torch.arange(1, timesteps + 1, 1))
+        self.multiplier = multiplier
+        ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier)
         self.register_buffer('sigmas', ts)
 
     @property
@@ -206,10 +207,10 @@ class ModelSamplingDiscreteFlow(torch.nn.Module):
         return self.sigmas[-1]
 
     def timestep(self, sigma):
-        return sigma * 1000
+        return sigma * self.multiplier
 
     def sigma(self, timestep):
-        return time_snr_shift(self.shift, timestep / 1000)
+        return time_snr_shift(self.shift, timestep / self.multiplier)
 
     def percent_to_sigma(self, percent):
         if percent <= 0.0:

From 628f0b8ebc2c9a51205e5e5a9973f8db348a310f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 7 Jul 2024 09:22:32 -0400
Subject: [PATCH 174/315] Move audio nodes out of _for_testing.

---
 comfy_extras/nodes_audio.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 9950d58ed..64d241e98 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -19,7 +19,7 @@ class EmptyLatentAudio:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "latent/audio"
 
     def generate(self, seconds):
         batch_size = 1
@@ -34,7 +34,7 @@ class VAEEncodeAudio:
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "encode"
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "latent/audio"
 
     def encode(self, vae, audio):
         sample_rate = audio["sample_rate"]
@@ -53,7 +53,7 @@ class VAEDecodeAudio:
     RETURN_TYPES = ("AUDIO",)
     FUNCTION = "decode"
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "latent/audio"
 
     def decode(self, vae, samples):
         audio = vae.decode(samples["samples"]).movedim(-1, 1)
@@ -132,7 +132,7 @@ class SaveAudio:
 
     OUTPUT_NODE = True
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "audio"
 
     def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
         filename_prefix += self.prefix_append
@@ -195,7 +195,7 @@ class LoadAudio:
         ]
         return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
 
-    CATEGORY = "_for_testing/audio"
+    CATEGORY = "audio"
 
     RETURN_TYPES = ("AUDIO", )
     FUNCTION = "load"
@@ -203,7 +203,6 @@ class LoadAudio:
     def load(self, audio):
         audio_path = folder_paths.get_annotated_filepath(audio)
         waveform, sample_rate = torchaudio.load(audio_path)
-        multiplier = 1.0
         audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
         return (audio, )
 

From bb663bcd6c81a4395100c5f8527a221037d366f8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 8 Jul 2024 08:48:38 -0400
Subject: [PATCH 175/315] Rename clip_t5base to t5base for stable audio text
 encoder.

---
 comfy/sa_t5.py    |  2 +-
 comfy/sd1_clip.py | 12 +++++++++---
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/comfy/sa_t5.py b/comfy/sa_t5.py
index 37be5287e..acc302f67 100644
--- a/comfy/sa_t5.py
+++ b/comfy/sa_t5.py
@@ -19,4 +19,4 @@ class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
 
 class SAT5Model(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, **kwargs):
-        super().__init__(device=device, dtype=dtype, clip_name="t5base", clip_model=T5BaseModel, **kwargs)
+        super().__init__(device=device, dtype=dtype, name="t5base", clip_model=T5BaseModel, **kwargs)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index ed3dc2298..0fe1f1d13 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -514,10 +514,16 @@ class SD1Tokenizer:
 
 
 class SD1ClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, **kwargs):
+    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, name=None, **kwargs):
         super().__init__()
-        self.clip_name = clip_name
-        self.clip = "clip_{}".format(self.clip_name)
+
+        if name is not None:
+            self.clip_name = name
+            self.clip = "{}".format(self.clip_name)
+        else:
+            self.clip_name = clip_name
+            self.clip = "clip_{}".format(self.clip_name)
+
         setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs))
 
         self.dtypes = set()

From faa57430b0ff882275b1afcf6610e8e9f8a5929b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 8 Jul 2024 23:26:34 -0400
Subject: [PATCH 176/315] Controlnet union model basic implementation.

This is only the model code itself, it currently defaults to an empty
embedding [0] * 6 which seems to work better than treating it like a
regular controlnet.

TODO: Add nodes to select the image type.
---
 comfy/cldm/cldm.py  | 113 +++++++++++++++++++++++++++++++++++++++++++-
 comfy/controlnet.py |   6 +++
 2 files changed, 118 insertions(+), 1 deletion(-)

diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index f8a161594..3feb9bab0 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -13,7 +13,47 @@ from ..ldm.modules.diffusionmodules.util import (
 from ..ldm.modules.attention import SpatialTransformer
 from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
 from ..ldm.util import exists
+from ..ldm.cascade.common import OptimizedAttention
+from collections import OrderedDict
 import comfy.ops
+from comfy.ldm.modules.attention import optimized_attention
+
+class OptimizedAttention(nn.Module):
+    def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.heads = nhead
+        self.c = c
+
+        self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device)
+        self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x):
+        x = self.in_proj(x)
+        q, k, v = x.split(self.c, dim=2)
+        out = optimized_attention(q, k, v, self.heads)
+        return self.out_proj(out)
+
+class QuickGELU(nn.Module):
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+class ResBlockUnionControlnet(nn.Module):
+    def __init__(self, dim, nhead, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations)
+        self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device)
+        self.mlp = nn.Sequential(
+            OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()),
+                         ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))]))
+        self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device)
+
+    def attention(self, x: torch.Tensor):
+        return self.attn(x)
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
 
 class ControlledUnetModel(UNetModel):
     #implemented in the ldm unet
@@ -53,6 +93,7 @@ class ControlNet(nn.Module):
         transformer_depth_middle=None,
         transformer_depth_output=None,
         attn_precision=None,
+        union_controlnet=False,
         device=None,
         operations=comfy.ops.disable_weight_init,
         **kwargs,
@@ -280,6 +321,65 @@ class ControlNet(nn.Module):
         self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
         self._feature_size += ch
 
+        if union_controlnet:
+            self.num_control_type = 6
+            num_trans_channel = 320
+            num_trans_head = 8
+            num_trans_layer = 1
+            num_proj_channel = 320
+            # task_scale_factor = num_trans_channel ** 0.5
+            self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device))
+
+            self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)])
+            self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device)
+            #-----------------------------------------------------------------------------------------------------
+
+            control_add_embed_dim = 256
+            class ControlAddEmbedding(nn.Module):
+                def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None):
+                    super().__init__()
+                    self.num_control_type = num_control_type
+                    self.in_dim = in_dim
+                    self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device)
+                    self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device)
+                def forward(self, control_type, dtype, device):
+                    c_type = torch.zeros((self.num_control_type,), device=device)
+                    c_type[control_type] = 1.0
+                    c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim))
+                    return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type)))
+
+            self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations)
+        else:
+            self.task_embedding = None
+            self.control_add_embedding = None
+
+    def union_controlnet_merge(self, hint, control_type, emb, context):
+        # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main
+        inputs = []
+        condition_list = []
+
+        for idx in range(min(1, len(control_type))):
+            controlnet_cond = self.input_hint_block(hint[idx], emb, context)
+            feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
+            if idx < len(control_type):
+                feat_seq += self.task_embedding[control_type[idx]]
+
+            inputs.append(feat_seq.unsqueeze(1))
+            condition_list.append(controlnet_cond)
+
+        x = torch.cat(inputs, dim=1)
+        x = self.transformer_layes(x)
+        controlnet_cond_fuser = None
+        for idx in range(len(control_type)):
+            alpha = self.spatial_ch_projs(x[:, idx])
+            alpha = alpha.unsqueeze(-1).unsqueeze(-1)
+            o = condition_list[idx] + alpha
+            if controlnet_cond_fuser is None:
+                controlnet_cond_fuser = o
+            else:
+                controlnet_cond_fuser += o
+        return controlnet_cond_fuser
+
     def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
         return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
 
@@ -287,7 +387,18 @@ class ControlNet(nn.Module):
         t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
-        guided_hint = self.input_hint_block(hint, emb, context)
+        guided_hint = None
+        if self.control_add_embedding is not None:
+            control_type = kwargs.get("control_type", [])
+
+            emb += self.control_add_embedding(control_type, emb.dtype, emb.device)
+            if len(control_type) > 0:
+                if len(hint.shape) < 5:
+                    hint = hint.unsqueeze(dim=0)
+                guided_hint = self.union_controlnet_merge(hint, control_type, emb, context)
+
+        if guided_hint is None:
+            guided_hint = self.input_hint_block(hint, emb, context)
 
         out_output = []
         out_middle = []
diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index d00395139..84286f1fa 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -413,6 +413,12 @@ def load_controlnet(ckpt_path, model=None):
             if k in controlnet_data:
                 new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
 
+        if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet
+            controlnet_config["union_controlnet"] = True
+            for k in list(controlnet_data.keys()):
+                new_k = k.replace('.attn.in_proj_', '.attn.in_proj.')
+                new_sd[new_k] = controlnet_data.pop(k)
+
         leftover_keys = controlnet_data.keys()
         if len(leftover_keys) > 0:
             logging.warning("leftover keys: {}".format(leftover_keys))

From ade7aa1b0ce7944f50f66af5d8742299ac49d07f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 9 Jul 2024 11:05:05 -0400
Subject: [PATCH 177/315] Remove useless import.

---
 comfy/cldm/cldm.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index 3feb9bab0..d4d32b87e 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -13,7 +13,6 @@ from ..ldm.modules.diffusionmodules.util import (
 from ..ldm.modules.attention import SpatialTransformer
 from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
 from ..ldm.util import exists
-from ..ldm.cascade.common import OptimizedAttention
 from collections import OrderedDict
 import comfy.ops
 from comfy.ldm.modules.attention import optimized_attention
@@ -388,7 +387,7 @@ class ControlNet(nn.Module):
         emb = self.time_embed(t_emb)
 
         guided_hint = None
-        if self.control_add_embedding is not None:
+        if self.control_add_embedding is not None: #Union Controlnet
             control_type = kwargs.get("control_type", [])
 
             emb += self.control_add_embedding(control_type, emb.dtype, emb.device)

From d160073829b836fce97046ebb5f145fa1c677ae3 Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Tue, 9 Jul 2024 08:23:26 -0700
Subject: [PATCH 178/315] Fix loadGraphData call during restore (#3976)

---
 web/scripts/app.js | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/web/scripts/app.js b/web/scripts/app.js
index 67e488bf9..8b4478a32 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -1599,7 +1599,7 @@ export class ComfyApp {
 				if (json) {
 					const workflow = JSON.parse(json);
 					const workflowName = getStorageValue("Comfy.PreviousWorkflow");
-					await this.loadGraphData(workflow, true, workflowName);
+					await this.loadGraphData(workflow, true, true, workflowName);
 					return true;
 				}
 			};

From c3db344746163f9ab942591020043d47ca34e4f0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 9 Jul 2024 11:52:31 -0400
Subject: [PATCH 179/315] Fix ConditioningZeroOut when there is no pooled
 output.

---
 nodes.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/nodes.py b/nodes.py
index 9c541eba8..a230f725b 100644
--- a/nodes.py
+++ b/nodes.py
@@ -232,8 +232,9 @@ class ConditioningZeroOut:
         c = []
         for t in conditioning:
             d = t[1].copy()
-            if "pooled_output" in d:
-                d["pooled_output"] = torch.zeros_like(d["pooled_output"])
+            pooled_output = d.get("pooled_output", None)
+            if pooled_output is not None:
+                d["pooled_output"] = torch.zeros_like(pooled_output)
             n = [torch.zeros_like(t[0]), d]
             c.append(n)
         return (c, )

From f1a01c2c7e2678f34c72212d2a8640237f31fa43 Mon Sep 17 00:00:00 2001
From: Extraltodeus <extraltodeus@gmail.com>
Date: Tue, 9 Jul 2024 22:20:49 +0200
Subject: [PATCH 180/315] Add sampler_pre_cfg_function (#3979)

* Update samplers.py

* Update model_patcher.py
---
 comfy/model_patcher.py | 9 +++++++++
 comfy/samplers.py      | 6 ++++++
 2 files changed, 15 insertions(+)

diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index b949031e9..efac251ca 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -57,6 +57,12 @@ def set_model_options_post_cfg_function(model_options, post_cfg_function, disabl
         model_options["disable_cfg1_optimization"] = True
     return model_options
 
+def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False):
+    model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function]
+    if disable_cfg1_optimization:
+        model_options["disable_cfg1_optimization"] = True
+    return model_options
+
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
         self.size = size
@@ -130,6 +136,9 @@ class ModelPatcher:
     def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
         self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization)
 
+    def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False):
+        self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization)
+
     def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
         self.model_options["model_function_wrapper"] = unet_wrapper_function
 
diff --git a/comfy/samplers.py b/comfy/samplers.py
index c0aa12916..bbf9219f7 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -275,6 +275,12 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
 
     conds = [cond, uncond_]
     out = calc_cond_batch(model, conds, x, timestep, model_options)
+
+    for fn in model_options.get("sampler_pre_cfg_function", []):
+        args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
+                "input": x, "sigma": timestep, "model": model, "model_options": model_options}
+        out  = fn(args)
+
     return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
 
 

From 83f70a88fbc4866050dae90d78ec66aebe8e341a Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 9 Jul 2024 17:07:15 -0400
Subject: [PATCH 181/315] Add __module__ to node info (#3936)

Use more explicit name 'python_module'

Parse abs ath

Move parse to nodes.py
---
 nodes.py  | 29 ++++++++++++++++++++++++++---
 server.py |  1 +
 2 files changed, 27 insertions(+), 3 deletions(-)

diff --git a/nodes.py b/nodes.py
index a230f725b..8d8b153f8 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1889,7 +1889,29 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 EXTENSION_WEB_DIRS = {}
 
 
-def load_custom_node(module_path, ignore=set()):
+def get_relative_module_name(module_path: str) -> str:
+    """
+    Returns the module name based on the given module path.
+    Examples:
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "custom_nodes.my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes.my
+    Args:
+        module_path (str): The path of the module.
+    Returns:
+        str: The module name.
+    """
+    relative_path = os.path.relpath(module_path, folder_paths.base_path)
+    if os.path.isfile(module_path):
+        relative_path = os.path.splitext(relative_path)[0]
+    return relative_path.replace(os.sep, '.')
+
+
+def load_custom_node(module_path: str, ignore=set()) -> bool:
     module_name = os.path.basename(module_path)
     if os.path.isfile(module_path):
         sp = os.path.splitext(module_path)
@@ -1913,9 +1935,10 @@ def load_custom_node(module_path, ignore=set()):
                 EXTENSION_WEB_DIRS[module_name] = web_dir
 
         if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
-            for name in module.NODE_CLASS_MAPPINGS:
+            for name, node_cls in module.NODE_CLASS_MAPPINGS.items():
                 if name not in ignore:
-                    NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name]
+                    NODE_CLASS_MAPPINGS[name] = node_cls
+                    node_cls.RELATIVE_PYTHON_MODULE = get_relative_module_name(module_path)
             if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
                 NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
             return True
diff --git a/server.py b/server.py
index 38b1bab80..ce7c7532d 100644
--- a/server.py
+++ b/server.py
@@ -416,6 +416,7 @@ class PromptServer():
             info['name'] = node_class
             info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class
             info['description'] = obj_class.DESCRIPTION if hasattr(obj_class,'DESCRIPTION') else ''
+            info['python_module'] = getattr(obj_class, "RELATIVE_PYTHON_MODULE", "nodes")
             info['category'] = 'sd'
             if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True:
                 info['output_node'] = True

From 8d3f979b633dab9824c59bf8a06c748a533eec0b Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 9 Jul 2024 17:12:57 -0400
Subject: [PATCH 182/315] Check unhandled exception in test log in test action
 (#3987)

* Upload console logs

* Check unhandled exception
---
 .github/workflows/test-browser.yml | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml
index 65483b00f..7beb0c696 100644
--- a/.github/workflows/test-browser.yml
+++ b/.github/workflows/test-browser.yml
@@ -42,7 +42,7 @@ jobs:
       working-directory: ComfyUI
     - name: Start ComfyUI server
       run: |
-        python main.py --cpu &
+        python main.py --cpu 2>&1 | tee console_output.log &
         wait-for-it --service 127.0.0.1:8188 -t 600
       working-directory: ComfyUI
     - name: Install ComfyUI_frontend dependencies
@@ -55,9 +55,22 @@ jobs:
     - name: Run Playwright tests
       run: npx playwright test
       working-directory: ComfyUI_frontend
+    - name: Check for unhandled exceptions in server log
+      run: |
+        if grep -qE "Exception|Error" console_output.log; then
+          echo "Unhandled exception/error found in server log."
+          exit 1
+        fi
+      working-directory: ComfyUI
     - uses: actions/upload-artifact@v4
       if: always()
       with:
         name: playwright-report
         path: ComfyUI_frontend/playwright-report/
         retention-days: 30
+    - uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: console-output
+        path: ComfyUI/console_output.log
+        retention-days: 30

From 90389b3b8a69c08c3ed0bcc9d87a92246578a8e3 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 10 Jul 2024 11:28:15 -0400
Subject: [PATCH 183/315] Update bug issue template (#3996)

* Update issue template

* nit
---
 .github/ISSUE_TEMPLATE/bug-report.yml | 83 ++++++++++++++-------------
 1 file changed, 43 insertions(+), 40 deletions(-)

diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml
index 87bef3681..39d1992d7 100644
--- a/.github/ISSUE_TEMPLATE/bug-report.yml
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -1,45 +1,48 @@
 name: Bug Report
 description: "Something is broken inside of ComfyUI. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)"
-labels: [ "Potential Bug" ]
+labels: ["Potential Bug"]
 body:
-    - type: markdown
-      attributes:
-        value: |
-                Before submitting a **Bug Report**, please ensure the following:
+  - type: markdown
+    attributes:
+      value: |
+        Before submitting a **Bug Report**, please ensure the following:
 
-                **1:** You are running the latest version of ComfyUI.
-                **2:** You have looked at the existing bug reports and made sure this isn't already reported.
-                **3:** This is an actual bug in ComfyUI, not just a support question and not caused by an custom node. A bug is when you can specify exact steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
+        - **1:** You are running the latest version of ComfyUI.
+        - **2:** You have looked at the existing bug reports and made sure this isn't already reported.
+        - **3:** You confirmed that the bug is not caused by a custom node. You can disable all custom nodes by passing
+        `--disable-all-custom-nodes` command line argument.
+        - **4:** This is an actual bug in ComfyUI, not just a support question. A bug is when you can specify exact
+        steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
 
-                If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
-    - type: textarea
-      attributes:
-            label: Expected Behavior
-            description: "What you expected to happen."
-      validations:
-            required: true
-    - type: textarea
-      attributes:
-                label: Actual Behavior
-                description: "What actually happened. Please include a screenshot of the issue if possible."
-      validations:
-                required: true
-    - type: textarea
-      attributes:
-                label: Steps to Reproduce
-                description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author."
-      validations:
-                required: true
-    - type: textarea
-      attributes:
-                label: Debug Logs
-                description: "Please copy the output from your terminal logs here."
-                render: powershell
-      validations:
-                required: true
-    - type: textarea
-      attributes:
-                label: Other
-                description: "Any other additional information you think might be helpful."
-      validations:
-                required: false
+        If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+  - type: textarea
+    attributes:
+      label: Expected Behavior
+      description: "What you expected to happen."
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Actual Behavior
+      description: "What actually happened. Please include a screenshot of the issue if possible."
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Steps to Reproduce
+      description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author."
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Debug Logs
+      description: "Please copy the output from your terminal logs here."
+      render: powershell
+    validations:
+      required: true
+  - type: textarea
+    attributes:
+      label: Other
+      description: "Any other additional information you think might be helpful."
+    validations:
+      required: false

From e44fa5667fd0a5469b1b5efa08187282779f4d44 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 10 Jul 2024 19:31:22 -0400
Subject: [PATCH 184/315] Support returning text encoder attention masks.

---
 comfy/sd1_clip.py | 19 ++++++++++++++-----
 1 file changed, 14 insertions(+), 5 deletions(-)

diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 0fe1f1d13..4da2b46fd 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -38,7 +38,9 @@ class ClipTokenWeightEncoder:
         if has_weights or sections == 0:
             to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
 
-        out, pooled = self.encode(to_encode)
+        o = self.encode(to_encode)
+        out, pooled = o[:2]
+
         if pooled is not None:
             first_pooled = pooled[0:1].to(model_management.intermediate_device())
         else:
@@ -57,8 +59,11 @@ class ClipTokenWeightEncoder:
             output.append(z)
 
         if (len(output) == 0):
-            return out[-1:].to(model_management.intermediate_device()), first_pooled
-        return torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled
+            r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
+        else:
+            r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
+        r = r + tuple(map(lambda a: a[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device()), o[2:]))
+        return r
 
 class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     """Uses the CLIP transformer encoder for text (from huggingface)"""
@@ -70,7 +75,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
                  special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
-                 return_projected_pooled=True):  # clip-vit-base-patch32
+                 return_projected_pooled=True, return_attention_masks=False):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
 
@@ -96,6 +101,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
         self.layer_norm_hidden_state = layer_norm_hidden_state
         self.return_projected_pooled = return_projected_pooled
+        self.return_attention_masks = return_attention_masks
 
         if layer == "hidden":
             assert layer_idx is not None
@@ -169,7 +175,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         tokens = torch.LongTensor(tokens).to(device)
 
         attention_mask = None
-        if self.enable_attention_masks or self.zero_out_masked:
+        if self.enable_attention_masks or self.zero_out_masked or self.return_attention_masks:
             attention_mask = torch.zeros_like(tokens)
             end_token = self.special_tokens.get("end", -1)
             for x in range(attention_mask.shape[0]):
@@ -200,6 +206,9 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             elif outputs[2] is not None:
                 pooled_output = outputs[2].float()
 
+        if self.return_attention_masks:
+            return z, pooled_output, attention_mask
+
         return z, pooled_output
 
     def encode(self, tokens):

From 391c1046cff8a3877a2ba343057579ab4278c5b1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 10 Jul 2024 20:06:50 -0400
Subject: [PATCH 185/315] More flexibility with text encoder return values.

Text encoders can now return other values to the CONDITIONING than the cond
and pooled output.
---
 comfy/sd.py       | 12 ++++++++++--
 comfy/sd1_clip.py | 21 +++++++++++++++++----
 nodes.py          |  5 +++--
 3 files changed, 30 insertions(+), 8 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index b39230bd7..25454f836 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -130,7 +130,7 @@ class CLIP:
     def tokenize(self, text, return_word_ids=False):
         return self.tokenizer.tokenize_with_weights(text, return_word_ids)
 
-    def encode_from_tokens(self, tokens, return_pooled=False):
+    def encode_from_tokens(self, tokens, return_pooled=False, return_dict=False):
         self.cond_stage_model.reset_clip_options()
 
         if self.layer_idx is not None:
@@ -140,7 +140,15 @@ class CLIP:
             self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
         self.load_model()
-        cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
+        o = self.cond_stage_model.encode_token_weights(tokens)
+        cond, pooled = o[:2]
+        if return_dict:
+            out = {"cond": cond, "pooled_output": pooled}
+            if len(o) > 2:
+                for k in o[2]:
+                    out[k] = o[2][k]
+            return out
+
         if return_pooled:
             return cond, pooled
         return cond
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 4da2b46fd..565ad69da 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -62,7 +62,16 @@ class ClipTokenWeightEncoder:
             r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
         else:
             r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
-        r = r + tuple(map(lambda a: a[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device()), o[2:]))
+
+        if len(o) > 2:
+            extra = {}
+            for k in o[2]:
+                v = o[2][k]
+                if k == "attention_mask":
+                    v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
+                extra[k] = v
+
+            r = r + (extra,)
         return r
 
 class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
@@ -206,8 +215,12 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
             elif outputs[2] is not None:
                 pooled_output = outputs[2].float()
 
+        extra = {}
         if self.return_attention_masks:
-            return z, pooled_output, attention_mask
+            extra["attention_mask"] = attention_mask
+
+        if len(extra) > 0:
+            return z, pooled_output, extra
 
         return z, pooled_output
 
@@ -547,8 +560,8 @@ class SD1ClipModel(torch.nn.Module):
 
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs = token_weight_pairs[self.clip_name]
-        out, pooled = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
-        return out, pooled
+        out = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
+        return out
 
     def load_sd(self, sd):
         return getattr(self, self.clip).load_sd(sd)
diff --git a/nodes.py b/nodes.py
index 8d8b153f8..5778f0609 100644
--- a/nodes.py
+++ b/nodes.py
@@ -55,8 +55,9 @@ class CLIPTextEncode:
 
     def encode(self, clip, text):
         tokens = clip.tokenize(text)
-        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
-        return ([[cond, {"pooled_output": pooled}]], )
+        output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
+        cond = output.pop("cond")
+        return ([[cond, output]], )
 
 class ConditioningCombine:
     @classmethod

From ffe0bb0a33a8a8d9a999dafb540558646127d443 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 10 Jul 2024 20:33:12 -0400
Subject: [PATCH 186/315] Remove useless code.

---
 comfy/sd.py | 30 ------------------------------
 1 file changed, 30 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 25454f836..d0832ad3a 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -28,36 +28,6 @@ import comfy.t2i_adapter.adapter
 import comfy.supported_models_base
 import comfy.taesd.taesd
 
-def load_model_weights(model, sd):
-    m, u = model.load_state_dict(sd, strict=False)
-    m = set(m)
-    unexpected_keys = set(u)
-
-    k = list(sd.keys())
-    for x in k:
-        if x not in unexpected_keys:
-            w = sd.pop(x)
-            del w
-    if len(m) > 0:
-        logging.warning("missing {}".format(m))
-    return model
-
-def load_clip_weights(model, sd):
-    k = list(sd.keys())
-    for x in k:
-        if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
-            y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
-            sd[y] = sd.pop(x)
-
-    if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in sd:
-        ids = sd['cond_stage_model.transformer.text_model.embeddings.position_ids']
-        if ids.dtype == torch.float32:
-            sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
-
-    sd = comfy.utils.clip_text_transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.")
-    return load_model_weights(model, sd)
-
-
 def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
     key_map = {}
     if model is not None:

From 5e1fced6392ca4fef404e01094fbcbec77bdd31f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 11:37:31 -0400
Subject: [PATCH 187/315] Cleaner support for loading different diffusion model
 types.

---
 comfy/model_detection.py | 5 +++++
 comfy/sd.py              | 8 ++------
 2 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 0b678480f..783a03eb7 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -105,6 +105,9 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config["audio_model"] = "dit1.0"
         return unet_config
 
+    if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
+        return None
+
     unet_config = {
         "use_checkpoint": False,
         "image_size": 32,
@@ -239,6 +242,8 @@ def model_config_from_unet_config(unet_config, state_dict=None):
 
 def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False):
     unet_config = detect_unet_config(state_dict, unet_key_prefix)
+    if unet_config is None:
+        return None
     model_config = model_config_from_unet_config(unet_config, state_dict)
     if model_config is None and use_base_if_no_match:
         return comfy.supported_models_base.BASE(unet_config)
diff --git a/comfy/sd.py b/comfy/sd.py
index d0832ad3a..66033b63e 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -546,21 +546,17 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
 def load_unet_state_dict(sd): #load unet in diffusers or regular format
 
     #Allow loading unets from checkpoint files
-    checkpoint = False
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
     temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
     if len(temp_sd) > 0:
         sd = temp_sd
-        checkpoint = True
 
     parameters = comfy.utils.calculate_parameters(sd)
     unet_dtype = model_management.unet_dtype(model_params=parameters)
     load_device = model_management.get_torch_device()
+    model_config = model_detection.model_config_from_unet(sd, "")
 
-    if checkpoint or "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
-        model_config = model_detection.model_config_from_unet(sd, "")
-        if model_config is None:
-            return None
+    if model_config is not None:
         new_sd = sd
     elif 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
         new_sd = model_detection.convert_diffusers_mmdit(sd, "")

From f45157e3acbef1c0edf7d31fc73ee83e7913a994 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 11:46:51 -0400
Subject: [PATCH 188/315] Fix error message never being shown.

---
 comfy/sd.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 66033b63e..eeffa423a 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -489,13 +489,13 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     load_device = model_management.get_torch_device()
 
     model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix)
+    if model_config is None:
+        raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
+
     unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
 
-    if model_config is None:
-        raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
-
     if model_config.clip_vision_prefix is not None:
         if output_clipvision:
             clipvision = clip_vision.load_clipvision_from_sd(sd, model_config.clip_vision_prefix, True)

From 9f291d75b344d7f7e83b7bd18447f430c99fcb28 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 16:51:06 -0400
Subject: [PATCH 189/315] AuraFlow model implementation.

---
 comfy/ldm/aura/mmdit.py                       | 479 +++++++++
 comfy/model_base.py                           |  12 +
 comfy/model_detection.py                      |   8 +
 comfy/sd.py                                   |   4 +
 comfy/supported_models.py                     |  24 +-
 comfy/text_encoders/aura_t5.py                |  22 +
 comfy/text_encoders/t5_pile_config_xl.json    |  22 +
 .../t5_pile_tokenizer/added_tokens.json       | 102 ++
 .../t5_pile_tokenizer/special_tokens_map.json | 125 +++
 .../t5_pile_tokenizer/tokenizer.model         | Bin 0 -> 499723 bytes
 .../t5_pile_tokenizer/tokenizer_config.json   | 945 ++++++++++++++++++
 requirements.txt                              |   3 +-
 12 files changed, 1744 insertions(+), 2 deletions(-)
 create mode 100644 comfy/ldm/aura/mmdit.py
 create mode 100644 comfy/text_encoders/aura_t5.py
 create mode 100644 comfy/text_encoders/t5_pile_config_xl.json
 create mode 100644 comfy/text_encoders/t5_pile_tokenizer/added_tokens.json
 create mode 100644 comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json
 create mode 100644 comfy/text_encoders/t5_pile_tokenizer/tokenizer.model
 create mode 100644 comfy/text_encoders/t5_pile_tokenizer/tokenizer_config.json

diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py
new file mode 100644
index 000000000..c465619bd
--- /dev/null
+++ b/comfy/ldm/aura/mmdit.py
@@ -0,0 +1,479 @@
+#AuraFlow MMDiT
+#Originally written by the AuraFlow Authors
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from comfy.ldm.modules.attention import optimized_attention
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        if hidden_dim is None:
+            hidden_dim = 4 * dim
+
+        n_hidden = int(2 * hidden_dim / 3)
+        n_hidden = find_multiple(n_hidden, 256)
+
+        self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+        self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+        self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class MultiHeadLayerNorm(nn.Module):
+    def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
+        # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78
+
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        mean = hidden_states.mean(-1, keepdim=True)
+        variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
+        hidden_states = (hidden_states - mean) * torch.rsqrt(
+            variance + self.variance_epsilon
+        )
+        hidden_states = self.weight.to(torch.float32) * hidden_states
+        return hidden_states.to(input_dtype)
+
+class SingleAttention(nn.Module):
+    def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.n_heads = n_heads
+        self.head_dim = dim // n_heads
+
+        # this is for cond
+        self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        self.q_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+    #@torch.compile()
+    def forward(self, c):
+
+        bsz, seqlen1, _ = c.shape
+
+        q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)
+        q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        q, k = self.q_norm1(q), self.k_norm1(k)
+
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
+        c = self.w1o(output)
+        return c
+
+
+
+class DoubleAttention(nn.Module):
+    def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.n_heads = n_heads
+        self.head_dim = dim // n_heads
+
+        # this is for cond
+        self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        # this is for x
+        self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        self.q_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+        self.q_norm2 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm2 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+
+    #@torch.compile()
+    def forward(self, c, x):
+
+        bsz, seqlen1, _ = c.shape
+        bsz, seqlen2, _ = x.shape
+        seqlen = seqlen1 + seqlen2
+
+        cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)
+        cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        cq, ck = self.q_norm1(cq), self.k_norm1(ck)
+
+        xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)
+        xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xq, xk = self.q_norm2(xq), self.k_norm2(xk)
+
+        # concat all
+        q, k, v = (
+            torch.cat([cq, xq], dim=1),
+            torch.cat([ck, xk], dim=1),
+            torch.cat([cv, xv], dim=1),
+        )
+
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
+
+        c, x = output.split([seqlen1, seqlen2], dim=1)
+        c = self.w1o(c)
+        x = self.w2o(x)
+
+        return c, x
+
+
+class MMDiTBlock(nn.Module):
+    def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        if not is_last:
+            self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+            self.modC = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+            )
+        else:
+            self.modC = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+            )
+
+        self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+        self.modX = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+        self.is_last = is_last
+
+    #@torch.compile()
+    def forward(self, c, x, global_cond, **kwargs):
+
+        cres, xres = c, x
+
+        cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (
+            self.modC(global_cond).chunk(6, dim=1)
+        )
+
+        c = modulate(self.normC1(c), cshift_msa, cscale_msa)
+
+        # xpath
+        xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (
+            self.modX(global_cond).chunk(6, dim=1)
+        )
+
+        x = modulate(self.normX1(x), xshift_msa, xscale_msa)
+
+        # attention
+        c, x = self.attn(c, x)
+
+
+        c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
+        c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))
+        c = cres + c
+
+        x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)
+        x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))
+        x = xres + x
+
+        return c, x
+
+class DiTBlock(nn.Module):
+    # like MMDiTBlock, but it only has X
+    def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+
+        self.modCX = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+        self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+
+    #@torch.compile()
+    def forward(self, cx, global_cond, **kwargs):
+        cxres = cx
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
+            global_cond
+        ).chunk(6, dim=1)
+        cx = modulate(self.norm1(cx), shift_msa, scale_msa)
+        cx = self.attn(cx)
+        cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
+        mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
+        cx = gate_mlp.unsqueeze(1) * mlpout
+
+        cx = cxres + cx
+
+        return cx
+
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = 1000 * torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half) / half
+        ).to(t.device)
+        args = t[:, None] * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        return embedding
+
+    #@torch.compile()
+    def forward(self, t, dtype):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class MMDiT(nn.Module):
+    def __init__(
+        self,
+        in_channels=4,
+        out_channels=4,
+        patch_size=2,
+        dim=3072,
+        n_layers=36,
+        n_double_layers=4,
+        n_heads=12,
+        global_conddim=3072,
+        cond_seq_dim=2048,
+        max_seq=32 * 32,
+        device=None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+
+        self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)
+
+        self.cond_seq_linear = operations.Linear(
+            cond_seq_dim, dim, bias=False, dtype=dtype, device=device
+        )  # linear for something like text sequence.
+        self.init_x_linear = operations.Linear(
+            patch_size * patch_size * in_channels, dim, dtype=dtype, device=device
+        )  # init linear for patchified image.
+
+        self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))
+        self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))
+
+        self.double_layers = nn.ModuleList([])
+        self.single_layers = nn.ModuleList([])
+
+
+        for idx in range(n_double_layers):
+            self.double_layers.append(
+                MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)
+            )
+
+        for idx in range(n_double_layers, n_layers):
+            self.single_layers.append(
+                DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)
+            )
+
+
+        self.final_linear = operations.Linear(
+            dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device
+        )
+
+        self.modF = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.out_channels = out_channels
+        self.patch_size = patch_size
+        self.n_double_layers = n_double_layers
+        self.n_layers = n_layers
+
+        self.h_max = round(max_seq**0.5)
+        self.w_max = round(max_seq**0.5)
+
+    @torch.no_grad()
+    def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
+        # extend pe
+        pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]
+
+        pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)
+
+        # now we need to extend this to target_dim. for this we will use interpolation.
+        # we will use torch.nn.functional.interpolate
+        pe_as_2d = F.interpolate(
+            pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear"
+        )
+        pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
+        self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
+        self.h_max, self.w_max = target_dim
+        print("PE extended to", target_dim)
+
+    def pe_selection_index_based_on_dim(self, h, w):
+        h_p, w_p = h // self.patch_size, w // self.patch_size
+        original_pe_indexes = torch.arange(self.positional_encoding.shape[1])
+        original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)
+        starth =  self.h_max // 2 - h_p // 2
+        endh =starth + h_p
+        startw = self.w_max // 2 - w_p // 2
+        endw = startw + w_p
+        original_pe_indexes = original_pe_indexes[
+            starth:endh, startw:endw
+        ]
+        return original_pe_indexes.flatten()
+
+    def unpatchify(self, x, h, w):
+        c = self.out_channels
+        p = self.patch_size
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum("nhwpqc->nchpwq", x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs
+
+    def patchify(self, x):
+        B, C, H, W = x.size()
+        pad_h = (self.patch_size - H % self.patch_size) % self.patch_size
+        pad_w = (self.patch_size - W % self.patch_size) % self.patch_size
+
+        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
+        x = x.view(
+            B,
+            C,
+            (H + 1) // self.patch_size,
+            self.patch_size,
+            (W + 1) // self.patch_size,
+            self.patch_size,
+        )
+        x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
+        return x
+
+    def apply_pos_embeds(self, x, h, w):
+        h = (h + 1) // self.patch_size
+        w = (w + 1) // self.patch_size
+        max_dim = max(h, w)
+
+        cur_dim = self.h_max
+        pos_encoding = self.positional_encoding.reshape(1, cur_dim, cur_dim, -1).to(device=x.device, dtype=x.dtype)
+
+        if max_dim > cur_dim:
+            pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
+            cur_dim = max_dim
+
+        from_h = (cur_dim - h) // 2
+        from_w = (cur_dim - w) // 2
+        pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
+        return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
+
+    def forward(self, x, timestep, context, **kwargs):
+        # patchify x, add PE
+        b, c, h, w = x.shape
+
+        # pe_indexes = self.pe_selection_index_based_on_dim(h, w)
+        # print(pe_indexes, pe_indexes.shape)
+
+        x = self.init_x_linear(self.patchify(x))  # B, T_x, D
+        x = self.apply_pos_embeds(x, h, w)
+        # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype)
+        # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype)
+
+        # process conditions for MMDiT Blocks
+        c_seq = context  # B, T_c, D_c
+        t = timestep
+
+        c = self.cond_seq_linear(c_seq)  # B, T_c, D
+        c = torch.cat([self.register_tokens.to(device=c.device, dtype=c.dtype).repeat(c.size(0), 1, 1), c], dim=1)
+
+        global_cond = self.t_embedder(t, x.dtype)  # B, D
+
+        if len(self.double_layers) > 0:
+            for layer in self.double_layers:
+                c, x = layer(c, x, global_cond, **kwargs)
+
+        if len(self.single_layers) > 0:
+            c_len = c.size(1)
+            cx = torch.cat([c, x], dim=1)
+            for layer in self.single_layers:
+                cx = layer(cx, global_cond, **kwargs)
+
+            x = cx[:, c_len:]
+
+        fshift, fscale = self.modF(global_cond).chunk(2, dim=1)
+
+        x = modulate(x, fshift, fscale)
+        x = self.final_linear(x)
+        x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
+        return x
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 80f6667ec..0e0e69d3b 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -6,6 +6,7 @@ from comfy.ldm.cascade.stage_b import StageB
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+import comfy.ldm.aura.mmdit
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
 import comfy.model_management
@@ -598,6 +599,17 @@ class SD3(BaseModel):
             area = input_shape[0] * input_shape[2] * input_shape[3]
             return (area * 0.3) * (1024 * 1024)
 
+class AuraFlow(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
+
 
 class StableAudio1(BaseModel):
     def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 783a03eb7..dd5bff82a 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -105,6 +105,12 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config["audio_model"] = "dit1.0"
         return unet_config
 
+    if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
+        unet_config = {}
+        unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
+        unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
+        return unet_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -253,6 +259,8 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
 def unet_prefix_from_state_dict(state_dict):
     if "model.model.postprocess_conv.weight" in state_dict: #audio models
         unet_key_prefix = "model.model."
+    elif "model.double_layers.0.attn.w1q.weight" in state_dict: #aura flow
+        unet_key_prefix = "model."
     else:
         unet_key_prefix = "model.diffusion_model."
     return unet_key_prefix
diff --git a/comfy/sd.py b/comfy/sd.py
index eeffa423a..6028029d6 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -21,6 +21,7 @@ from . import sd2_clip
 from . import sdxl_clip
 from . import sd3_clip
 from . import sa_t5
+import comfy.text_encoders.aura_t5
 
 import comfy.model_patcher
 import comfy.lora
@@ -415,6 +416,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             if weight.shape[-1] == 4096:
                 clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
                 clip_target.tokenizer = sd3_clip.SD3Tokenizer
+            elif weight.shape[-1] == 2048:
+                clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
+                clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
         elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
             clip_target.clip = sa_t5.SAT5Model
             clip_target.tokenizer = sa_t5.SAT5Tokenizer
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 21fdb7ec7..ccf8c333e 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -7,6 +7,7 @@ from . import sd2_clip
 from . import sdxl_clip
 from . import sd3_clip
 from . import sa_t5
+import comfy.text_encoders.aura_t5
 
 from . import supported_models_base
 from . import latent_formats
@@ -556,7 +557,28 @@ class StableAudio(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
 
+class AuraFlow(supported_models_base.BASE):
+    unet_config = {
+        "cond_seq_dim": 2048,
+    }
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio]
+    sampling_settings = {
+        "multiplier": 1.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.SDXL
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.AuraFlow(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow]
 
 models += [SVD_img2vid]
diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py
new file mode 100644
index 000000000..0e84189aa
--- /dev/null
+++ b/comfy/text_encoders/aura_t5.py
@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from transformers import LlamaTokenizerFast
+import comfy.t5
+import os
+
+class PT5XlModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+
+class PT5XlTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=LlamaTokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
+
+class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer)
+
+class AuraT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, **kwargs):
+        super().__init__(device=device, dtype=dtype, name="pile_t5xl", clip_model=PT5XlModel, **kwargs)
diff --git a/comfy/text_encoders/t5_pile_config_xl.json b/comfy/text_encoders/t5_pile_config_xl.json
new file mode 100644
index 000000000..ee4e03f97
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_config_xl.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 2,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "umt5",
+  "num_decoder_layers": 24,
+  "num_heads": 32,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/text_encoders/t5_pile_tokenizer/added_tokens.json b/comfy/text_encoders/t5_pile_tokenizer/added_tokens.json
new file mode 100644
index 000000000..3f5132007
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_tokenizer/added_tokens.json
@@ -0,0 +1,102 @@
+{
+  "<extra_id_0>": 32099,
+  "<extra_id_10>": 32089,
+  "<extra_id_11>": 32088,
+  "<extra_id_12>": 32087,
+  "<extra_id_13>": 32086,
+  "<extra_id_14>": 32085,
+  "<extra_id_15>": 32084,
+  "<extra_id_16>": 32083,
+  "<extra_id_17>": 32082,
+  "<extra_id_18>": 32081,
+  "<extra_id_19>": 32080,
+  "<extra_id_1>": 32098,
+  "<extra_id_20>": 32079,
+  "<extra_id_21>": 32078,
+  "<extra_id_22>": 32077,
+  "<extra_id_23>": 32076,
+  "<extra_id_24>": 32075,
+  "<extra_id_25>": 32074,
+  "<extra_id_26>": 32073,
+  "<extra_id_27>": 32072,
+  "<extra_id_28>": 32071,
+  "<extra_id_29>": 32070,
+  "<extra_id_2>": 32097,
+  "<extra_id_30>": 32069,
+  "<extra_id_31>": 32068,
+  "<extra_id_32>": 32067,
+  "<extra_id_33>": 32066,
+  "<extra_id_34>": 32065,
+  "<extra_id_35>": 32064,
+  "<extra_id_36>": 32063,
+  "<extra_id_37>": 32062,
+  "<extra_id_38>": 32061,
+  "<extra_id_39>": 32060,
+  "<extra_id_3>": 32096,
+  "<extra_id_40>": 32059,
+  "<extra_id_41>": 32058,
+  "<extra_id_42>": 32057,
+  "<extra_id_43>": 32056,
+  "<extra_id_44>": 32055,
+  "<extra_id_45>": 32054,
+  "<extra_id_46>": 32053,
+  "<extra_id_47>": 32052,
+  "<extra_id_48>": 32051,
+  "<extra_id_49>": 32050,
+  "<extra_id_4>": 32095,
+  "<extra_id_50>": 32049,
+  "<extra_id_51>": 32048,
+  "<extra_id_52>": 32047,
+  "<extra_id_53>": 32046,
+  "<extra_id_54>": 32045,
+  "<extra_id_55>": 32044,
+  "<extra_id_56>": 32043,
+  "<extra_id_57>": 32042,
+  "<extra_id_58>": 32041,
+  "<extra_id_59>": 32040,
+  "<extra_id_5>": 32094,
+  "<extra_id_60>": 32039,
+  "<extra_id_61>": 32038,
+  "<extra_id_62>": 32037,
+  "<extra_id_63>": 32036,
+  "<extra_id_64>": 32035,
+  "<extra_id_65>": 32034,
+  "<extra_id_66>": 32033,
+  "<extra_id_67>": 32032,
+  "<extra_id_68>": 32031,
+  "<extra_id_69>": 32030,
+  "<extra_id_6>": 32093,
+  "<extra_id_70>": 32029,
+  "<extra_id_71>": 32028,
+  "<extra_id_72>": 32027,
+  "<extra_id_73>": 32026,
+  "<extra_id_74>": 32025,
+  "<extra_id_75>": 32024,
+  "<extra_id_76>": 32023,
+  "<extra_id_77>": 32022,
+  "<extra_id_78>": 32021,
+  "<extra_id_79>": 32020,
+  "<extra_id_7>": 32092,
+  "<extra_id_80>": 32019,
+  "<extra_id_81>": 32018,
+  "<extra_id_82>": 32017,
+  "<extra_id_83>": 32016,
+  "<extra_id_84>": 32015,
+  "<extra_id_85>": 32014,
+  "<extra_id_86>": 32013,
+  "<extra_id_87>": 32012,
+  "<extra_id_88>": 32011,
+  "<extra_id_89>": 32010,
+  "<extra_id_8>": 32091,
+  "<extra_id_90>": 32009,
+  "<extra_id_91>": 32008,
+  "<extra_id_92>": 32007,
+  "<extra_id_93>": 32006,
+  "<extra_id_94>": 32005,
+  "<extra_id_95>": 32004,
+  "<extra_id_96>": 32003,
+  "<extra_id_97>": 32002,
+  "<extra_id_98>": 32001,
+  "<extra_id_99>": 32000,
+  "<extra_id_9>": 32090
+}
diff --git a/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json b/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json
new file mode 100644
index 000000000..19fb1d5f4
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json
@@ -0,0 +1,125 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_99>",
+    "<extra_id_98>",
+    "<extra_id_97>",
+    "<extra_id_96>",
+    "<extra_id_95>",
+    "<extra_id_94>",
+    "<extra_id_93>",
+    "<extra_id_92>",
+    "<extra_id_91>",
+    "<extra_id_90>",
+    "<extra_id_89>",
+    "<extra_id_88>",
+    "<extra_id_87>",
+    "<extra_id_86>",
+    "<extra_id_85>",
+    "<extra_id_84>",
+    "<extra_id_83>",
+    "<extra_id_82>",
+    "<extra_id_81>",
+    "<extra_id_80>",
+    "<extra_id_79>",
+    "<extra_id_78>",
+    "<extra_id_77>",
+    "<extra_id_76>",
+    "<extra_id_75>",
+    "<extra_id_74>",
+    "<extra_id_73>",
+    "<extra_id_72>",
+    "<extra_id_71>",
+    "<extra_id_70>",
+    "<extra_id_69>",
+    "<extra_id_68>",
+    "<extra_id_67>",
+    "<extra_id_66>",
+    "<extra_id_65>",
+    "<extra_id_64>",
+    "<extra_id_63>",
+    "<extra_id_62>",
+    "<extra_id_61>",
+    "<extra_id_60>",
+    "<extra_id_59>",
+    "<extra_id_58>",
+    "<extra_id_57>",
+    "<extra_id_56>",
+    "<extra_id_55>",
+    "<extra_id_54>",
+    "<extra_id_53>",
+    "<extra_id_52>",
+    "<extra_id_51>",
+    "<extra_id_50>",
+    "<extra_id_49>",
+    "<extra_id_48>",
+    "<extra_id_47>",
+    "<extra_id_46>",
+    "<extra_id_45>",
+    "<extra_id_44>",
+    "<extra_id_43>",
+    "<extra_id_42>",
+    "<extra_id_41>",
+    "<extra_id_40>",
+    "<extra_id_39>",
+    "<extra_id_38>",
+    "<extra_id_37>",
+    "<extra_id_36>",
+    "<extra_id_35>",
+    "<extra_id_34>",
+    "<extra_id_33>",
+    "<extra_id_32>",
+    "<extra_id_31>",
+    "<extra_id_30>",
+    "<extra_id_29>",
+    "<extra_id_28>",
+    "<extra_id_27>",
+    "<extra_id_26>",
+    "<extra_id_25>",
+    "<extra_id_24>",
+    "<extra_id_23>",
+    "<extra_id_22>",
+    "<extra_id_21>",
+    "<extra_id_20>",
+    "<extra_id_19>",
+    "<extra_id_18>",
+    "<extra_id_17>",
+    "<extra_id_16>",
+    "<extra_id_15>",
+    "<extra_id_14>",
+    "<extra_id_13>",
+    "<extra_id_12>",
+    "<extra_id_11>",
+    "<extra_id_10>",
+    "<extra_id_9>",
+    "<extra_id_8>",
+    "<extra_id_7>",
+    "<extra_id_6>",
+    "<extra_id_5>",
+    "<extra_id_4>",
+    "<extra_id_3>",
+    "<extra_id_2>",
+    "<extra_id_1>",
+    "<extra_id_0>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model b/comfy/text_encoders/t5_pile_tokenizer/tokenizer.model
new file mode 100644
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HcmV?d00001

diff --git a/comfy/text_encoders/t5_pile_tokenizer/tokenizer_config.json b/comfy/text_encoders/t5_pile_tokenizer/tokenizer_config.json
new file mode 100644
index 000000000..81f8e11e9
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_tokenizer/tokenizer_config.json
@@ -0,0 +1,945 @@
+{
+  "add_bos_token": false,
+  "add_eos_token": true,
+  "add_prefix_space": true,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<extra_id_99>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<extra_id_98>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32002": {
+      "content": "<extra_id_97>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32003": {
+      "content": "<extra_id_96>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32004": {
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+      "lstrip": false,
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+      "single_word": false,
+      "special": true
+    },
+    "32005": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32006": {
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+      "special": true
+    },
+    "32007": {
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+      "special": true
+    },
+    "32008": {
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+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32009": {
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32010": {
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+    },
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+    },
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+      "single_word": false,
+      "special": true
+    },
+    "32013": {
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+      "lstrip": false,
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+      "special": true
+    },
+    "32014": {
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+      "special": true
+    },
+    "32015": {
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+    },
+    "32016": {
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+    },
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+      "special": true
+    },
+    "32044": {
+      "content": "<extra_id_55>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32045": {
+      "content": "<extra_id_54>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32046": {
+      "content": "<extra_id_53>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32047": {
+      "content": "<extra_id_52>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32048": {
+      "content": "<extra_id_51>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32049": {
+      "content": "<extra_id_50>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32050": {
+      "content": "<extra_id_49>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32051": {
+      "content": "<extra_id_48>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32052": {
+      "content": "<extra_id_47>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32053": {
+      "content": "<extra_id_46>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32054": {
+      "content": "<extra_id_45>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32055": {
+      "content": "<extra_id_44>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32056": {
+      "content": "<extra_id_43>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32057": {
+      "content": "<extra_id_42>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32058": {
+      "content": "<extra_id_41>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32059": {
+      "content": "<extra_id_40>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32060": {
+      "content": "<extra_id_39>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32061": {
+      "content": "<extra_id_38>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32062": {
+      "content": "<extra_id_37>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32063": {
+      "content": "<extra_id_36>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32064": {
+      "content": "<extra_id_35>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32065": {
+      "content": "<extra_id_34>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32066": {
+      "content": "<extra_id_33>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32067": {
+      "content": "<extra_id_32>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32068": {
+      "content": "<extra_id_31>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32069": {
+      "content": "<extra_id_30>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32070": {
+      "content": "<extra_id_29>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32071": {
+      "content": "<extra_id_28>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32072": {
+      "content": "<extra_id_27>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32073": {
+      "content": "<extra_id_26>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32074": {
+      "content": "<extra_id_25>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32075": {
+      "content": "<extra_id_24>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32076": {
+      "content": "<extra_id_23>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32077": {
+      "content": "<extra_id_22>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32078": {
+      "content": "<extra_id_21>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32079": {
+      "content": "<extra_id_20>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32080": {
+      "content": "<extra_id_19>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32081": {
+      "content": "<extra_id_18>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32082": {
+      "content": "<extra_id_17>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32083": {
+      "content": "<extra_id_16>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32084": {
+      "content": "<extra_id_15>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32085": {
+      "content": "<extra_id_14>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32086": {
+      "content": "<extra_id_13>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32087": {
+      "content": "<extra_id_12>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32088": {
+      "content": "<extra_id_11>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32089": {
+      "content": "<extra_id_10>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32090": {
+      "content": "<extra_id_9>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32091": {
+      "content": "<extra_id_8>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32092": {
+      "content": "<extra_id_7>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32093": {
+      "content": "<extra_id_6>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32094": {
+      "content": "<extra_id_5>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32095": {
+      "content": "<extra_id_4>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32096": {
+      "content": "<extra_id_3>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32097": {
+      "content": "<extra_id_2>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32098": {
+      "content": "<extra_id_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32099": {
+      "content": "<extra_id_0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<extra_id_99>",
+    "<extra_id_98>",
+    "<extra_id_97>",
+    "<extra_id_96>",
+    "<extra_id_95>",
+    "<extra_id_94>",
+    "<extra_id_93>",
+    "<extra_id_92>",
+    "<extra_id_91>",
+    "<extra_id_90>",
+    "<extra_id_89>",
+    "<extra_id_88>",
+    "<extra_id_87>",
+    "<extra_id_86>",
+    "<extra_id_85>",
+    "<extra_id_84>",
+    "<extra_id_83>",
+    "<extra_id_82>",
+    "<extra_id_81>",
+    "<extra_id_80>",
+    "<extra_id_79>",
+    "<extra_id_78>",
+    "<extra_id_77>",
+    "<extra_id_76>",
+    "<extra_id_75>",
+    "<extra_id_74>",
+    "<extra_id_73>",
+    "<extra_id_72>",
+    "<extra_id_71>",
+    "<extra_id_70>",
+    "<extra_id_69>",
+    "<extra_id_68>",
+    "<extra_id_67>",
+    "<extra_id_66>",
+    "<extra_id_65>",
+    "<extra_id_64>",
+    "<extra_id_63>",
+    "<extra_id_62>",
+    "<extra_id_61>",
+    "<extra_id_60>",
+    "<extra_id_59>",
+    "<extra_id_58>",
+    "<extra_id_57>",
+    "<extra_id_56>",
+    "<extra_id_55>",
+    "<extra_id_54>",
+    "<extra_id_53>",
+    "<extra_id_52>",
+    "<extra_id_51>",
+    "<extra_id_50>",
+    "<extra_id_49>",
+    "<extra_id_48>",
+    "<extra_id_47>",
+    "<extra_id_46>",
+    "<extra_id_45>",
+    "<extra_id_44>",
+    "<extra_id_43>",
+    "<extra_id_42>",
+    "<extra_id_41>",
+    "<extra_id_40>",
+    "<extra_id_39>",
+    "<extra_id_38>",
+    "<extra_id_37>",
+    "<extra_id_36>",
+    "<extra_id_35>",
+    "<extra_id_34>",
+    "<extra_id_33>",
+    "<extra_id_32>",
+    "<extra_id_31>",
+    "<extra_id_30>",
+    "<extra_id_29>",
+    "<extra_id_28>",
+    "<extra_id_27>",
+    "<extra_id_26>",
+    "<extra_id_25>",
+    "<extra_id_24>",
+    "<extra_id_23>",
+    "<extra_id_22>",
+    "<extra_id_21>",
+    "<extra_id_20>",
+    "<extra_id_19>",
+    "<extra_id_18>",
+    "<extra_id_17>",
+    "<extra_id_16>",
+    "<extra_id_15>",
+    "<extra_id_14>",
+    "<extra_id_13>",
+    "<extra_id_12>",
+    "<extra_id_11>",
+    "<extra_id_10>",
+    "<extra_id_9>",
+    "<extra_id_8>",
+    "<extra_id_7>",
+    "<extra_id_6>",
+    "<extra_id_5>",
+    "<extra_id_4>",
+    "<extra_id_3>",
+    "<extra_id_2>",
+    "<extra_id_1>",
+    "<extra_id_0>"
+  ],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": false,
+  "model_max_length": 512,
+  "pad_token": null,
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}
diff --git a/requirements.txt b/requirements.txt
index 108958d2f..6febc3d68 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,7 +3,8 @@ torchsde
 torchvision
 torchaudio
 einops
-transformers>=4.25.1
+transformers>=4.28.1
+tokenizers>=0.13.3
 safetensors>=0.4.2
 aiohttp
 pyyaml

From 8e012043a9d0af3979bbe2cea8dc1ec7768f9d88 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 17:51:56 -0400
Subject: [PATCH 190/315] Add a ModelSamplingAuraFlow node to change the shift
 value.

Set the default AuraFlow shift value to 1.73 (sqrt(3)).
---
 comfy/supported_models.py            |  1 +
 comfy_extras/nodes_model_advanced.py | 17 +++++++++++++++--
 2 files changed, 16 insertions(+), 2 deletions(-)

diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index ccf8c333e..a030f6229 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -564,6 +564,7 @@ class AuraFlow(supported_models_base.BASE):
 
     sampling_settings = {
         "multiplier": 1.0,
+        "shift": 1.73,
     }
 
     unet_extra_config = {}
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 97559cf56..22ba9547b 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -144,7 +144,7 @@ class ModelSamplingSD3:
 
     CATEGORY = "advanced/model"
 
-    def patch(self, model, shift):
+    def patch(self, model, shift, multiplier=1000):
         m = model.clone()
 
         sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
@@ -154,10 +154,22 @@ class ModelSamplingSD3:
             pass
 
         model_sampling = ModelSamplingAdvanced(model.model.model_config)
-        model_sampling.set_parameters(shift=shift)
+        model_sampling.set_parameters(shift=shift, multiplier=multiplier)
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )
 
+class ModelSamplingAuraFlow(ModelSamplingSD3):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "shift": ("FLOAT", {"default": 1.73, "min": 0.0, "max": 100.0, "step":0.01}),
+                              }}
+
+    FUNCTION = "patch_aura"
+
+    def patch_aura(self, model, shift):
+        return self.patch(model, shift, multiplier=1.0)
+
 class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
@@ -271,5 +283,6 @@ NODE_CLASS_MAPPINGS = {
     "ModelSamplingContinuousV": ModelSamplingContinuousV,
     "ModelSamplingStableCascade": ModelSamplingStableCascade,
     "ModelSamplingSD3": ModelSamplingSD3,
+    "ModelSamplingAuraFlow": ModelSamplingAuraFlow,
     "RescaleCFG": RescaleCFG,
 }

From b6f09cf649a8e06783f148a0f5824405ed460003 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 22:58:03 -0400
Subject: [PATCH 191/315] Add sentencepiece dependency.

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 6febc3d68..4c2c0b2b2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,6 +5,7 @@ torchaudio
 einops
 transformers>=4.28.1
 tokenizers>=0.13.3
+sentencepiece
 safetensors>=0.4.2
 aiohttp
 pyyaml

From 29c2e26724d4982a3e33114eb9064f1a11f4f4ed Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 12 Jul 2024 01:08:45 -0400
Subject: [PATCH 192/315] Better tokenizing code for AuraFlow.

---
 comfy/text_encoders/aura_t5.py                |   6 +-
 comfy/text_encoders/llama_tokenizer.py        |  22 +
 .../t5_pile_tokenizer/added_tokens.json       | 102 --
 .../t5_pile_tokenizer/special_tokens_map.json | 125 ---
 .../t5_pile_tokenizer/tokenizer_config.json   | 945 ------------------
 5 files changed, 25 insertions(+), 1175 deletions(-)
 create mode 100644 comfy/text_encoders/llama_tokenizer.py
 delete mode 100644 comfy/text_encoders/t5_pile_tokenizer/added_tokens.json
 delete mode 100644 comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json
 delete mode 100644 comfy/text_encoders/t5_pile_tokenizer/tokenizer_config.json

diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py
index 0e84189aa..95f942ef5 100644
--- a/comfy/text_encoders/aura_t5.py
+++ b/comfy/text_encoders/aura_t5.py
@@ -1,5 +1,5 @@
 from comfy import sd1_clip
-from transformers import LlamaTokenizerFast
+from .llama_tokenizer import LLAMATokenizer
 import comfy.t5
 import os
 
@@ -10,8 +10,8 @@ class PT5XlModel(sd1_clip.SDClipModel):
 
 class PT5XlTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None):
-        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=LlamaTokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
+        tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=LLAMATokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
 
 class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None):
diff --git a/comfy/text_encoders/llama_tokenizer.py b/comfy/text_encoders/llama_tokenizer.py
new file mode 100644
index 000000000..a6db1da62
--- /dev/null
+++ b/comfy/text_encoders/llama_tokenizer.py
@@ -0,0 +1,22 @@
+import os
+
+class LLAMATokenizer:
+    @staticmethod
+    def from_pretrained(path):
+        return LLAMATokenizer(path)
+
+    def __init__(self, tokenizer_path):
+        import sentencepiece
+        self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path)
+        self.end = self.tokenizer.eos_id()
+
+    def get_vocab(self):
+        out = {}
+        for i in range(self.tokenizer.get_piece_size()):
+            out[self.tokenizer.id_to_piece(i)] = i
+        return out
+
+    def __call__(self, string):
+        out = self.tokenizer.encode(string)
+        out += [self.end]
+        return {"input_ids": out}
diff --git a/comfy/text_encoders/t5_pile_tokenizer/added_tokens.json b/comfy/text_encoders/t5_pile_tokenizer/added_tokens.json
deleted file mode 100644
index 3f5132007..000000000
--- a/comfy/text_encoders/t5_pile_tokenizer/added_tokens.json
+++ /dev/null
@@ -1,102 +0,0 @@
-{
-  "<extra_id_0>": 32099,
-  "<extra_id_10>": 32089,
-  "<extra_id_11>": 32088,
-  "<extra_id_12>": 32087,
-  "<extra_id_13>": 32086,
-  "<extra_id_14>": 32085,
-  "<extra_id_15>": 32084,
-  "<extra_id_16>": 32083,
-  "<extra_id_17>": 32082,
-  "<extra_id_18>": 32081,
-  "<extra_id_19>": 32080,
-  "<extra_id_1>": 32098,
-  "<extra_id_20>": 32079,
-  "<extra_id_21>": 32078,
-  "<extra_id_22>": 32077,
-  "<extra_id_23>": 32076,
-  "<extra_id_24>": 32075,
-  "<extra_id_25>": 32074,
-  "<extra_id_26>": 32073,
-  "<extra_id_27>": 32072,
-  "<extra_id_28>": 32071,
-  "<extra_id_29>": 32070,
-  "<extra_id_2>": 32097,
-  "<extra_id_30>": 32069,
-  "<extra_id_31>": 32068,
-  "<extra_id_32>": 32067,
-  "<extra_id_33>": 32066,
-  "<extra_id_34>": 32065,
-  "<extra_id_35>": 32064,
-  "<extra_id_36>": 32063,
-  "<extra_id_37>": 32062,
-  "<extra_id_38>": 32061,
-  "<extra_id_39>": 32060,
-  "<extra_id_3>": 32096,
-  "<extra_id_40>": 32059,
-  "<extra_id_41>": 32058,
-  "<extra_id_42>": 32057,
-  "<extra_id_43>": 32056,
-  "<extra_id_44>": 32055,
-  "<extra_id_45>": 32054,
-  "<extra_id_46>": 32053,
-  "<extra_id_47>": 32052,
-  "<extra_id_48>": 32051,
-  "<extra_id_49>": 32050,
-  "<extra_id_4>": 32095,
-  "<extra_id_50>": 32049,
-  "<extra_id_51>": 32048,
-  "<extra_id_52>": 32047,
-  "<extra_id_53>": 32046,
-  "<extra_id_54>": 32045,
-  "<extra_id_55>": 32044,
-  "<extra_id_56>": 32043,
-  "<extra_id_57>": 32042,
-  "<extra_id_58>": 32041,
-  "<extra_id_59>": 32040,
-  "<extra_id_5>": 32094,
-  "<extra_id_60>": 32039,
-  "<extra_id_61>": 32038,
-  "<extra_id_62>": 32037,
-  "<extra_id_63>": 32036,
-  "<extra_id_64>": 32035,
-  "<extra_id_65>": 32034,
-  "<extra_id_66>": 32033,
-  "<extra_id_67>": 32032,
-  "<extra_id_68>": 32031,
-  "<extra_id_69>": 32030,
-  "<extra_id_6>": 32093,
-  "<extra_id_70>": 32029,
-  "<extra_id_71>": 32028,
-  "<extra_id_72>": 32027,
-  "<extra_id_73>": 32026,
-  "<extra_id_74>": 32025,
-  "<extra_id_75>": 32024,
-  "<extra_id_76>": 32023,
-  "<extra_id_77>": 32022,
-  "<extra_id_78>": 32021,
-  "<extra_id_79>": 32020,
-  "<extra_id_7>": 32092,
-  "<extra_id_80>": 32019,
-  "<extra_id_81>": 32018,
-  "<extra_id_82>": 32017,
-  "<extra_id_83>": 32016,
-  "<extra_id_84>": 32015,
-  "<extra_id_85>": 32014,
-  "<extra_id_86>": 32013,
-  "<extra_id_87>": 32012,
-  "<extra_id_88>": 32011,
-  "<extra_id_89>": 32010,
-  "<extra_id_8>": 32091,
-  "<extra_id_90>": 32009,
-  "<extra_id_91>": 32008,
-  "<extra_id_92>": 32007,
-  "<extra_id_93>": 32006,
-  "<extra_id_94>": 32005,
-  "<extra_id_95>": 32004,
-  "<extra_id_96>": 32003,
-  "<extra_id_97>": 32002,
-  "<extra_id_98>": 32001,
-  "<extra_id_99>": 32000,
-  "<extra_id_9>": 32090
-}
diff --git a/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json b/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json
deleted file mode 100644
index 19fb1d5f4..000000000
--- a/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json
+++ /dev/null
@@ -1,125 +0,0 @@
-{
-  "additional_special_tokens": [
-    "<extra_id_99>",
-    "<extra_id_98>",
-    "<extra_id_97>",
-    "<extra_id_96>",
-    "<extra_id_95>",
-    "<extra_id_94>",
-    "<extra_id_93>",
-    "<extra_id_92>",
-    "<extra_id_91>",
-    "<extra_id_90>",
-    "<extra_id_89>",
-    "<extra_id_88>",
-    "<extra_id_87>",
-    "<extra_id_86>",
-    "<extra_id_85>",
-    "<extra_id_84>",
-    "<extra_id_83>",
-    "<extra_id_82>",
-    "<extra_id_81>",
-    "<extra_id_80>",
-    "<extra_id_79>",
-    "<extra_id_78>",
-    "<extra_id_77>",
-    "<extra_id_76>",
-    "<extra_id_75>",
-    "<extra_id_74>",
-    "<extra_id_73>",
-    "<extra_id_72>",
-    "<extra_id_71>",
-    "<extra_id_70>",
-    "<extra_id_69>",
-    "<extra_id_68>",
-    "<extra_id_67>",
-    "<extra_id_66>",
-    "<extra_id_65>",
-    "<extra_id_64>",
-    "<extra_id_63>",
-    "<extra_id_62>",
-    "<extra_id_61>",
-    "<extra_id_60>",
-    "<extra_id_59>",
-    "<extra_id_58>",
-    "<extra_id_57>",
-    "<extra_id_56>",
-    "<extra_id_55>",
-    "<extra_id_54>",
-    "<extra_id_53>",
-    "<extra_id_52>",
-    "<extra_id_51>",
-    "<extra_id_50>",
-    "<extra_id_49>",
-    "<extra_id_48>",
-    "<extra_id_47>",
-    "<extra_id_46>",
-    "<extra_id_45>",
-    "<extra_id_44>",
-    "<extra_id_43>",
-    "<extra_id_42>",
-    "<extra_id_41>",
-    "<extra_id_40>",
-    "<extra_id_39>",
-    "<extra_id_38>",
-    "<extra_id_37>",
-    "<extra_id_36>",
-    "<extra_id_35>",
-    "<extra_id_34>",
-    "<extra_id_33>",
-    "<extra_id_32>",
-    "<extra_id_31>",
-    "<extra_id_30>",
-    "<extra_id_29>",
-    "<extra_id_28>",
-    "<extra_id_27>",
-    "<extra_id_26>",
-    "<extra_id_25>",
-    "<extra_id_24>",
-    "<extra_id_23>",
-    "<extra_id_22>",
-    "<extra_id_21>",
-    "<extra_id_20>",
-    "<extra_id_19>",
-    "<extra_id_18>",
-    "<extra_id_17>",
-    "<extra_id_16>",
-    "<extra_id_15>",
-    "<extra_id_14>",
-    "<extra_id_13>",
-    "<extra_id_12>",
-    "<extra_id_11>",
-    "<extra_id_10>",
-    "<extra_id_9>",
-    "<extra_id_8>",
-    "<extra_id_7>",
-    "<extra_id_6>",
-    "<extra_id_5>",
-    "<extra_id_4>",
-    "<extra_id_3>",
-    "<extra_id_2>",
-    "<extra_id_1>",
-    "<extra_id_0>"
-  ],
-  "bos_token": {
-    "content": "<s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "eos_token": {
-    "content": "</s>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  },
-  "unk_token": {
-    "content": "<unk>",
-    "lstrip": false,
-    "normalized": false,
-    "rstrip": false,
-    "single_word": false
-  }
-}
diff --git a/comfy/text_encoders/t5_pile_tokenizer/tokenizer_config.json b/comfy/text_encoders/t5_pile_tokenizer/tokenizer_config.json
deleted file mode 100644
index 81f8e11e9..000000000
--- a/comfy/text_encoders/t5_pile_tokenizer/tokenizer_config.json
+++ /dev/null
@@ -1,945 +0,0 @@
-{
-  "add_bos_token": false,
-  "add_eos_token": true,
-  "add_prefix_space": true,
-  "added_tokens_decoder": {
-    "0": {
-      "content": "<unk>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "1": {
-      "content": "<s>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "2": {
-      "content": "</s>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32000": {
-      "content": "<extra_id_99>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32001": {
-      "content": "<extra_id_98>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32002": {
-      "content": "<extra_id_97>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32003": {
-      "content": "<extra_id_96>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32004": {
-      "content": "<extra_id_95>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32005": {
-      "content": "<extra_id_94>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32006": {
-      "content": "<extra_id_93>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32007": {
-      "content": "<extra_id_92>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32008": {
-      "content": "<extra_id_91>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32009": {
-      "content": "<extra_id_90>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32010": {
-      "content": "<extra_id_89>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32011": {
-      "content": "<extra_id_88>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32012": {
-      "content": "<extra_id_87>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32013": {
-      "content": "<extra_id_86>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32014": {
-      "content": "<extra_id_85>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32015": {
-      "content": "<extra_id_84>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32016": {
-      "content": "<extra_id_83>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32017": {
-      "content": "<extra_id_82>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32018": {
-      "content": "<extra_id_81>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32019": {
-      "content": "<extra_id_80>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32020": {
-      "content": "<extra_id_79>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32021": {
-      "content": "<extra_id_78>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32022": {
-      "content": "<extra_id_77>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32023": {
-      "content": "<extra_id_76>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32024": {
-      "content": "<extra_id_75>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32025": {
-      "content": "<extra_id_74>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32026": {
-      "content": "<extra_id_73>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32027": {
-      "content": "<extra_id_72>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32028": {
-      "content": "<extra_id_71>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32029": {
-      "content": "<extra_id_70>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32030": {
-      "content": "<extra_id_69>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32031": {
-      "content": "<extra_id_68>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32032": {
-      "content": "<extra_id_67>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32033": {
-      "content": "<extra_id_66>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32034": {
-      "content": "<extra_id_65>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32035": {
-      "content": "<extra_id_64>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32036": {
-      "content": "<extra_id_63>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32037": {
-      "content": "<extra_id_62>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32038": {
-      "content": "<extra_id_61>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32039": {
-      "content": "<extra_id_60>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32040": {
-      "content": "<extra_id_59>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32041": {
-      "content": "<extra_id_58>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32042": {
-      "content": "<extra_id_57>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32043": {
-      "content": "<extra_id_56>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32044": {
-      "content": "<extra_id_55>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32045": {
-      "content": "<extra_id_54>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32046": {
-      "content": "<extra_id_53>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32047": {
-      "content": "<extra_id_52>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32048": {
-      "content": "<extra_id_51>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32049": {
-      "content": "<extra_id_50>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32050": {
-      "content": "<extra_id_49>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32051": {
-      "content": "<extra_id_48>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32052": {
-      "content": "<extra_id_47>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32053": {
-      "content": "<extra_id_46>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32054": {
-      "content": "<extra_id_45>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32055": {
-      "content": "<extra_id_44>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32056": {
-      "content": "<extra_id_43>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32057": {
-      "content": "<extra_id_42>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32058": {
-      "content": "<extra_id_41>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32059": {
-      "content": "<extra_id_40>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32060": {
-      "content": "<extra_id_39>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32061": {
-      "content": "<extra_id_38>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32062": {
-      "content": "<extra_id_37>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32063": {
-      "content": "<extra_id_36>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32064": {
-      "content": "<extra_id_35>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32065": {
-      "content": "<extra_id_34>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32066": {
-      "content": "<extra_id_33>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32067": {
-      "content": "<extra_id_32>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32068": {
-      "content": "<extra_id_31>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32069": {
-      "content": "<extra_id_30>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32070": {
-      "content": "<extra_id_29>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
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-      "special": true
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-    "32073": {
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32075": {
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-      "lstrip": false,
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-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-    "32078": {
-      "content": "<extra_id_21>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32079": {
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-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
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-    "32080": {
-      "content": "<extra_id_19>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32081": {
-      "content": "<extra_id_18>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32082": {
-      "content": "<extra_id_17>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32083": {
-      "content": "<extra_id_16>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32084": {
-      "content": "<extra_id_15>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32085": {
-      "content": "<extra_id_14>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32086": {
-      "content": "<extra_id_13>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32087": {
-      "content": "<extra_id_12>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32088": {
-      "content": "<extra_id_11>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32089": {
-      "content": "<extra_id_10>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32090": {
-      "content": "<extra_id_9>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32091": {
-      "content": "<extra_id_8>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32092": {
-      "content": "<extra_id_7>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32093": {
-      "content": "<extra_id_6>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32094": {
-      "content": "<extra_id_5>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32095": {
-      "content": "<extra_id_4>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32096": {
-      "content": "<extra_id_3>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32097": {
-      "content": "<extra_id_2>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32098": {
-      "content": "<extra_id_1>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    },
-    "32099": {
-      "content": "<extra_id_0>",
-      "lstrip": false,
-      "normalized": false,
-      "rstrip": false,
-      "single_word": false,
-      "special": true
-    }
-  },
-  "additional_special_tokens": [
-    "<extra_id_99>",
-    "<extra_id_98>",
-    "<extra_id_97>",
-    "<extra_id_96>",
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-    "<extra_id_82>",
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-    "<extra_id_75>",
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-    "<extra_id_72>",
-    "<extra_id_71>",
-    "<extra_id_70>",
-    "<extra_id_69>",
-    "<extra_id_68>",
-    "<extra_id_67>",
-    "<extra_id_66>",
-    "<extra_id_65>",
-    "<extra_id_64>",
-    "<extra_id_63>",
-    "<extra_id_62>",
-    "<extra_id_61>",
-    "<extra_id_60>",
-    "<extra_id_59>",
-    "<extra_id_58>",
-    "<extra_id_57>",
-    "<extra_id_56>",
-    "<extra_id_55>",
-    "<extra_id_54>",
-    "<extra_id_53>",
-    "<extra_id_52>",
-    "<extra_id_51>",
-    "<extra_id_50>",
-    "<extra_id_49>",
-    "<extra_id_48>",
-    "<extra_id_47>",
-    "<extra_id_46>",
-    "<extra_id_45>",
-    "<extra_id_44>",
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-    "<extra_id_41>",
-    "<extra_id_40>",
-    "<extra_id_39>",
-    "<extra_id_38>",
-    "<extra_id_37>",
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-    "<extra_id_35>",
-    "<extra_id_34>",
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-    "<extra_id_30>",
-    "<extra_id_29>",
-    "<extra_id_28>",
-    "<extra_id_27>",
-    "<extra_id_26>",
-    "<extra_id_25>",
-    "<extra_id_24>",
-    "<extra_id_23>",
-    "<extra_id_22>",
-    "<extra_id_21>",
-    "<extra_id_20>",
-    "<extra_id_19>",
-    "<extra_id_18>",
-    "<extra_id_17>",
-    "<extra_id_16>",
-    "<extra_id_15>",
-    "<extra_id_14>",
-    "<extra_id_13>",
-    "<extra_id_12>",
-    "<extra_id_11>",
-    "<extra_id_10>",
-    "<extra_id_9>",
-    "<extra_id_8>",
-    "<extra_id_7>",
-    "<extra_id_6>",
-    "<extra_id_5>",
-    "<extra_id_4>",
-    "<extra_id_3>",
-    "<extra_id_2>",
-    "<extra_id_1>",
-    "<extra_id_0>"
-  ],
-  "bos_token": "<s>",
-  "clean_up_tokenization_spaces": false,
-  "eos_token": "</s>",
-  "legacy": false,
-  "model_max_length": 512,
-  "pad_token": null,
-  "padding_side": "right",
-  "sp_model_kwargs": {},
-  "spaces_between_special_tokens": false,
-  "tokenizer_class": "LlamaTokenizer",
-  "unk_token": "<unk>",
-  "use_default_system_prompt": false
-}

From 4ca9b9cc29fefaa899cba67d61a8252ae9f16c0d Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Fri, 12 Jul 2024 10:33:57 -0700
Subject: [PATCH 193/315] Add Github Workflow for releasing stable versions and
 standalone bundle. (#3949)

* Add stable release.

* Only build CUDA 12.1 + 3.11 Python.

* Upgrade checkout and setup-python to latest version.

* lzma2

* Update artifact name to be ComfyUI_windows_portable_nvidia.7z
---
 .github/workflows/stable-release.yml | 109 +++++++++++++++++++++++++++
 1 file changed, 109 insertions(+)
 create mode 100644 .github/workflows/stable-release.yml

diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml
new file mode 100644
index 000000000..1fd76b530
--- /dev/null
+++ b/.github/workflows/stable-release.yml
@@ -0,0 +1,109 @@
+
+name: "Release Stable Version"
+
+on:
+  push:
+    tags:
+      - 'v*'
+
+jobs:
+  package_comfy_windows:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    runs-on: windows-latest
+    strategy:
+      matrix:
+        python_version: [3.11.8]
+        cuda_version: [121]
+    steps:
+      - name: Calculate Minor Version
+        shell: bash
+        run: |
+          # Extract the minor version from the Python version
+          MINOR_VERSION=$(echo "${{ matrix.python_version }}" | cut -d'.' -f2)
+          echo "MINOR_VERSION=$MINOR_VERSION" >> $GITHUB_ENV
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python_version }}
+        
+      - uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: false
+      - shell: bash
+        run: |
+          echo "@echo off
+          call update_comfyui.bat nopause
+          echo -
+          echo This will try to update pytorch and all python dependencies.
+          echo -
+          echo If you just want to update normally, close this and run update_comfyui.bat instead.
+          echo -
+          pause
+          ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu${{ matrix.cuda_version }} -r ../ComfyUI/requirements.txt pygit2
+          pause" > update_comfyui_and_python_dependencies.bat
+
+          python -m pip wheel --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu${{ matrix.cuda_version }} -r requirements.txt pygit2 -w ./temp_wheel_dir
+          python -m pip install --no-cache-dir ./temp_wheel_dir/*
+          echo installed basic
+          ls -lah temp_wheel_dir
+          mv temp_wheel_dir cu${{ matrix.cuda_version }}_python_deps
+          mv cu${{ matrix.cuda_version }}_python_deps ../
+          mv update_comfyui_and_python_dependencies.bat ../
+          cd ..
+          pwd
+          ls
+          
+          cp -r ComfyUI ComfyUI_copy
+          curl https://www.python.org/ftp/python/${{ matrix.python_version }}/python-${{ matrix.python_version }}-embed-amd64.zip -o python_embeded.zip
+          unzip python_embeded.zip -d python_embeded
+          cd python_embeded
+          echo ${{ env.MINOR_VERSION }}
+          echo 'import site' >> ./python3${{ env.MINOR_VERSION }}._pth
+          curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
+          ./python.exe get-pip.py
+          ./python.exe --version
+          echo "Pip version:"
+          ./python.exe -m pip --version
+
+          set PATH=$PWD/Scripts:$PATH
+          echo $PATH
+          ./python.exe -s -m pip install ../cu${{ matrix.cuda_version }}_python_deps/*
+          sed -i '1i../ComfyUI' ./python3${{ env.MINOR_VERSION }}._pth
+          cd ..
+
+          git clone https://github.com/comfyanonymous/taesd
+          cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
+
+          mkdir ComfyUI_windows_portable
+          mv python_embeded ComfyUI_windows_portable
+          mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI
+
+          cd ComfyUI_windows_portable
+
+          mkdir update
+          cp -r ComfyUI/.ci/update_windows/* ./update/
+          cp -r ComfyUI/.ci/windows_base_files/* ./
+          cp ../update_comfyui_and_python_dependencies.bat ./update/
+
+          cd ..
+
+          "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z
+
+          cd ComfyUI_windows_portable
+          python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
+
+          ls
+
+      - name: Upload binaries to release
+        uses: svenstaro/upload-release-action@v2
+        with:
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+          file: ComfyUI_windows_portable_nvidia.7z
+          tag: ${{ github.ref }}
+          overwrite: true
+        

From ce2473bb016748a4d98e9bce02d675b4fc22f4ac Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 12 Jul 2024 15:25:07 -0400
Subject: [PATCH 194/315] Add link to AuraFlow example in Readme.

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 35e3238a1..5f747321f 100644
--- a/README.md
+++ b/README.md
@@ -32,6 +32,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
 - [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
+- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
 - Starts up very fast.
 - Works fully offline: will never download anything.

From a3dffc447a7b9f4948037a988fd143eeffd87bd1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 13 Jul 2024 13:51:40 -0400
Subject: [PATCH 195/315] Support AuraFlow Lora and loading model weights in
 diffusers format.

You can load model weights in diffusers format using the UNETLoader node.
---
 comfy/lora.py            |  8 +++++
 comfy/model_detection.py | 66 +++++++++++++++++++++----------------
 comfy/sd.py              | 33 +++++++++----------
 comfy/utils.py           | 70 ++++++++++++++++++++++++++++++++++++++++
 4 files changed, 133 insertions(+), 44 deletions(-)

diff --git a/comfy/lora.py b/comfy/lora.py
index 037431775..e36b354f0 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -274,4 +274,12 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
                 key_map[key_lora] = to
 
+    if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow
+        diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format
+                key_map[key_lora] = to
+
     return key_map
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index dd5bff82a..c62e2b822 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -109,6 +109,10 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config = {}
         unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
         unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
+        double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.')
+        single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.')
+        unet_config["n_double_layers"] = double_layers
+        unet_config["n_layers"] = double_layers + single_layers
         return unet_config
 
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
@@ -450,37 +454,45 @@ def model_config_from_diffusers_unet(state_dict):
     return None
 
 def convert_diffusers_mmdit(state_dict, output_prefix=""):
-    num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
-    if num_blocks > 0:
+    out_sd = {}
+
+    if 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3
+        num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
-        out_sd = {}
         sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
-        for k in sd_map:
-            weight = state_dict.get(k, None)
-            if weight is not None:
-                t = sd_map[k]
+    elif 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
+        num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
+        num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+        sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
+    else:
+        return None
 
-                if not isinstance(t, str):
-                    if len(t) > 2:
-                        fun = t[2]
-                    else:
-                        fun = lambda a: a
-                    offset = t[1]
-                    if offset is not None:
-                        old_weight = out_sd.get(t[0], None)
-                        if old_weight is None:
-                            old_weight = torch.empty_like(weight)
-                            old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
+    for k in sd_map:
+        weight = state_dict.get(k, None)
+        if weight is not None:
+            t = sd_map[k]
 
-                        w = old_weight.narrow(offset[0], offset[1], offset[2])
-                    else:
-                        old_weight = weight
-                        w = weight
-                    w[:] = fun(weight)
-                    t = t[0]
-                    out_sd[t] = old_weight
+            if not isinstance(t, str):
+                if len(t) > 2:
+                    fun = t[2]
                 else:
-                    out_sd[t] = weight
-                state_dict.pop(k)
+                    fun = lambda a: a
+                offset = t[1]
+                if offset is not None:
+                    old_weight = out_sd.get(t[0], None)
+                    if old_weight is None:
+                        old_weight = torch.empty_like(weight)
+                        old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
+
+                    w = old_weight.narrow(offset[0], offset[1], offset[2])
+                else:
+                    old_weight = weight
+                    w = weight
+                w[:] = fun(weight)
+                t = t[0]
+                out_sd[t] = old_weight
+            else:
+                out_sd[t] = weight
+            state_dict.pop(k)
 
     return out_sd
diff --git a/comfy/sd.py b/comfy/sd.py
index 6028029d6..b5bbcef61 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -562,26 +562,25 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
 
     if model_config is not None:
         new_sd = sd
-    elif 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
+    else:
         new_sd = model_detection.convert_diffusers_mmdit(sd, "")
-        if new_sd is None:
-            return None
-        model_config = model_detection.model_config_from_unet(new_sd, "")
-        if model_config is None:
-            return None
-    else: #diffusers
-        model_config = model_detection.model_config_from_diffusers_unet(sd)
-        if model_config is None:
-            return None
+        if new_sd is not None: #diffusers mmdit
+            model_config = model_detection.model_config_from_unet(new_sd, "")
+            if model_config is None:
+                return None
+        else: #diffusers unet
+            model_config = model_detection.model_config_from_diffusers_unet(sd)
+            if model_config is None:
+                return None
 
-        diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
+            diffusers_keys = comfy.utils.unet_to_diffusers(model_config.unet_config)
 
-        new_sd = {}
-        for k in diffusers_keys:
-            if k in sd:
-                new_sd[diffusers_keys[k]] = sd.pop(k)
-            else:
-                logging.warning("{} {}".format(diffusers_keys[k], k))
+            new_sd = {}
+            for k in diffusers_keys:
+                if k in sd:
+                    new_sd[diffusers_keys[k]] = sd.pop(k)
+                else:
+                    logging.warning("{} {}".format(diffusers_keys[k], k))
 
     offload_device = model_management.unet_offload_device()
     unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
diff --git a/comfy/utils.py b/comfy/utils.py
index 48618e076..1e4b5ef88 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -332,6 +332,76 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
 
     return key_map
 
+
+def auraflow_to_diffusers(mmdit_config, output_prefix=""):
+    n_double_layers = mmdit_config.get("n_double_layers", 0)
+    n_layers = mmdit_config.get("n_layers", 0)
+
+    key_map = {}
+    for i in range(n_layers):
+        if i < n_double_layers:
+            index = i
+            prefix_from = "joint_transformer_blocks"
+            prefix_to = "{}double_layers".format(output_prefix)
+            block_map = {
+                            "attn.to_q.weight": "attn.w2q.weight",
+                            "attn.to_k.weight": "attn.w2k.weight",
+                            "attn.to_v.weight": "attn.w2v.weight",
+                            "attn.to_out.0.weight": "attn.w2o.weight",
+                            "attn.add_q_proj.weight": "attn.w1q.weight",
+                            "attn.add_k_proj.weight": "attn.w1k.weight",
+                            "attn.add_v_proj.weight": "attn.w1v.weight",
+                            "attn.to_add_out.weight": "attn.w1o.weight",
+                            "ff.linear_1.weight": "mlpX.c_fc1.weight",
+                            "ff.linear_2.weight": "mlpX.c_fc2.weight",
+                            "ff.out_projection.weight": "mlpX.c_proj.weight",
+                            "ff_context.linear_1.weight": "mlpC.c_fc1.weight",
+                            "ff_context.linear_2.weight": "mlpC.c_fc2.weight",
+                            "ff_context.out_projection.weight": "mlpC.c_proj.weight",
+                            "norm1.linear.weight": "modX.1.weight",
+                            "norm1_context.linear.weight": "modC.1.weight",
+                        }
+        else:
+            index = i - n_double_layers
+            prefix_from = "single_transformer_blocks"
+            prefix_to = "{}single_layers".format(output_prefix)
+
+            block_map = {
+                            "attn.to_q.weight": "attn.w1q.weight",
+                            "attn.to_k.weight": "attn.w1k.weight",
+                            "attn.to_v.weight": "attn.w1v.weight",
+                            "attn.to_out.0.weight": "attn.w1o.weight",
+                            "norm1.linear.weight": "modCX.1.weight",
+                            "ff.linear_1.weight": "mlp.c_fc1.weight",
+                            "ff.linear_2.weight": "mlp.c_fc2.weight",
+                            "ff.out_projection.weight": "mlp.c_proj.weight"
+                        }
+
+        for k in block_map:
+            key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k])
+
+    MAP_BASIC = {
+        ("positional_encoding", "pos_embed.pos_embed"),
+        ("register_tokens", "register_tokens"),
+        ("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"),
+        ("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"),
+        ("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"),
+        ("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"),
+        ("cond_seq_linear.weight", "context_embedder.weight"),
+        ("init_x_linear.weight", "pos_embed.proj.weight"),
+        ("init_x_linear.bias", "pos_embed.proj.bias"),
+        ("final_linear.weight", "proj_out.weight"),
+        ("modF.1.weight", "norm_out.linear.weight", swap_scale_shift),
+    }
+
+    for k in MAP_BASIC:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
 def repeat_to_batch_size(tensor, batch_size, dim=0):
     if tensor.shape[dim] > batch_size:
         return tensor.narrow(dim, 0, batch_size)

From 79547efb65ebb41a964ce7686170c4e6c3641eb2 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Sun, 14 Jul 2024 07:04:40 +0100
Subject: [PATCH 196/315] New menu fixes - fix send to workflow (#3909)

* Fix send to workflow
Fix center align of close workflow dialog
Better support for elements around canvas

* More resilent to extra elements added to body
---
 web/scripts/ui/menu/workflows.js |  8 +++++++-
 web/style.css                    | 25 +++++++++++++++++--------
 2 files changed, 24 insertions(+), 9 deletions(-)

diff --git a/web/scripts/ui/menu/workflows.js b/web/scripts/ui/menu/workflows.js
index afdff538a..3b904fb4b 100644
--- a/web/scripts/ui/menu/workflows.js
+++ b/web/scripts/ui/menu/workflows.js
@@ -182,6 +182,11 @@ export class ComfyWorkflowsMenu {
 				 * @param {ComfyWorkflow} workflow
 				 */
 				async function sendToWorkflow(img, workflow) {
+					const openWorkflow = app.workflowManager.openWorkflows.find((w) => w.path === workflow.path);
+					if (openWorkflow) {
+						workflow = openWorkflow;
+					}
+
 					await workflow.load();
 					let options = [];
 					const nodes = app.graph.computeExecutionOrder(false);
@@ -214,7 +219,8 @@ export class ComfyWorkflowsMenu {
 				nodeType.prototype["getExtraMenuOptions"] = function (_, options) {
 					const r = getExtraMenuOptions?.apply?.(this, arguments);
 
-					if (app.ui.settings.getSettingValue("Comfy.UseNewMenu", false) === true) {
+					const setting = app.ui.settings.getSettingValue("Comfy.UseNewMenu", false);
+					if (setting && setting != "Disabled") {
 						const t = /** @type { {imageIndex?: number, overIndex?: number, imgs: string[]} } */ /** @type {any} */ (this);
 						let img;
 						if (t.imageIndex != null) {
diff --git a/web/style.css b/web/style.css
index e983b652a..8ef1d0dd1 100644
--- a/web/style.css
+++ b/web/style.css
@@ -41,7 +41,7 @@ body {
 	background-color: var(--bg-color);
 	color: var(--fg-color);
 	grid-template-columns: auto 1fr auto;
-	grid-template-rows: auto auto 1fr auto;
+	grid-template-rows: auto 1fr auto;
 	min-height: -webkit-fill-available;
 	max-height: -webkit-fill-available;
 	min-width: -webkit-fill-available;
@@ -49,32 +49,37 @@ body {
 }
 
 .comfyui-body-top {
-	order: 0;
+	order: -5;
 	grid-column: 1/-1;
 	z-index: 10;
+	display: flex;
+	flex-direction: column;
 }
 
 .comfyui-body-left {
-	order: 1;
+	order: -4;
 	z-index: 10;
+	display: flex;
 }
 
 #graph-canvas {
 	width: 100%;
 	height: 100%;
-	order: 2;
-	grid-column: 1/-1;
+	order: -3;
 }
 
 .comfyui-body-right {
-	order: 3;
+	order: -2;
 	z-index: 10;
+	display: flex;
 }
 
 .comfyui-body-bottom {
-	order: 4;
+	order: -1;
 	grid-column: 1/-1;
 	z-index: 10;
+	display: flex;
+	flex-direction: column;
 }
 
 .comfy-multiline-input {
@@ -408,8 +413,12 @@ dialog::backdrop {
 	background: rgba(0, 0, 0, 0.5);
 }
 
-.comfy-dialog.comfyui-dialog {
+.comfy-dialog.comfyui-dialog.comfy-modal {
 	top: 0;
+	left: 0;
+	right: 0;
+	bottom: 0;
+	transform: none;
 }
 
 .comfy-dialog.comfy-modal {

From 7914c47d5afb1c5ffab49d665f9a1ac86a458821 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 14 Jul 2024 10:07:36 -0400
Subject: [PATCH 197/315] Quick fix for the promax controlnet.

---
 comfy/cldm/cldm.py  | 6 +++---
 comfy/controlnet.py | 2 +-
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index d4d32b87e..4a58c823f 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -92,7 +92,7 @@ class ControlNet(nn.Module):
         transformer_depth_middle=None,
         transformer_depth_output=None,
         attn_precision=None,
-        union_controlnet=False,
+        union_controlnet_num_control_type=None,
         device=None,
         operations=comfy.ops.disable_weight_init,
         **kwargs,
@@ -320,8 +320,8 @@ class ControlNet(nn.Module):
         self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
         self._feature_size += ch
 
-        if union_controlnet:
-            self.num_control_type = 6
+        if union_controlnet_num_control_type is not None:
+            self.num_control_type = union_controlnet_num_control_type
             num_trans_channel = 320
             num_trans_head = 8
             num_trans_layer = 1
diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index 84286f1fa..b8e27c71f 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -414,7 +414,7 @@ def load_controlnet(ckpt_path, model=None):
                 new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
 
         if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet
-            controlnet_config["union_controlnet"] = True
+            controlnet_config["union_controlnet_num_control_type"] = controlnet_data["task_embedding"].shape[0]
             for k in list(controlnet_data.keys()):
                 new_k = k.replace('.attn.in_proj_', '.attn.in_proj.')
                 new_sd[new_k] = controlnet_data.pop(k)

From 1305fb294ca69d0a44d88c5bf7ce8c682abd0c8a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 15 Jul 2024 17:36:24 -0400
Subject: [PATCH 198/315] Refactor: Move some code to the comfy/text_encoders
 folder.

---
 comfy/sd.py                                   | 20 +++++++++----------
 comfy/supported_models.py                     |  8 ++++----
 comfy/text_encoders/aura_t5.py                |  4 ++--
 comfy/{ => text_encoders}/sa_t5.py            |  4 ++--
 comfy/{ => text_encoders}/sd3_clip.py         |  4 ++--
 comfy/{ => text_encoders}/t5.py               |  0
 comfy/{ => text_encoders}/t5_config_base.json |  0
 comfy/{ => text_encoders}/t5_config_xxl.json  |  0
 .../t5_tokenizer/special_tokens_map.json      |  0
 .../t5_tokenizer/tokenizer.json               |  0
 .../t5_tokenizer/tokenizer_config.json        |  0
 11 files changed, 20 insertions(+), 20 deletions(-)
 rename comfy/{ => text_encoders}/sa_t5.py (88%)
 rename comfy/{ => text_encoders}/sd3_clip.py (98%)
 rename comfy/{ => text_encoders}/t5.py (100%)
 rename comfy/{ => text_encoders}/t5_config_base.json (100%)
 rename comfy/{ => text_encoders}/t5_config_xxl.json (100%)
 rename comfy/{ => text_encoders}/t5_tokenizer/special_tokens_map.json (100%)
 rename comfy/{ => text_encoders}/t5_tokenizer/tokenizer.json (100%)
 rename comfy/{ => text_encoders}/t5_tokenizer/tokenizer_config.json (100%)

diff --git a/comfy/sd.py b/comfy/sd.py
index b5bbcef61..17df5faff 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -19,8 +19,8 @@ from . import model_detection
 from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
-from . import sd3_clip
-from . import sa_t5
+import comfy.text_encoders.sd3_clip
+import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
 
 import comfy.model_patcher
@@ -414,27 +414,27 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
             dtype_t5 = weight.dtype
             if weight.shape[-1] == 4096:
-                clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
-                clip_target.tokenizer = sd3_clip.SD3Tokenizer
+                clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
+                clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
             elif weight.shape[-1] == 2048:
                 clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
                 clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
         elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
-            clip_target.clip = sa_t5.SAT5Model
-            clip_target.tokenizer = sa_t5.SAT5Tokenizer
+            clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
+            clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
         else:
             clip_target.clip = sd1_clip.SD1ClipModel
             clip_target.tokenizer = sd1_clip.SD1Tokenizer
     elif len(clip_data) == 2:
         if clip_type == CLIPType.SD3:
-            clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
-            clip_target.tokenizer = sd3_clip.SD3Tokenizer
+            clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
+            clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
     elif len(clip_data) == 3:
-        clip_target.clip = sd3_clip.SD3ClipModel
-        clip_target.tokenizer = sd3_clip.SD3Tokenizer
+        clip_target.clip = comfy.text_encoders.sd3_clip.SD3ClipModel
+        clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
 
     clip = CLIP(clip_target, embedding_directory=embedding_directory)
     for c in clip_data:
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index a030f6229..b4d1059ef 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -5,8 +5,8 @@ from . import utils
 from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
-from . import sd3_clip
-from . import sa_t5
+import comfy.text_encoders.sd3_clip
+import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
 
 from . import supported_models_base
@@ -524,7 +524,7 @@ class SD3(supported_models_base.BASE):
             t5 = True
             dtype_t5 = state_dict[t5_key].dtype
 
-        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
+        return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
 
 class StableAudio(supported_models_base.BASE):
     unet_config = {
@@ -555,7 +555,7 @@ class StableAudio(supported_models_base.BASE):
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
     def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
+        return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model)
 
 class AuraFlow(supported_models_base.BASE):
     unet_config = {
diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py
index 95f942ef5..6b9e4fe53 100644
--- a/comfy/text_encoders/aura_t5.py
+++ b/comfy/text_encoders/aura_t5.py
@@ -1,12 +1,12 @@
 from comfy import sd1_clip
 from .llama_tokenizer import LLAMATokenizer
-import comfy.t5
+import comfy.text_encoders.t5
 import os
 
 class PT5XlModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json")
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
 
 class PT5XlTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None):
diff --git a/comfy/sa_t5.py b/comfy/text_encoders/sa_t5.py
similarity index 88%
rename from comfy/sa_t5.py
rename to comfy/text_encoders/sa_t5.py
index acc302f67..038558e7a 100644
--- a/comfy/sa_t5.py
+++ b/comfy/text_encoders/sa_t5.py
@@ -1,12 +1,12 @@
 from comfy import sd1_clip
 from transformers import T5TokenizerFast
-import comfy.t5
+import comfy.text_encoders.t5
 import os
 
 class T5BaseModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json")
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
 
 class T5BaseTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None):
diff --git a/comfy/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
similarity index 98%
rename from comfy/sd3_clip.py
rename to comfy/text_encoders/sd3_clip.py
index 0713eb285..70127e509 100644
--- a/comfy/sd3_clip.py
+++ b/comfy/text_encoders/sd3_clip.py
@@ -1,7 +1,7 @@
 from comfy import sd1_clip
 from comfy import sdxl_clip
 from transformers import T5TokenizerFast
-import comfy.t5
+import comfy.text_encoders.t5
 import torch
 import os
 import comfy.model_management
@@ -10,7 +10,7 @@ import logging
 class T5XXLModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5)
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5)
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None):
diff --git a/comfy/t5.py b/comfy/text_encoders/t5.py
similarity index 100%
rename from comfy/t5.py
rename to comfy/text_encoders/t5.py
diff --git a/comfy/t5_config_base.json b/comfy/text_encoders/t5_config_base.json
similarity index 100%
rename from comfy/t5_config_base.json
rename to comfy/text_encoders/t5_config_base.json
diff --git a/comfy/t5_config_xxl.json b/comfy/text_encoders/t5_config_xxl.json
similarity index 100%
rename from comfy/t5_config_xxl.json
rename to comfy/text_encoders/t5_config_xxl.json
diff --git a/comfy/t5_tokenizer/special_tokens_map.json b/comfy/text_encoders/t5_tokenizer/special_tokens_map.json
similarity index 100%
rename from comfy/t5_tokenizer/special_tokens_map.json
rename to comfy/text_encoders/t5_tokenizer/special_tokens_map.json
diff --git a/comfy/t5_tokenizer/tokenizer.json b/comfy/text_encoders/t5_tokenizer/tokenizer.json
similarity index 100%
rename from comfy/t5_tokenizer/tokenizer.json
rename to comfy/text_encoders/t5_tokenizer/tokenizer.json
diff --git a/comfy/t5_tokenizer/tokenizer_config.json b/comfy/text_encoders/t5_tokenizer/tokenizer_config.json
similarity index 100%
rename from comfy/t5_tokenizer/tokenizer_config.json
rename to comfy/text_encoders/t5_tokenizer/tokenizer_config.json

From 136c93cb477e4c5c308791746583499805ab29aa Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 15 Jul 2024 20:01:49 -0400
Subject: [PATCH 199/315] Fix bug with workflow not registering change.

There was an issue when only the class type of a node changed with all the
inputs staying the same.
---
 execution.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/execution.py b/execution.py
index 76225a962..8b3d116c6 100644
--- a/execution.py
+++ b/execution.py
@@ -247,6 +247,8 @@ def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item
             to_delete = True
         elif unique_id not in old_prompt:
             to_delete = True
+        elif class_type != old_prompt[unique_id]['class_type']:
+            to_delete = True
         elif inputs == old_prompt[unique_id]['inputs']:
             for x in inputs:
                 input_data = inputs[x]

From 33346fd9b8856942187ad8c818498a080a2027b5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 15 Jul 2024 20:36:03 -0400
Subject: [PATCH 200/315] Fix bug with custom nodes on other drives.

---
 nodes.py | 30 +++++++++++++++---------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/nodes.py b/nodes.py
index 5778f0609..998b316b7 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1890,29 +1890,29 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 EXTENSION_WEB_DIRS = {}
 
 
-def get_relative_module_name(module_path: str) -> str:
+def get_module_name(module_path: str) -> str:
     """
     Returns the module name based on the given module path.
     Examples:
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "custom_nodes.my_custom_node"
-        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes.my
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.py") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__.py") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node/__init__/") -> "my_custom_node"
+        get_module_name("C:/Users/username/ComfyUI/custom_nodes/my_custom_node.disabled") -> "custom_nodes
     Args:
         module_path (str): The path of the module.
     Returns:
         str: The module name.
     """
-    relative_path = os.path.relpath(module_path, folder_paths.base_path)
+    base_path = os.path.basename(module_path)
     if os.path.isfile(module_path):
-        relative_path = os.path.splitext(relative_path)[0]
-    return relative_path.replace(os.sep, '.')
+        base_path = os.path.splitext(base_path)[0]
+    return base_path
 
 
-def load_custom_node(module_path: str, ignore=set()) -> bool:
+def load_custom_node(module_path: str, ignore=set(), module_parent="custom_nodes") -> bool:
     module_name = os.path.basename(module_path)
     if os.path.isfile(module_path):
         sp = os.path.splitext(module_path)
@@ -1939,7 +1939,7 @@ def load_custom_node(module_path: str, ignore=set()) -> bool:
             for name, node_cls in module.NODE_CLASS_MAPPINGS.items():
                 if name not in ignore:
                     NODE_CLASS_MAPPINGS[name] = node_cls
-                    node_cls.RELATIVE_PYTHON_MODULE = get_relative_module_name(module_path)
+                    node_cls.RELATIVE_PYTHON_MODULE = "{}.{}".format(module_parent, get_module_name(module_path))
             if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
                 NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
             return True
@@ -1974,7 +1974,7 @@ def init_external_custom_nodes():
             if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
             if module_path.endswith(".disabled"): continue
             time_before = time.perf_counter()
-            success = load_custom_node(module_path, base_node_names)
+            success = load_custom_node(module_path, base_node_names, module_parent="custom_nodes")
             node_import_times.append((time.perf_counter() - time_before, module_path, success))
 
     if len(node_import_times) > 0:
@@ -2040,7 +2040,7 @@ def init_builtin_extra_nodes():
 
     import_failed = []
     for node_file in extras_files:
-        if not load_custom_node(os.path.join(extras_dir, node_file)):
+        if not load_custom_node(os.path.join(extras_dir, node_file), module_parent="comfy_extras"):
             import_failed.append(node_file)
 
     return import_failed

From 99458e8aca3c48c7b2de3c404f66536991c37182 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 16 Jul 2024 11:26:11 -0400
Subject: [PATCH 201/315] Add `FrontendManager` to manage non-default front-end
 impl (#3897)

* Add frontend manager

* Add tests

* nit

* Add unit test to github CI

* Fix path

* nit

* ignore

* Add logging

* Install test deps

* Remove 'stable' keyword support

* Update test

* Add web-root arg

* Rename web-root to front-end-root

* Add test on non-exist version number

* Use repo owner/name to replace hard coded provider list

* Inline cmd args

* nit

* Fix unit test
---
 .github/workflows/test-ui.yaml               |   4 +
 .gitignore                                   |   3 +-
 app/__init__.py                              |   0
 app/frontend_management.py                   | 187 +++++++++++++++++++
 comfy/cli_args.py                            |  35 ++++
 pytest.ini                                   |   7 +-
 server.py                                    |  11 +-
 tests-unit/README.md                         |   8 +
 tests-unit/app_test/__init__.py              |   0
 tests-unit/app_test/frontend_manager_test.py | 100 ++++++++++
 tests-unit/requirements.txt                  |   1 +
 11 files changed, 350 insertions(+), 6 deletions(-)
 create mode 100644 app/__init__.py
 create mode 100644 app/frontend_management.py
 create mode 100644 tests-unit/README.md
 create mode 100644 tests-unit/app_test/__init__.py
 create mode 100644 tests-unit/app_test/frontend_manager_test.py
 create mode 100644 tests-unit/requirements.txt

diff --git a/.github/workflows/test-ui.yaml b/.github/workflows/test-ui.yaml
index 4b8b97934..d947e9d5f 100644
--- a/.github/workflows/test-ui.yaml
+++ b/.github/workflows/test-ui.yaml
@@ -24,3 +24,7 @@ jobs:
         npm run test:generate
         npm test -- --verbose
       working-directory: ./tests-ui
+    - name: Run Unit Tests
+      run: |
+        pip install -r tests-unit/requirements.txt
+        python -m pytest tests-unit
diff --git a/.gitignore b/.gitignore
index a9beebe73..5092c98f4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,4 +17,5 @@ venv/
 !/web/extensions/core/
 /tests-ui/data/object_info.json
 /user/
-*.log
\ No newline at end of file
+*.log
+web_custom_versions/
\ No newline at end of file
diff --git a/app/__init__.py b/app/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/app/frontend_management.py b/app/frontend_management.py
new file mode 100644
index 000000000..0855f4160
--- /dev/null
+++ b/app/frontend_management.py
@@ -0,0 +1,187 @@
+import argparse
+import logging
+import os
+import re
+import tempfile
+import zipfile
+from dataclasses import dataclass
+from functools import cached_property
+from pathlib import Path
+from typing import TypedDict
+
+import requests
+from typing_extensions import NotRequired
+from comfy.cli_args import DEFAULT_VERSION_STRING
+
+
+REQUEST_TIMEOUT = 10  # seconds
+
+
+class Asset(TypedDict):
+    url: str
+
+
+class Release(TypedDict):
+    id: int
+    tag_name: str
+    name: str
+    prerelease: bool
+    created_at: str
+    published_at: str
+    body: str
+    assets: NotRequired[list[Asset]]
+
+
+@dataclass
+class FrontEndProvider:
+    owner: str
+    repo: str
+
+    @property
+    def folder_name(self) -> str:
+        return f"{self.owner}_{self.repo}"
+
+    @property
+    def release_url(self) -> str:
+        return f"https://api.github.com/repos/{self.owner}/{self.repo}/releases"
+
+    @cached_property
+    def all_releases(self) -> list[Release]:
+        releases = []
+        api_url = self.release_url
+        while api_url:
+            response = requests.get(api_url, timeout=REQUEST_TIMEOUT)
+            response.raise_for_status()  # Raises an HTTPError if the response was an error
+            releases.extend(response.json())
+            # GitHub uses the Link header to provide pagination links. Check if it exists and update api_url accordingly.
+            if "next" in response.links:
+                api_url = response.links["next"]["url"]
+            else:
+                api_url = None
+        return releases
+
+    @cached_property
+    def latest_release(self) -> Release:
+        latest_release_url = f"{self.release_url}/latest"
+        response = requests.get(latest_release_url, timeout=REQUEST_TIMEOUT)
+        response.raise_for_status()  # Raises an HTTPError if the response was an error
+        return response.json()
+
+    def get_release(self, version: str) -> Release:
+        if version == "latest":
+            return self.latest_release
+        else:
+            for release in self.all_releases:
+                if release["tag_name"] in [version, f"v{version}"]:
+                    return release
+            raise ValueError(f"Version {version} not found in releases")
+
+
+def download_release_asset_zip(release: Release, destination_path: str) -> None:
+    """Download dist.zip from github release."""
+    asset_url = None
+    for asset in release.get("assets", []):
+        if asset["name"] == "dist.zip":
+            asset_url = asset["url"]
+            break
+
+    if not asset_url:
+        raise ValueError("dist.zip not found in the release assets")
+
+    # Use a temporary file to download the zip content
+    with tempfile.TemporaryFile() as tmp_file:
+        headers = {"Accept": "application/octet-stream"}
+        response = requests.get(
+            asset_url, headers=headers, allow_redirects=True, timeout=REQUEST_TIMEOUT
+        )
+        response.raise_for_status()  # Ensure we got a successful response
+
+        # Write the content to the temporary file
+        tmp_file.write(response.content)
+
+        # Go back to the beginning of the temporary file
+        tmp_file.seek(0)
+
+        # Extract the zip file content to the destination path
+        with zipfile.ZipFile(tmp_file, "r") as zip_ref:
+            zip_ref.extractall(destination_path)
+
+
+class FrontendManager:
+    DEFAULT_FRONTEND_PATH = str(Path(__file__).parents[1] / "web")
+    CUSTOM_FRONTENDS_ROOT = str(Path(__file__).parents[1] / "web_custom_versions")
+
+    @classmethod
+    def parse_version_string(cls, value: str) -> tuple[str, str, str]:
+        """
+        Args:
+            value (str): The version string to parse.
+
+        Returns:
+            tuple[str, str]: A tuple containing provider name and version.
+
+        Raises:
+            argparse.ArgumentTypeError: If the version string is invalid.
+        """
+        VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(\d+\.\d+\.\d+|latest)$"
+        match_result = re.match(VERSION_PATTERN, value)
+        if match_result is None:
+            raise argparse.ArgumentTypeError(f"Invalid version string: {value}")
+
+        return match_result.group(1), match_result.group(2), match_result.group(3)
+
+    @classmethod
+    def init_frontend_unsafe(cls, version_string: str) -> str:
+        """
+        Initializes the frontend for the specified version.
+
+        Args:
+            version_string (str): The version string.
+
+        Returns:
+            str: The path to the initialized frontend.
+
+        Raises:
+            Exception: If there is an error during the initialization process.
+            main error source might be request timeout or invalid URL.
+        """
+        if version_string == DEFAULT_VERSION_STRING:
+            return cls.DEFAULT_FRONTEND_PATH
+
+        repo_owner, repo_name, version = cls.parse_version_string(version_string)
+        provider = FrontEndProvider(repo_owner, repo_name)
+        release = provider.get_release(version)
+
+        semantic_version = release["tag_name"].lstrip("v")
+        web_root = str(
+            Path(cls.CUSTOM_FRONTENDS_ROOT) / provider.folder_name / semantic_version
+        )
+        if not os.path.exists(web_root):
+            os.makedirs(web_root, exist_ok=True)
+            logging.info(
+                "Downloading frontend(%s) version(%s) to (%s)",
+                provider.folder_name,
+                semantic_version,
+                web_root,
+            )
+            logging.debug(release)
+            download_release_asset_zip(release, destination_path=web_root)
+        return web_root
+
+    @classmethod
+    def init_frontend(cls, version_string: str) -> str:
+        """
+        Initializes the frontend with the specified version string.
+
+        Args:
+            version_string (str): The version string to initialize the frontend with.
+
+        Returns:
+            str: The path of the initialized frontend.
+        """
+        try:
+            return cls.init_frontend_unsafe(version_string)
+        except Exception as e:
+            logging.error("Failed to initialize frontend: %s", e)
+            logging.info("Falling back to the default frontend.")
+            return cls.DEFAULT_FRONTEND_PATH
diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index b72bf3998..6251e3d28 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -1,7 +1,10 @@
 import argparse
 import enum
+import os
+from typing import Optional
 import comfy.options
 
+
 class EnumAction(argparse.Action):
     """
     Argparse action for handling Enums
@@ -124,6 +127,38 @@ parser.add_argument("--multi-user", action="store_true", help="Enables per-user
 
 parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
 
+# The default built-in provider hosted under web/
+DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
+
+parser.add_argument(
+    "--front-end-version",
+    type=str,
+    default=DEFAULT_VERSION_STRING,
+    help="""
+    Specifies the version of the frontend to be used. This command needs internet connectivity to query and
+    download available frontend implementations from GitHub releases.
+
+    The version string should be in the format of:
+    [repoOwner]/[repoName]@[version]
+    where version is one of: "latest" or a valid version number (e.g. "1.0.0")
+    """,
+)
+
+def is_valid_directory(path: Optional[str]) -> Optional[str]:
+    """Validate if the given path is a directory."""
+    if path is None:
+        return None
+
+    if not os.path.isdir(path):
+        raise argparse.ArgumentTypeError(f"{path} is not a valid directory.")
+    return path
+
+parser.add_argument(
+    "--front-end-root",
+    type=is_valid_directory,
+    default=None,
+    help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
+)
 
 if comfy.options.args_parsing:
     args = parser.parse_args()
diff --git a/pytest.ini b/pytest.ini
index b5a68e0f1..8b7a747e7 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -1,5 +1,8 @@
 [pytest]
 markers = 
   inference: mark as inference test (deselect with '-m "not inference"')
-testpaths = tests
-addopts = -s
\ No newline at end of file
+testpaths =
+  tests
+  tests-unit
+addopts = -s
+pythonpath = .
diff --git a/server.py b/server.py
index ce7c7532d..03c4c4219 100644
--- a/server.py
+++ b/server.py
@@ -25,9 +25,10 @@ import mimetypes
 from comfy.cli_args import args
 import comfy.utils
 import comfy.model_management
-
+from app.frontend_management import FrontendManager
 from app.user_manager import UserManager
 
+
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
     UNENCODED_PREVIEW_IMAGE = 2
@@ -83,8 +84,12 @@ class PromptServer():
         max_upload_size = round(args.max_upload_size * 1024 * 1024)
         self.app = web.Application(client_max_size=max_upload_size, middlewares=middlewares)
         self.sockets = dict()
-        self.web_root = os.path.join(os.path.dirname(
-            os.path.realpath(__file__)), "web")
+        self.web_root = (
+            FrontendManager.init_frontend(args.front_end_version)
+            if args.front_end_root is None
+            else args.front_end_root
+        )
+        logging.info(f"[Prompt Server] web root: {self.web_root}")
         routes = web.RouteTableDef()
         self.routes = routes
         self.last_node_id = None
diff --git a/tests-unit/README.md b/tests-unit/README.md
new file mode 100644
index 000000000..94abd9853
--- /dev/null
+++ b/tests-unit/README.md
@@ -0,0 +1,8 @@
+# Pytest Unit Tests
+
+## Install test dependencies
+
+`pip install -r tests-units/requirements.txt`
+
+## Run tests
+`pytest tests-units/`
diff --git a/tests-unit/app_test/__init__.py b/tests-unit/app_test/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests-unit/app_test/frontend_manager_test.py b/tests-unit/app_test/frontend_manager_test.py
new file mode 100644
index 000000000..637869cfb
--- /dev/null
+++ b/tests-unit/app_test/frontend_manager_test.py
@@ -0,0 +1,100 @@
+import argparse
+import pytest
+from requests.exceptions import HTTPError
+
+from app.frontend_management import (
+    FrontendManager,
+    FrontEndProvider,
+    Release,
+)
+from comfy.cli_args import DEFAULT_VERSION_STRING
+
+
+@pytest.fixture
+def mock_releases():
+    return [
+        Release(
+            id=1,
+            tag_name="1.0.0",
+            name="Release 1.0.0",
+            prerelease=False,
+            created_at="2022-01-01T00:00:00Z",
+            published_at="2022-01-01T00:00:00Z",
+            body="Release notes for 1.0.0",
+            assets=[{"name": "dist.zip", "url": "https://example.com/dist.zip"}],
+        ),
+        Release(
+            id=2,
+            tag_name="2.0.0",
+            name="Release 2.0.0",
+            prerelease=False,
+            created_at="2022-02-01T00:00:00Z",
+            published_at="2022-02-01T00:00:00Z",
+            body="Release notes for 2.0.0",
+            assets=[{"name": "dist.zip", "url": "https://example.com/dist.zip"}],
+        ),
+    ]
+
+
+@pytest.fixture
+def mock_provider(mock_releases):
+    provider = FrontEndProvider(
+        owner="test-owner",
+        repo="test-repo",
+    )
+    provider.all_releases = mock_releases
+    provider.latest_release = mock_releases[1]
+    FrontendManager.PROVIDERS = [provider]
+    return provider
+
+
+def test_get_release(mock_provider, mock_releases):
+    version = "1.0.0"
+    release = mock_provider.get_release(version)
+    assert release == mock_releases[0]
+
+
+def test_get_release_latest(mock_provider, mock_releases):
+    version = "latest"
+    release = mock_provider.get_release(version)
+    assert release == mock_releases[1]
+
+
+def test_get_release_invalid_version(mock_provider):
+    version = "invalid"
+    with pytest.raises(ValueError):
+        mock_provider.get_release(version)
+
+
+def test_init_frontend_default():
+    version_string = DEFAULT_VERSION_STRING
+    frontend_path = FrontendManager.init_frontend(version_string)
+    assert frontend_path == FrontendManager.DEFAULT_FRONTEND_PATH
+
+
+def test_init_frontend_invalid_version():
+    version_string = "test-owner/test-repo@1.100.99"
+    with pytest.raises(HTTPError):
+        FrontendManager.init_frontend_unsafe(version_string)
+
+
+def test_init_frontend_invalid_provider():
+    version_string = "invalid/invalid@latest"
+    with pytest.raises(HTTPError):
+        FrontendManager.init_frontend_unsafe(version_string)
+
+
+def test_parse_version_string():
+    version_string = "owner/repo@1.0.0"
+    repo_owner, repo_name, version = FrontendManager.parse_version_string(
+        version_string
+    )
+    assert repo_owner == "owner"
+    assert repo_name == "repo"
+    assert version == "1.0.0"
+
+
+def test_parse_version_string_invalid():
+    version_string = "invalid"
+    with pytest.raises(argparse.ArgumentTypeError):
+        FrontendManager.parse_version_string(version_string)
diff --git a/tests-unit/requirements.txt b/tests-unit/requirements.txt
new file mode 100644
index 000000000..0587502f8
--- /dev/null
+++ b/tests-unit/requirements.txt
@@ -0,0 +1 @@
+pytest>=7.8.0

From e1630391d6d190c3dd9ef4609f0c84af72be54af Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 16 Jul 2024 11:29:38 -0400
Subject: [PATCH 202/315] Allow version names like v0.0.1 for the
 FrontendManager.

---
 app/frontend_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/app/frontend_management.py b/app/frontend_management.py
index 0855f4160..5452f6e11 100644
--- a/app/frontend_management.py
+++ b/app/frontend_management.py
@@ -123,7 +123,7 @@ class FrontendManager:
         Raises:
             argparse.ArgumentTypeError: If the version string is invalid.
         """
-        VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(\d+\.\d+\.\d+|latest)$"
+        VERSION_PATTERN = r"^([a-zA-Z0-9][a-zA-Z0-9-]{0,38})/([a-zA-Z0-9_.-]+)@(v?\d+\.\d+\.\d+|latest)$"
         match_result = re.match(VERSION_PATTERN, value)
         if match_result is None:
             raise argparse.ArgumentTypeError(f"Invalid version string: {value}")

From 821f93872ecf3f1769b8abebd640a804839f4ab9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 16 Jul 2024 15:18:24 -0400
Subject: [PATCH 203/315] Allow model sampling to set number of timesteps.

---
 comfy/model_sampling.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index 2d95a83dc..25bb7e043 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -59,8 +59,9 @@ class ModelSamplingDiscrete(torch.nn.Module):
         beta_schedule = sampling_settings.get("beta_schedule", "linear")
         linear_start = sampling_settings.get("linear_start", 0.00085)
         linear_end = sampling_settings.get("linear_end", 0.012)
+        timesteps = sampling_settings.get("timesteps", 1000)
 
-        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=1000, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
+        self._register_schedule(given_betas=None, beta_schedule=beta_schedule, timesteps=timesteps, linear_start=linear_start, linear_end=linear_end, cosine_s=8e-3)
         self.sigma_data = 1.0
 
     def _register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,

From 8270c625301034767c77b7d7b1e5a135172d1ebb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 16 Jul 2024 17:01:40 -0400
Subject: [PATCH 204/315] Add SetUnionControlNetType to set the type of the
 union controlnet model.

---
 comfy/controlnet.py              |  8 ++++++-
 comfy_extras/nodes_controlnet.py | 37 ++++++++++++++++++++++++++++++++
 nodes.py                         |  1 +
 3 files changed, 45 insertions(+), 1 deletion(-)
 create mode 100644 comfy_extras/nodes_controlnet.py

diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index b8e27c71f..12e5f16c8 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -45,6 +45,7 @@ class ControlBase:
         self.timestep_range = None
         self.compression_ratio = 8
         self.upscale_algorithm = 'nearest-exact'
+        self.extra_args = {}
 
         if device is None:
             device = comfy.model_management.get_torch_device()
@@ -90,6 +91,7 @@ class ControlBase:
         c.compression_ratio = self.compression_ratio
         c.upscale_algorithm = self.upscale_algorithm
         c.latent_format = self.latent_format
+        c.extra_args = self.extra_args.copy()
         c.vae = self.vae
 
     def inference_memory_requirements(self, dtype):
@@ -135,6 +137,10 @@ class ControlBase:
                                 o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue
         return out
 
+    def set_extra_arg(self, argument, value=None):
+        self.extra_args[argument] = value
+
+
 class ControlNet(ControlBase):
     def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None):
         super().__init__(device)
@@ -191,7 +197,7 @@ class ControlNet(ControlBase):
         timestep = self.model_sampling_current.timestep(t)
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
-        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
+        control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y, **self.extra_args)
         return self.control_merge(control, control_prev, output_dtype)
 
     def copy(self):
diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py
new file mode 100644
index 000000000..e550436b0
--- /dev/null
+++ b/comfy_extras/nodes_controlnet.py
@@ -0,0 +1,37 @@
+
+UNION_CONTROLNET_TYPES = {"auto": -1,
+                          "openpose": 0,
+                          "depth": 1,
+                          "hed/pidi/scribble/ted": 2,
+                          "canny/lineart/anime_lineart/mlsd": 3,
+                          "normal": 4,
+                          "segment": 5,
+                          "tile": 6,
+                          "repaint": 7,
+                        }
+
+class SetUnionControlNetType:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"control_net": ("CONTROL_NET", ),
+                             "type": (list(UNION_CONTROLNET_TYPES.keys()),)
+                             }}
+
+    CATEGORY = "conditioning"
+    RETURN_TYPES = ("CONTROL_NET",)
+
+    FUNCTION = "set_controlnet_type"
+
+    def set_controlnet_type(self, control_net, type):
+        control_net = control_net.copy()
+        type_number = UNION_CONTROLNET_TYPES[type]
+        if type_number >= 0:
+            control_net.set_extra_arg("control_type", [type_number])
+        else:
+            control_net.set_extra_arg("control_type", [])
+
+        return (control_net,)
+
+NODE_CLASS_MAPPINGS = {
+    "SetUnionControlNetType": SetUnionControlNetType,
+}
diff --git a/nodes.py b/nodes.py
index 998b316b7..89a2f21d2 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2036,6 +2036,7 @@ def init_builtin_extra_nodes():
         "nodes_audio.py",
         "nodes_sd3.py",
         "nodes_gits.py",
+        "nodes_controlnet.py",
     ]
 
     import_failed = []

From 60383f3b64aa4477edfea325879bb04d3695161c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 16 Jul 2024 17:08:25 -0400
Subject: [PATCH 205/315] Move controlnet nodes to conditioning/controlnet.

---
 comfy_extras/nodes_controlnet.py | 2 +-
 comfy_extras/nodes_sd3.py        | 2 +-
 nodes.py                         | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py
index e550436b0..ef7cfc6ab 100644
--- a/comfy_extras/nodes_controlnet.py
+++ b/comfy_extras/nodes_controlnet.py
@@ -17,7 +17,7 @@ class SetUnionControlNetType:
                              "type": (list(UNION_CONTROLNET_TYPES.keys()),)
                              }}
 
-    CATEGORY = "conditioning"
+    CATEGORY = "conditioning/controlnet"
     RETURN_TYPES = ("CONTROL_NET",)
 
     FUNCTION = "set_controlnet_type"
diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py
index 548b1ad6a..0aafa2426 100644
--- a/comfy_extras/nodes_sd3.py
+++ b/comfy_extras/nodes_sd3.py
@@ -92,7 +92,7 @@ class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
                              "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                              "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
                              }}
-    CATEGORY = "_for_testing/sd3"
+    CATEGORY = "conditioning/controlnet"
 
 NODE_CLASS_MAPPINGS = {
     "TripleCLIPLoader": TripleCLIPLoader,
diff --git a/nodes.py b/nodes.py
index 89a2f21d2..fbdcb6c91 100644
--- a/nodes.py
+++ b/nodes.py
@@ -748,7 +748,7 @@ class ControlNetApply:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "apply_controlnet"
 
-    CATEGORY = "conditioning"
+    CATEGORY = "conditioning/controlnet"
 
     def apply_controlnet(self, conditioning, control_net, image, strength):
         if strength == 0:
@@ -783,7 +783,7 @@ class ControlNetApplyAdvanced:
     RETURN_NAMES = ("positive", "negative")
     FUNCTION = "apply_controlnet"
 
-    CATEGORY = "conditioning"
+    CATEGORY = "conditioning/controlnet"
 
     def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None):
         if strength == 0:

From f2298799ba58e915a1f22fec336d455c0913f409 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 16 Jul 2024 18:20:39 -0400
Subject: [PATCH 206/315] Fix annotation (#4035)

---
 app/frontend_management.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/app/frontend_management.py b/app/frontend_management.py
index 5452f6e11..fb57b23f3 100644
--- a/app/frontend_management.py
+++ b/app/frontend_management.py
@@ -1,3 +1,4 @@
+from __future__ import annotations
 import argparse
 import logging
 import os

From c5a48b15bddab089f0054af0ed72eea868263ef6 Mon Sep 17 00:00:00 2001
From: Thomas Ward <teward@thomas-ward.net>
Date: Tue, 16 Jul 2024 18:27:09 -0400
Subject: [PATCH 207/315] Make default hash lib configurable without code
 changes via CLI argument (#3947)

* cli_args: Add --duplicate-check-hash-function.

* server.py: compare_image_hash configurable hash function

Uses an argument added in cli_args to specify the type of hashing to default to for duplicate hash checking.  Uses an `eval()` to identify the specific hashlib class to utilize, but ultimately safely operates because we have specific options and only those options/choices in the arg parser.  So we don't have any unsafe input there.

* Add hasher() to node_helpers

* hashlib selection moved to node_helpers

* default-hashing-function instead of dupe checking hasher

This makes a default-hashing-function option instead of previous selected option.

* Use args.default_hashing_function

* Use safer handling for node_helpers.hasher()

Uses a safer handling method than `eval` to evaluate default hashing function.

* Stray parentheses are evil.

* Indentation fix.

Somehow when I hit save I didn't notice I missed a space to make indentation work proper.  Oops!
---
 comfy/cli_args.py |  1 +
 node_helpers.py   | 13 +++++++++++++
 server.py         |  7 +++++--
 3 files changed, 19 insertions(+), 2 deletions(-)

diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index 6251e3d28..2397de3d6 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -112,6 +112,7 @@ vram_group.add_argument("--lowvram", action="store_true", help="Split the unet i
 vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
+parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.")
 
 parser.add_argument("--disable-smart-memory", action="store_true", help="Force ComfyUI to agressively offload to regular ram instead of keeping models in vram when it can.")
 parser.add_argument("--deterministic", action="store_true", help="Make pytorch use slower deterministic algorithms when it can. Note that this might not make images deterministic in all cases.")
diff --git a/node_helpers.py b/node_helpers.py
index 43b9e829f..fee628790 100644
--- a/node_helpers.py
+++ b/node_helpers.py
@@ -1,3 +1,7 @@
+import hashlib
+
+from comfy.cli_args import args
+
 from PIL import ImageFile, UnidentifiedImageError
 
 def conditioning_set_values(conditioning, values={}):
@@ -22,3 +26,12 @@ def pillow(fn, arg):
         if prev_value is not None:
             ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
         return x
+
+def hasher():
+    hashfuncs = {
+        "md5": hashlib.md5,
+        "sha1": hashlib.sha1,
+        "sha256": hashlib.sha256,
+        "sha512": hashlib.sha512
+    }
+    return hashfuncs[args.default_hashing_function]
diff --git a/server.py b/server.py
index 03c4c4219..23ca2fd33 100644
--- a/server.py
+++ b/server.py
@@ -25,6 +25,7 @@ import mimetypes
 from comfy.cli_args import args
 import comfy.utils
 import comfy.model_management
+import node_helpers
 from app.frontend_management import FrontendManager
 from app.user_manager import UserManager
 
@@ -161,10 +162,12 @@ class PromptServer():
             return type_dir, dir_type
 
         def compare_image_hash(filepath, image):
+            hasher = node_helpers.hasher()
+            
             # function to compare hashes of two images to see if it already exists, fix to #3465
             if os.path.exists(filepath):
-                a = hashlib.sha256()
-                b = hashlib.sha256()
+                a = hasher()
+                b = hasher()
                 with open(filepath, "rb") as f:
                     a.update(f.read())
                     b.update(image.file.read())

From 1cde6b2eff5721175725e9cc5f80191d8276e1e8 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 16 Jul 2024 21:15:08 -0400
Subject: [PATCH 208/315] Disallow use of eval with pylint (#4033)

---
 .github/workflows/pylint.yml | 23 +++++++++++++++++++++++
 .pylintrc                    |  3 +++
 2 files changed, 26 insertions(+)
 create mode 100644 .github/workflows/pylint.yml
 create mode 100644 .pylintrc

diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml
new file mode 100644
index 000000000..5effbea35
--- /dev/null
+++ b/.github/workflows/pylint.yml
@@ -0,0 +1,23 @@
+name: Python Linting
+
+on: [push, pull_request]
+
+jobs:
+  pylint:
+    name: Run Pylint
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: 3.x
+
+    - name: Install Pylint
+      run: pip install pylint
+
+    - name: Run Pylint
+      run: pylint --rcfile=.pylintrc $(find . -type f -name "*.py")
diff --git a/.pylintrc b/.pylintrc
new file mode 100644
index 000000000..a5da56e57
--- /dev/null
+++ b/.pylintrc
@@ -0,0 +1,3 @@
+[MESSAGES CONTROL]
+disable=all
+enable=eval-used

From 281ad42df4fae74ed11c1e7ff14cff6b7db6498c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 17 Jul 2024 10:16:31 -0400
Subject: [PATCH 209/315] Fix lowvram union controlnet bug.

---
 comfy/cldm/cldm.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index 4a58c823f..1d7294bd6 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -361,7 +361,7 @@ class ControlNet(nn.Module):
             controlnet_cond = self.input_hint_block(hint[idx], emb, context)
             feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
             if idx < len(control_type):
-                feat_seq += self.task_embedding[control_type[idx]]
+                feat_seq += self.task_embedding[control_type[idx]].to(dtype=feat_seq.dtype, device=feat_seq.device)
 
             inputs.append(feat_seq.unsqueeze(1))
             condition_list.append(controlnet_cond)

From 6f7869f365ca4ec34b017f9002b9dcb9bce56b1a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 17 Jul 2024 13:05:38 -0400
Subject: [PATCH 210/315] Get clip vision image size from config.

---
 comfy/clip_vision.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py
index acc86be85..ac39c6227 100644
--- a/comfy/clip_vision.py
+++ b/comfy/clip_vision.py
@@ -34,6 +34,7 @@ class ClipVisionModel():
         with open(json_config) as f:
             config = json.load(f)
 
+        self.image_size = config.get("image_size", 224)
         self.load_device = comfy.model_management.text_encoder_device()
         offload_device = comfy.model_management.text_encoder_offload_device()
         self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
@@ -50,7 +51,7 @@ class ClipVisionModel():
 
     def encode_image(self, image):
         comfy.model_management.load_model_gpu(self.patcher)
-        pixel_values = clip_preprocess(image.to(self.load_device)).float()
+        pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size).float()
         out = self.model(pixel_values=pixel_values, intermediate_output=-2)
 
         outputs = Output()

From 855789403b346c7d7772e2c7ed0a6a327f56047f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=96=B5=E5=93=A9=E4=B8=AA=E5=92=AA?= <wailovet@163.com>
Date: Thu, 18 Jul 2024 01:12:50 +0800
Subject: [PATCH 211/315] support clip-vit-large-patch14-336 (#4042)

* support clip-vit-large-patch14-336

* support clip-vit-large-patch14-336
---
 comfy/clip_vision.py                   |  5 ++++-
 comfy/clip_vision_config_vitl_336.json | 18 ++++++++++++++++++
 2 files changed, 22 insertions(+), 1 deletion(-)
 create mode 100644 comfy/clip_vision_config_vitl_336.json

diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py
index ac39c6227..20dc3345d 100644
--- a/comfy/clip_vision.py
+++ b/comfy/clip_vision.py
@@ -94,7 +94,10 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     elif "vision_model.encoder.layers.30.layer_norm1.weight" in sd:
         json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_h.json")
     elif "vision_model.encoder.layers.22.layer_norm1.weight" in sd:
-        json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
+        if sd["vision_model.embeddings.position_embedding.weight"].shape[0] == 577:
+            json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl_336.json")
+        else:
+            json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_vision_config_vitl.json")
     else:
         return None
 
diff --git a/comfy/clip_vision_config_vitl_336.json b/comfy/clip_vision_config_vitl_336.json
new file mode 100644
index 000000000..f26945273
--- /dev/null
+++ b/comfy/clip_vision_config_vitl_336.json
@@ -0,0 +1,18 @@
+{
+  "attention_dropout": 0.0,
+  "dropout": 0.0,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 1024,
+  "image_size": 336,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-5,
+  "model_type": "clip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 24,
+  "patch_size": 14,
+  "projection_dim": 768,
+  "torch_dtype": "float32"
+}

From 374e093e09c94b528d7c9dfc337c65cc5c433ee3 Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Wed, 17 Jul 2024 13:11:10 -0700
Subject: [PATCH 212/315] Disable audio widget trying to get previews (#4044)

---
 web/extensions/core/uploadAudio.js | 1 -
 1 file changed, 1 deletion(-)

diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js
index 0ac9cb807..6cc3863a1 100644
--- a/web/extensions/core/uploadAudio.js
+++ b/web/extensions/core/uploadAudio.js
@@ -17,7 +17,6 @@ function getResourceURL(subfolder, filename, type = "input") {
     "filename=" + encodeURIComponent(filename),
     "type=" + type,
     "subfolder=" + subfolder,
-    app.getPreviewFormatParam().substring(1),
     app.getRandParam().substring(1)
   ].join("&")
 

From ff6ca2a892c25c5c6edb25c0c674445a7fe6b562 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 18 Jul 2024 17:20:05 -0400
Subject: [PATCH 213/315] Move PAG to model_patches/unet section.

Move other unet model_patches nodes to model_patches/unet section.
---
 comfy_extras/nodes_freelunch.py | 4 ++--
 comfy_extras/nodes_hypertile.py | 2 +-
 comfy_extras/nodes_pag.py       | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py
index c5ebcf26f..e3ac58447 100644
--- a/comfy_extras/nodes_freelunch.py
+++ b/comfy_extras/nodes_freelunch.py
@@ -34,7 +34,7 @@ class FreeU:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "model_patches"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, b1, b2, s1, s2):
         model_channels = model.model.model_config.unet_config["model_channels"]
@@ -73,7 +73,7 @@ class FreeU_V2:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "model_patches"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, b1, b2, s1, s2):
         model_channels = model.model.model_config.unet_config["model_channels"]
diff --git a/comfy_extras/nodes_hypertile.py b/comfy_extras/nodes_hypertile.py
index ae55d23dd..227133f39 100644
--- a/comfy_extras/nodes_hypertile.py
+++ b/comfy_extras/nodes_hypertile.py
@@ -32,7 +32,7 @@ class HyperTile:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "model_patches"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
         model_channels = model.model.model_config.unet_config["model_channels"]
diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py
index 63f43fd62..aec78bd8a 100644
--- a/comfy_extras/nodes_pag.py
+++ b/comfy_extras/nodes_pag.py
@@ -19,7 +19,7 @@ class PerturbedAttentionGuidance:
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
 
-    CATEGORY = "_for_testing"
+    CATEGORY = "model_patches/unet"
 
     def patch(self, model, scale):
         unet_block = "middle"

From 011b11d8d767da3355555fe1253b162f55e985c5 Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Thu, 18 Jul 2024 18:59:18 -0700
Subject: [PATCH 214/315] LoadAudio restores file value from workflow (#4043)

* LoadAudio restores file value from workflow

* use onAfterGraphConfigured

* Don't use anonnymous function
---
 web/extensions/core/uploadAudio.js | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js
index 6cc3863a1..9dfa029bf 100644
--- a/web/extensions/core/uploadAudio.js
+++ b/web/extensions/core/uploadAudio.js
@@ -149,6 +149,15 @@ app.registerExtension({
         }
         audioWidget.callback = onAudioWidgetUpdate
 
+        // Load saved audio file widget values if restoring from workflow
+        const onGraphConfigured = node.onGraphConfigured;
+        node.onGraphConfigured = function() {
+          onGraphConfigured?.apply(this, arguments)
+          if (audioWidget.value) {
+            onAudioWidgetUpdate()
+          }
+        }
+
         const fileInput = document.createElement("input")
         fileInput.type = "file"
         fileInput.accept = "audio/*"

From 6ab8cad22ee376c0a9a506924cca278bb650afcc Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 19 Jul 2024 17:44:56 -0400
Subject: [PATCH 215/315] Implement beta sampling scheduler.

It is based on: https://arxiv.org/abs/2407.12173

Add "beta" to the list of schedulers and the BetaSamplingScheduler node.
---
 comfy/samplers.py                    | 18 +++++++++++++++++-
 comfy_extras/nodes_custom_sampler.py | 20 ++++++++++++++++++++
 2 files changed, 37 insertions(+), 1 deletion(-)

diff --git a/comfy/samplers.py b/comfy/samplers.py
index bbf9219f7..0ab08a4cd 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -6,6 +6,8 @@ from comfy import model_management
 import math
 import logging
 import comfy.sampler_helpers
+import scipy
+import numpy
 
 def get_area_and_mult(conds, x_in, timestep_in):
     dims = tuple(x_in.shape[2:])
@@ -337,6 +339,18 @@ def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
+# Implemented based on: https://arxiv.org/abs/2407.12173
+def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
+    total_timesteps = (len(model_sampling.sigmas) - 1)
+    ts = 1 - numpy.linspace(0, 1, steps, endpoint=False)
+    ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps)
+
+    sigs = []
+    for t in ts:
+        sigs += [float(model_sampling.sigmas[int(t)])]
+    sigs += [0.0]
+    return torch.FloatTensor(sigs)
+
 def get_mask_aabb(masks):
     if masks.numel() == 0:
         return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
@@ -703,7 +717,7 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
     return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
 
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
+SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta"]
 SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
 
 def calculate_sigmas(model_sampling, scheduler_name, steps):
@@ -719,6 +733,8 @@ def calculate_sigmas(model_sampling, scheduler_name, steps):
         sigmas = ddim_scheduler(model_sampling, steps)
     elif scheduler_name == "sgm_uniform":
         sigmas = normal_scheduler(model_sampling, steps, sgm=True)
+    elif scheduler_name == "beta":
+        sigmas = beta_scheduler(model_sampling, steps)
     else:
         logging.error("error invalid scheduler {}".format(scheduler_name))
     return sigmas
diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
index 64a8c0631..b7ab88c27 100644
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -111,6 +111,25 @@ class SDTurboScheduler:
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )
 
+class BetaSamplingScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+                     "alpha": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}),
+                     "beta": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}),
+                      }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, model, steps, alpha, beta):
+        sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta)
+        return (sigmas, )
+
 class VPScheduler:
     @classmethod
     def INPUT_TYPES(s):
@@ -638,6 +657,7 @@ NODE_CLASS_MAPPINGS = {
     "ExponentialScheduler": ExponentialScheduler,
     "PolyexponentialScheduler": PolyexponentialScheduler,
     "VPScheduler": VPScheduler,
+    "BetaSamplingScheduler": BetaSamplingScheduler,
     "SDTurboScheduler": SDTurboScheduler,
     "KSamplerSelect": KSamplerSelect,
     "SamplerEulerAncestral": SamplerEulerAncestral,

From 11b74147ee67944c1707b4cc0b4db1cedc73aacc Mon Sep 17 00:00:00 2001
From: Greg Wainer <gregcebowainer@hotmail.com>
Date: Fri, 19 Jul 2024 17:39:04 -0500
Subject: [PATCH 216/315] Fix/webp exif little endian (#4061)

* Fix for isLittleEndian flag in parseExifData.

* Add break after reading first exif chunk in getWebpMetadata.
---
 web/scripts/pnginfo.js | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js
index 2c03cf74a..8b1b2c61c 100644
--- a/web/scripts/pnginfo.js
+++ b/web/scripts/pnginfo.js
@@ -49,7 +49,7 @@ export function getPngMetadata(file) {
 
 function parseExifData(exifData) {
 	// Check for the correct TIFF header (0x4949 for little-endian or 0x4D4D for big-endian)
-	const isLittleEndian = new Uint16Array(exifData.slice(0, 2))[0] === 0x4949;
+	const isLittleEndian = String.fromCharCode(...exifData.slice(0, 2)) === "II";
 
 	// Function to read 16-bit and 32-bit integers from binary data
 	function readInt(offset, isLittleEndian, length) {
@@ -134,6 +134,7 @@ export function getWebpMetadata(file) {
 						let index = value.indexOf(':');
 						txt_chunks[value.slice(0, index)] = value.slice(index + 1);
 					}
+					break;
 				}
 
 				offset += 8 + chunk_length;

From 95fa9545f167ccf4010849c70045a67a8800aa31 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 20 Jul 2024 12:27:42 -0400
Subject: [PATCH 217/315] Only append zero to noise schedule if last sigma
 isn't zero.

---
 comfy/samplers.py | 20 ++++++++++++++++----
 1 file changed, 16 insertions(+), 4 deletions(-)

diff --git a/comfy/samplers.py b/comfy/samplers.py
index 0ab08a4cd..3f7633814 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -313,13 +313,18 @@ def simple_scheduler(model_sampling, steps):
 def ddim_scheduler(model_sampling, steps):
     s = model_sampling
     sigs = []
-    ss = max(len(s.sigmas) // steps, 1)
     x = 1
+    if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001):
+        steps += 1
+        sigs = []
+    else:
+        sigs = [0.0]
+
+    ss = max(len(s.sigmas) // steps, 1)
     while x < len(s.sigmas):
         sigs += [float(s.sigmas[x])]
         x += ss
     sigs = sigs[::-1]
-    sigs += [0.0]
     return torch.FloatTensor(sigs)
 
 def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
@@ -327,16 +332,23 @@ def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
     start = s.timestep(s.sigma_max)
     end = s.timestep(s.sigma_min)
 
+    append_zero = True
     if sgm:
         timesteps = torch.linspace(start, end, steps + 1)[:-1]
     else:
+        if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001):
+            steps += 1
+            append_zero = False
         timesteps = torch.linspace(start, end, steps)
 
     sigs = []
     for x in range(len(timesteps)):
         ts = timesteps[x]
-        sigs.append(s.sigma(ts))
-    sigs += [0.0]
+        sigs.append(float(s.sigma(ts)))
+
+    if append_zero:
+        sigs += [0.0]
+
     return torch.FloatTensor(sigs)
 
 # Implemented based on: https://arxiv.org/abs/2407.12173

From 5b69cfe7c343e8672fc350ec35e17f0d046297ca Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Sun, 21 Jul 2024 15:29:10 -0400
Subject: [PATCH 218/315] Add timestamp to execution messages (#4076)

* Add timestamp to execution messages

* Add execution_end message

* Rename to execution_success
---
 execution.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/execution.py b/execution.py
index 8b3d116c6..0a2e62e7e 100644
--- a/execution.py
+++ b/execution.py
@@ -3,6 +3,7 @@ import copy
 import logging
 import threading
 import heapq
+import time
 import traceback
 import inspect
 from typing import List, Literal, NamedTuple, Optional
@@ -283,7 +284,11 @@ class PromptExecutor:
         self.success = True
         self.old_prompt = {}
 
-    def add_message(self, event, data, broadcast: bool):
+    def add_message(self, event, data: dict, broadcast: bool):
+        data = {
+            **data,
+            "timestamp": int(time.time() * 1000),
+        }
         self.status_messages.append((event, data))
         if self.server.client_id is not None or broadcast:
             self.server.send_sync(event, data, self.server.client_id)
@@ -394,6 +399,9 @@ class PromptExecutor:
                 if self.success is not True:
                     self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex)
                     break
+            else:
+                # Only execute when the while-loop ends without break
+                self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
 
             for x in executed:
                 self.old_prompt[x] = copy.deepcopy(prompt[x])

From f836e69346844c65dcf2418346db9a9a9b32a445 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 21 Jul 2024 16:16:45 -0400
Subject: [PATCH 219/315] Fix bug with SaveAudio node with --gpu-only

---
 comfy_extras/nodes_audio.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 64d241e98..6f0e26365 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -147,7 +147,7 @@ class SaveAudio:
                 for x in extra_pnginfo:
                     metadata[x] = json.dumps(extra_pnginfo[x])
 
-        for (batch_number, waveform) in enumerate(audio["waveform"]):
+        for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
             filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
             file = f"{filename_with_batch_num}_{counter:05}_.flac"
 

From 6045ed31f898e278e4693f2a4e210393fc9153d0 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Sun, 21 Jul 2024 21:15:01 -0400
Subject: [PATCH 220/315] Supress frontend exception on unhandled message type
 (#4078)

* Supress frontend exception on unhandled message type

* nit
---
 web/scripts/api.js | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/web/scripts/api.js b/web/scripts/api.js
index 39f0a9bb2..03c3fb607 100644
--- a/web/scripts/api.js
+++ b/web/scripts/api.js
@@ -136,6 +136,9 @@ class ComfyApi extends EventTarget {
 					    case "execution_start":
 						    this.dispatchEvent(new CustomEvent("execution_start", { detail: msg.data }));
 						    break;
+					    case "execution_success":
+						    this.dispatchEvent(new CustomEvent("execution_success", { detail: msg.data }));
+						    break;
 					    case "execution_error":
 						    this.dispatchEvent(new CustomEvent("execution_error", { detail: msg.data }));
 						    break;

From 4151fbfa8ab705d0a7e632c7d7c23ea5c436a60b Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Mon, 22 Jul 2024 11:27:32 -0400
Subject: [PATCH 221/315] Add error message on union controlnet (#4081)

---
 comfy/cldm/cldm.py               | 13 +++++++++++++
 comfy/cldm/control_types.py      | 11 +++++++++++
 comfy_extras/nodes_controlnet.py | 12 +-----------
 3 files changed, 25 insertions(+), 11 deletions(-)
 create mode 100644 comfy/cldm/control_types.py

diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index 1d7294bd6..9ec64a227 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -13,6 +13,7 @@ from ..ldm.modules.diffusionmodules.util import (
 from ..ldm.modules.attention import SpatialTransformer
 from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
 from ..ldm.util import exists
+from .control_types import UNION_CONTROLNET_TYPES
 from collections import OrderedDict
 import comfy.ops
 from comfy.ldm.modules.attention import optimized_attention
@@ -390,6 +391,18 @@ class ControlNet(nn.Module):
         if self.control_add_embedding is not None: #Union Controlnet
             control_type = kwargs.get("control_type", [])
 
+            if any([c >= self.num_control_type for c in control_type]):
+                max_type = max(control_type)
+                max_type_name = {
+                    v: k for k, v in UNION_CONTROLNET_TYPES.items()
+                }[max_type]
+                raise ValueError(
+                    f"Control type {max_type_name}({max_type}) is out of range for the number of control types" +
+                    f"({self.num_control_type}) supported.\n" +
+                    "Please consider using the ProMax ControlNet Union model.\n" +
+                    "https://huggingface.co/xinsir/controlnet-union-sdxl-1.0/tree/main"
+                )
+
             emb += self.control_add_embedding(control_type, emb.dtype, emb.device)
             if len(control_type) > 0:
                 if len(hint.shape) < 5:
diff --git a/comfy/cldm/control_types.py b/comfy/cldm/control_types.py
new file mode 100644
index 000000000..fd6e56e8f
--- /dev/null
+++ b/comfy/cldm/control_types.py
@@ -0,0 +1,11 @@
+UNION_CONTROLNET_TYPES = {
+    "auto": -1,
+    "openpose": 0,
+    "depth": 1,
+    "hed/pidi/scribble/ted": 2,
+    "canny/lineart/anime_lineart/mlsd": 3,
+    "normal": 4,
+    "segment": 5,
+    "tile": 6,
+    "repaint": 7,
+}
diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py
index ef7cfc6ab..566d51e33 100644
--- a/comfy_extras/nodes_controlnet.py
+++ b/comfy_extras/nodes_controlnet.py
@@ -1,14 +1,4 @@
-
-UNION_CONTROLNET_TYPES = {"auto": -1,
-                          "openpose": 0,
-                          "depth": 1,
-                          "hed/pidi/scribble/ted": 2,
-                          "canny/lineart/anime_lineart/mlsd": 3,
-                          "normal": 4,
-                          "segment": 5,
-                          "tile": 6,
-                          "repaint": 7,
-                        }
+from comfy.cldm.control_types import UNION_CONTROLNET_TYPES
 
 class SetUnionControlNetType:
     @classmethod

From b2c995f623fd7169db87dfd6d356e535d0bb99ce Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 22 Jul 2024 11:30:38 -0400
Subject: [PATCH 222/315] "auto" type is only relevant to the
 SetUnionControlNetType node.

---
 comfy/cldm/control_types.py      | 1 -
 comfy_extras/nodes_controlnet.py | 4 ++--
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/comfy/cldm/control_types.py b/comfy/cldm/control_types.py
index fd6e56e8f..4128631a3 100644
--- a/comfy/cldm/control_types.py
+++ b/comfy/cldm/control_types.py
@@ -1,5 +1,4 @@
 UNION_CONTROLNET_TYPES = {
-    "auto": -1,
     "openpose": 0,
     "depth": 1,
     "hed/pidi/scribble/ted": 2,
diff --git a/comfy_extras/nodes_controlnet.py b/comfy_extras/nodes_controlnet.py
index 566d51e33..0773c8a5b 100644
--- a/comfy_extras/nodes_controlnet.py
+++ b/comfy_extras/nodes_controlnet.py
@@ -4,7 +4,7 @@ class SetUnionControlNetType:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"control_net": ("CONTROL_NET", ),
-                             "type": (list(UNION_CONTROLNET_TYPES.keys()),)
+                             "type": (["auto"] + list(UNION_CONTROLNET_TYPES.keys()),)
                              }}
 
     CATEGORY = "conditioning/controlnet"
@@ -14,7 +14,7 @@ class SetUnionControlNetType:
 
     def set_controlnet_type(self, control_net, type):
         control_net = control_net.copy()
-        type_number = UNION_CONTROLNET_TYPES[type]
+        type_number = UNION_CONTROLNET_TYPES.get(type, -1)
         if type_number >= 0:
             control_net.set_extra_arg("control_type", [type_number])
         else:

From 14764aa2e2e2b282c4a4dffbfab4c01d3e46e8a7 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 22 Jul 2024 12:21:45 -0400
Subject: [PATCH 223/315] Rename LLAMATokenizer to SPieceTokenizer.

---
 comfy/text_encoders/aura_t5.py                                | 4 ++--
 .../text_encoders/{llama_tokenizer.py => spiece_tokenizer.py} | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)
 rename comfy/text_encoders/{llama_tokenizer.py => spiece_tokenizer.py} (90%)

diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py
index 6b9e4fe53..409867af3 100644
--- a/comfy/text_encoders/aura_t5.py
+++ b/comfy/text_encoders/aura_t5.py
@@ -1,5 +1,5 @@
 from comfy import sd1_clip
-from .llama_tokenizer import LLAMATokenizer
+from .spiece_tokenizer import SPieceTokenizer
 import comfy.text_encoders.t5
 import os
 
@@ -11,7 +11,7 @@ class PT5XlModel(sd1_clip.SDClipModel):
 class PT5XlTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None):
         tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=LLAMATokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
 
 class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None):
diff --git a/comfy/text_encoders/llama_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py
similarity index 90%
rename from comfy/text_encoders/llama_tokenizer.py
rename to comfy/text_encoders/spiece_tokenizer.py
index a6db1da62..d611d5bb7 100644
--- a/comfy/text_encoders/llama_tokenizer.py
+++ b/comfy/text_encoders/spiece_tokenizer.py
@@ -1,9 +1,9 @@
 import os
 
-class LLAMATokenizer:
+class SPieceTokenizer:
     @staticmethod
     def from_pretrained(path):
-        return LLAMATokenizer(path)
+        return SPieceTokenizer(path)
 
     def __init__(self, tokenizer_path):
         import sentencepiece

From 334ba48cea2961994e92c2fb25de9417b19897ed Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 23 Jul 2024 14:13:32 -0400
Subject: [PATCH 224/315] More generic unet prefix detection code.

---
 comfy/model_detection.py | 21 +++++++++++++++------
 1 file changed, 15 insertions(+), 6 deletions(-)

diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index c62e2b822..ae88eeb97 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -261,13 +261,22 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
         return model_config
 
 def unet_prefix_from_state_dict(state_dict):
-    if "model.model.postprocess_conv.weight" in state_dict: #audio models
-        unet_key_prefix = "model.model."
-    elif "model.double_layers.0.attn.w1q.weight" in state_dict: #aura flow
-        unet_key_prefix = "model."
+    candidates = ["model.diffusion_model.", #ldm/sgm models
+                  "model.model.", #audio models
+                  ]
+    counts = {k: 0 for k in candidates}
+    for k in state_dict:
+        for c in candidates:
+            if k.startswith(c):
+                counts[c] += 1
+                break
+
+    top = max(counts, key=counts.get)
+    if counts[top] > 5:
+        return top
     else:
-        unet_key_prefix = "model.diffusion_model."
-    return unet_key_prefix
+        return "model." #aura flow and others
+
 
 def convert_config(unet_config):
     new_config = unet_config.copy()

From 88ed89303463ab030654ef87f7f54d72ee3979bb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 23 Jul 2024 14:17:42 -0400
Subject: [PATCH 225/315] Allow SPieceTokenizer to load model from a byte
 string.

---
 comfy/text_encoders/spiece_tokenizer.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py
index d611d5bb7..fa303da0b 100644
--- a/comfy/text_encoders/spiece_tokenizer.py
+++ b/comfy/text_encoders/spiece_tokenizer.py
@@ -1,14 +1,18 @@
 import os
 
 class SPieceTokenizer:
+    add_eos = True
+
     @staticmethod
     def from_pretrained(path):
         return SPieceTokenizer(path)
 
     def __init__(self, tokenizer_path):
         import sentencepiece
-        self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path)
-        self.end = self.tokenizer.eos_id()
+        if isinstance(tokenizer_path, bytes):
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_eos=self.add_eos)
+        else:
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos)
 
     def get_vocab(self):
         out = {}
@@ -18,5 +22,4 @@ class SPieceTokenizer:
 
     def __call__(self, string):
         out = self.tokenizer.encode(string)
-        out += [self.end]
         return {"input_ids": out}

From 0a4c49c57ca792535174d27aea87449257527b2f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 23 Jul 2024 15:35:28 -0400
Subject: [PATCH 226/315] Support MT5.

---
 comfy/text_encoders/t5.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
index 448c5aad3..ba628478d 100644
--- a/comfy/text_encoders/t5.py
+++ b/comfy/text_encoders/t5.py
@@ -223,7 +223,7 @@ class T5(torch.nn.Module):
         self.num_layers = config_dict["num_layers"]
         model_dim = config_dict["d_model"]
 
-        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] == "t5", dtype, device, operations)
+        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations)
         self.dtype = dtype
         self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
 

From 10b43ceea52d86395d10a7029eda2822fd65dfd1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 24 Jul 2024 01:12:59 -0400
Subject: [PATCH 227/315] Remove duplicate code.

---
 comfy/ldm/modules/diffusionmodules/mmdit.py | 29 ++-------------------
 1 file changed, 2 insertions(+), 27 deletions(-)

diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
index 927451534..f37f7ff77 100644
--- a/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -7,6 +7,7 @@ import torch
 import torch.nn as nn
 from .. import attention
 from einops import rearrange, repeat
+from .util import timestep_embedding
 
 def default(x, y):
     if x is not None:
@@ -230,34 +231,8 @@ class TimestepEmbedder(nn.Module):
         )
         self.frequency_embedding_size = frequency_embedding_size
 
-    @staticmethod
-    def timestep_embedding(t, dim, max_period=10000):
-        """
-        Create sinusoidal timestep embeddings.
-        :param t: a 1-D Tensor of N indices, one per batch element.
-                          These may be fractional.
-        :param dim: the dimension of the output.
-        :param max_period: controls the minimum frequency of the embeddings.
-        :return: an (N, D) Tensor of positional embeddings.
-        """
-        half = dim // 2
-        freqs = torch.exp(
-            -math.log(max_period)
-            * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
-            / half
-        )
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat(
-                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
-            )
-        if torch.is_floating_point(t):
-            embedding = embedding.to(dtype=t.dtype)
-        return embedding
-
     def forward(self, t, dtype, **kwargs):
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_freq = timestep_embedding(t, self.frequency_embedding_size).to(dtype)
         t_emb = self.mlp(t_freq)
         return t_emb
 

From 19944ad252106e46a40e0b952c86c1cebc8486ab Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 24 Jul 2024 12:49:29 -0400
Subject: [PATCH 228/315] Add code to fix issues with new pytorch version on
 the standalone.

---
 fix_torch.py | 16 ++++++++++++++++
 main.py      |  6 ++++++
 2 files changed, 22 insertions(+)
 create mode 100644 fix_torch.py

diff --git a/fix_torch.py b/fix_torch.py
new file mode 100644
index 000000000..b60a0bbb4
--- /dev/null
+++ b/fix_torch.py
@@ -0,0 +1,16 @@
+import importlib.util
+import shutil
+import os
+import ctypes
+import logging
+
+
+torch_spec = importlib.util.find_spec("torch")
+for folder in torch_spec.submodule_search_locations:
+    lib_folder = os.path.join(folder, "lib")
+    test_file = os.path.join(lib_folder, "fbgemm.dll")
+    try:
+        mydll = ctypes.cdll.LoadLibrary(test_file)
+    except FileNotFoundError as e:
+        logging.warning("Detected pytorch version with libomp issue, patching.")
+        shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), os.path.join(lib_folder, "libomp140.x86_64.dll"))
diff --git a/main.py b/main.py
index 196351a3d..479643b84 100644
--- a/main.py
+++ b/main.py
@@ -74,6 +74,12 @@ if __name__ == "__main__":
 
     import cuda_malloc
 
+if args.windows_standalone_build:
+    try:
+        import fix_torch
+    except:
+        pass
+
 import comfy.utils
 import yaml
 

From ce80e69fb89f5f8e48273977e94b7a3b0421f6e6 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 24 Jul 2024 13:50:34 -0400
Subject: [PATCH 229/315] Avoid loading the dll when it's not necessary.

---
 fix_torch.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/fix_torch.py b/fix_torch.py
index b60a0bbb4..e350f5c7d 100644
--- a/fix_torch.py
+++ b/fix_torch.py
@@ -9,8 +9,16 @@ torch_spec = importlib.util.find_spec("torch")
 for folder in torch_spec.submodule_search_locations:
     lib_folder = os.path.join(folder, "lib")
     test_file = os.path.join(lib_folder, "fbgemm.dll")
+    dest = os.path.join(lib_folder, "libomp140.x86_64.dll")
+    if os.path.exists(dest):
+        break
+
+    with open(test_file, 'rb') as f:
+        contents = f.read()
+        if b"libomp140.x86_64.dll" not in contents:
+            break
     try:
         mydll = ctypes.cdll.LoadLibrary(test_file)
     except FileNotFoundError as e:
         logging.warning("Detected pytorch version with libomp issue, patching.")
-        shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), os.path.join(lib_folder, "libomp140.x86_64.dll"))
+        shutil.copyfile(os.path.join(lib_folder, "libiomp5md.dll"), dest)

From 10c919f4c77b3615f0efa9014e8b77f294c23a2d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 24 Jul 2024 16:43:53 -0400
Subject: [PATCH 230/315] Make it possible to load tokenizer data from
 checkpoints.

---
 comfy/sd.py                             |  6 +++---
 comfy/sd1_clip.py                       |  6 +++---
 comfy/sd2_clip.py                       |  6 +++---
 comfy/sdxl_clip.py                      | 10 +++++-----
 comfy/text_encoders/aura_t5.py          |  6 +++---
 comfy/text_encoders/sa_t5.py            |  6 +++---
 comfy/text_encoders/sd3_clip.py         | 13 ++-----------
 comfy/text_encoders/spiece_tokenizer.py |  4 ++++
 8 files changed, 26 insertions(+), 31 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 17df5faff..fe6ce4c96 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -60,7 +60,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
 
 
 class CLIP:
-    def __init__(self, target=None, embedding_directory=None, no_init=False):
+    def __init__(self, target=None, embedding_directory=None, no_init=False, tokenizer_data={}):
         if no_init:
             return
         params = target.params.copy()
@@ -79,7 +79,7 @@ class CLIP:
             if not model_management.supports_cast(load_device, dt):
                 load_device = offload_device
 
-        self.tokenizer = tokenizer(embedding_directory=embedding_directory)
+        self.tokenizer = tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
         self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
         self.layer_idx = None
         logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device))
@@ -520,7 +520,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         if clip_target is not None:
             clip_sd = model_config.process_clip_state_dict(sd)
             if len(clip_sd) > 0:
-                clip = CLIP(clip_target, embedding_directory=embedding_directory)
+                clip = CLIP(clip_target, embedding_directory=embedding_directory, tokenizer_data=clip_sd)
                 m, u = clip.load_sd(clip_sd, full_model=True)
                 if len(m) > 0:
                     m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 565ad69da..ea66da00b 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -386,7 +386,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None, tokenizer_data={}):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
@@ -521,10 +521,10 @@ class SDTokenizer:
 
 
 class SD1Tokenizer:
-    def __init__(self, embedding_directory=None, clip_name="l", tokenizer=SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer):
         self.clip_name = clip_name
         self.clip = "clip_{}".format(self.clip_name)
-        setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory))
+        setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
 
     def tokenize_with_weights(self, text:str, return_word_ids=False):
         out = {}
diff --git a/comfy/sd2_clip.py b/comfy/sd2_clip.py
index d14b44544..8ea8e1cd0 100644
--- a/comfy/sd2_clip.py
+++ b/comfy/sd2_clip.py
@@ -11,12 +11,12 @@ class SD2ClipHModel(sd1_clip.SDClipModel):
         super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0})
 
 class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
+    def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
         super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024)
 
 class SD2Tokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="h", tokenizer=SD2ClipHTokenizer)
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer)
 
 class SD2ClipModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, **kwargs):
diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py
index 1257cba1e..57c30f2e6 100644
--- a/comfy/sdxl_clip.py
+++ b/comfy/sdxl_clip.py
@@ -16,12 +16,12 @@ class SDXLClipG(sd1_clip.SDClipModel):
         return super().load_sd(sd)
 
 class SDXLClipGTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
+    def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
         super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
 
 
 class SDXLTokenizer:
-    def __init__(self, embedding_directory=None):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
         self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
         self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory)
 
@@ -68,12 +68,12 @@ class SDXLRefinerClipModel(sd1_clip.SD1ClipModel):
 
 
 class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, tokenizer_path=None, embedding_directory=None):
+    def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
         super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
 
 class StableCascadeTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="g", tokenizer=StableCascadeClipGTokenizer)
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="g", tokenizer=StableCascadeClipGTokenizer)
 
 class StableCascadeClipG(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None):
diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py
index 409867af3..8500c7b70 100644
--- a/comfy/text_encoders/aura_t5.py
+++ b/comfy/text_encoders/aura_t5.py
@@ -9,13 +9,13 @@ class PT5XlModel(sd1_clip.SDClipModel):
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
 
 class PT5XlTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model")
         super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
 
 class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer)
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer)
 
 class AuraT5Model(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, **kwargs):
diff --git a/comfy/text_encoders/sa_t5.py b/comfy/text_encoders/sa_t5.py
index 038558e7a..189f8c181 100644
--- a/comfy/text_encoders/sa_t5.py
+++ b/comfy/text_encoders/sa_t5.py
@@ -9,13 +9,13 @@ class T5BaseModel(sd1_clip.SDClipModel):
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
 
 class T5BaseTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
         super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128)
 
 class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="t5base", tokenizer=T5BaseTokenizer)
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5base", tokenizer=T5BaseTokenizer)
 
 class SAT5Model(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None, **kwargs):
diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
index 70127e509..286c5dc05 100644
--- a/comfy/text_encoders/sd3_clip.py
+++ b/comfy/text_encoders/sd3_clip.py
@@ -13,22 +13,13 @@ class T5XXLModel(sd1_clip.SDClipModel):
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5)
 
 class T5XXLTokenizer(sd1_clip.SDTokenizer):
-    def __init__(self, embedding_directory=None):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
         super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
 
-class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer):
-    def __init__(self, embedding_directory=None):
-        super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
-
-class SDT5XXLModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, **kwargs):
-        super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs)
-
-
 
 class SD3Tokenizer:
-    def __init__(self, embedding_directory=None):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
         self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
         self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py
index fa303da0b..deb026ffd 100644
--- a/comfy/text_encoders/spiece_tokenizer.py
+++ b/comfy/text_encoders/spiece_tokenizer.py
@@ -1,4 +1,5 @@
 import os
+import torch
 
 class SPieceTokenizer:
     add_eos = True
@@ -9,6 +10,9 @@ class SPieceTokenizer:
 
     def __init__(self, tokenizer_path):
         import sentencepiece
+        if torch.is_tensor(tokenizer_path):
+            tokenizer_path = tokenizer_path.numpy().tobytes()
+
         if isinstance(tokenizer_path, bytes):
             self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_eos=self.add_eos)
         else:

From f87810cd3ed2cc3922811422181d0572f98b103d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 25 Jul 2024 10:52:09 -0400
Subject: [PATCH 231/315] Let tokenizers return weights to be stored in the
 saved checkpoint.

---
 comfy/sd.py                     | 6 +++++-
 comfy/sd1_clip.py               | 4 ++++
 comfy/sdxl_clip.py              | 3 +++
 comfy/text_encoders/sd3_clip.py | 3 +++
 4 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index fe6ce4c96..c6fcd810c 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -135,7 +135,11 @@ class CLIP:
             return self.cond_stage_model.load_sd(sd)
 
     def get_sd(self):
-        return self.cond_stage_model.state_dict()
+        sd_clip = self.cond_stage_model.state_dict()
+        sd_tokenizer = self.tokenizer.state_dict()
+        for k in sd_tokenizer:
+            sd_clip[k] = sd_tokenizer[k]
+        return sd_clip
 
     def load_model(self):
         model_management.load_model_gpu(self.patcher)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index ea66da00b..c7bc1e4dc 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -519,6 +519,8 @@ class SDTokenizer:
     def untokenize(self, token_weight_pair):
         return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
 
+    def state_dict(self):
+        return {}
 
 class SD1Tokenizer:
     def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer):
@@ -534,6 +536,8 @@ class SD1Tokenizer:
     def untokenize(self, token_weight_pair):
         return getattr(self, self.clip).untokenize(token_weight_pair)
 
+    def state_dict(self):
+        return {}
 
 class SD1ClipModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, name=None, **kwargs):
diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py
index 57c30f2e6..6e6b87d62 100644
--- a/comfy/sdxl_clip.py
+++ b/comfy/sdxl_clip.py
@@ -34,6 +34,9 @@ class SDXLTokenizer:
     def untokenize(self, token_weight_pair):
         return self.clip_g.untokenize(token_weight_pair)
 
+    def state_dict(self):
+        return {}
+
 class SDXLClipModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None):
         super().__init__()
diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
index 286c5dc05..b01fad221 100644
--- a/comfy/text_encoders/sd3_clip.py
+++ b/comfy/text_encoders/sd3_clip.py
@@ -34,6 +34,9 @@ class SD3Tokenizer:
     def untokenize(self, token_weight_pair):
         return self.clip_g.untokenize(token_weight_pair)
 
+    def state_dict(self):
+        return {}
+
 class SD3ClipModel(torch.nn.Module):
     def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None):
         super().__init__()

From a5f4292f9f41ab78759e6e2de490fe6240f5d9ea Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 25 Jul 2024 18:21:08 -0400
Subject: [PATCH 232/315] Basic hunyuan dit implementation. (#4102)

* Let tokenizers return weights to be stored in the saved checkpoint.

* Basic hunyuan dit implementation.

* Fix some resolutions not working.

* Support hydit checkpoint save.

* Init with right dtype.

* Switch to optimized attention in pooler.

* Fix black images on hunyuan dit.
---
 comfy/ldm/hydit/attn_layers.py                |   219 +
 comfy/ldm/hydit/models.py                     |   402 +
 comfy/ldm/hydit/poolers.py                    |    36 +
 comfy/ldm/hydit/posemb_layers.py              |   224 +
 comfy/model_base.py                           |    33 +
 comfy/model_detection.py                      |    13 +
 comfy/sd.py                                   |     5 +
 comfy/supported_models.py                     |    42 +-
 comfy/text_encoders/hydit.py                  |   120 +
 comfy/text_encoders/hydit_clip.json           |    35 +
 .../special_tokens_map.json                   |     7 +
 .../tokenizer_config.json                     |    16 +
 .../hydit_clip_tokenizer/vocab.txt            | 47020 ++++++++++++++++
 comfy/text_encoders/mt5_config_xl.json        |    22 +
 comfy/text_encoders/spiece_tokenizer.py       |     3 +
 15 files changed, 48196 insertions(+), 1 deletion(-)
 create mode 100644 comfy/ldm/hydit/attn_layers.py
 create mode 100644 comfy/ldm/hydit/models.py
 create mode 100644 comfy/ldm/hydit/poolers.py
 create mode 100644 comfy/ldm/hydit/posemb_layers.py
 create mode 100644 comfy/text_encoders/hydit.py
 create mode 100644 comfy/text_encoders/hydit_clip.json
 create mode 100644 comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json
 create mode 100644 comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json
 create mode 100644 comfy/text_encoders/hydit_clip_tokenizer/vocab.txt
 create mode 100644 comfy/text_encoders/mt5_config_xl.json

diff --git a/comfy/ldm/hydit/attn_layers.py b/comfy/ldm/hydit/attn_layers.py
new file mode 100644
index 000000000..920b84286
--- /dev/null
+++ b/comfy/ldm/hydit/attn_layers.py
@@ -0,0 +1,219 @@
+import torch
+import torch.nn as nn
+from typing import Tuple, Union, Optional
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def reshape_for_broadcast(freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]], x: torch.Tensor, head_first=False):
+    """
+    Reshape frequency tensor for broadcasting it with another tensor.
+
+    This function reshapes the frequency tensor to have the same shape as the target tensor 'x'
+    for the purpose of broadcasting the frequency tensor during element-wise operations.
+
+    Args:
+        freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Frequency tensor to be reshaped.
+        x (torch.Tensor): Target tensor for broadcasting compatibility.
+        head_first (bool): head dimension first (except batch dim) or not.
+
+    Returns:
+        torch.Tensor: Reshaped frequency tensor.
+
+    Raises:
+        AssertionError: If the frequency tensor doesn't match the expected shape.
+        AssertionError: If the target tensor 'x' doesn't have the expected number of dimensions.
+    """
+    ndim = x.ndim
+    assert 0 <= 1 < ndim
+
+    if isinstance(freqs_cis, tuple):
+        # freqs_cis: (cos, sin) in real space
+        if head_first:
+            assert freqs_cis[0].shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
+            shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        else:
+            assert freqs_cis[0].shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis[0].shape} does not match x shape {x.shape}'
+            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis[0].view(*shape), freqs_cis[1].view(*shape)
+    else:
+        # freqs_cis: values in complex space
+        if head_first:
+            assert freqs_cis.shape == (x.shape[-2], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
+            shape = [d if i == ndim - 2 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        else:
+            assert freqs_cis.shape == (x.shape[1], x.shape[-1]), f'freqs_cis shape {freqs_cis.shape} does not match x shape {x.shape}'
+            shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+        return freqs_cis.view(*shape)
+
+
+def rotate_half(x):
+    x_real, x_imag = x.float().reshape(*x.shape[:-1], -1, 2).unbind(-1)  # [B, S, H, D//2]
+    return torch.stack([-x_imag, x_real], dim=-1).flatten(3)
+
+
+def apply_rotary_emb(
+        xq: torch.Tensor,
+        xk: Optional[torch.Tensor],
+        freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]],
+        head_first: bool = False,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Apply rotary embeddings to input tensors using the given frequency tensor.
+
+    This function applies rotary embeddings to the given query 'xq' and key 'xk' tensors using the provided
+    frequency tensor 'freqs_cis'. The input tensors are reshaped as complex numbers, and the frequency tensor
+    is reshaped for broadcasting compatibility. The resulting tensors contain rotary embeddings and are
+    returned as real tensors.
+
+    Args:
+        xq (torch.Tensor): Query tensor to apply rotary embeddings. [B, S, H, D]
+        xk (torch.Tensor): Key tensor to apply rotary embeddings.   [B, S, H, D]
+        freqs_cis (Union[torch.Tensor, Tuple[torch.Tensor]]): Precomputed frequency tensor for complex exponentials.
+        head_first (bool): head dimension first (except batch dim) or not.
+
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor and key tensor with rotary embeddings.
+
+    """
+    xk_out = None
+    if isinstance(freqs_cis, tuple):
+        cos, sin = reshape_for_broadcast(freqs_cis, xq, head_first)    # [S, D]
+        cos, sin = cos.to(xq.device), sin.to(xq.device)
+        xq_out = (xq.float() * cos + rotate_half(xq.float()) * sin).type_as(xq)
+        if xk is not None:
+            xk_out = (xk.float() * cos + rotate_half(xk.float()) * sin).type_as(xk)
+    else:
+        xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))  # [B, S, H, D//2]
+        freqs_cis = reshape_for_broadcast(freqs_cis, xq_, head_first).to(xq.device)   # [S, D//2] --> [1, S, 1, D//2]
+        xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3).type_as(xq)
+        if xk is not None:
+            xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))  # [B, S, H, D//2]
+            xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3).type_as(xk)
+
+    return xq_out, xk_out
+
+
+
+class CrossAttention(nn.Module):
+    """
+    Use QK Normalization.
+    """
+    def __init__(self,
+                 qdim,
+                 kdim,
+                 num_heads,
+                 qkv_bias=True,
+                 qk_norm=False,
+                 attn_drop=0.0,
+                 proj_drop=0.0,
+                 attn_precision=None,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__()
+        self.attn_precision = attn_precision
+        self.qdim = qdim
+        self.kdim = kdim
+        self.num_heads = num_heads
+        assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads"
+        self.head_dim = self.qdim // num_heads
+        assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
+        self.scale = self.head_dim ** -0.5
+
+        self.q_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
+        self.kv_proj = operations.Linear(kdim, 2 * qdim, bias=qkv_bias, **factory_kwargs)
+
+        # TODO: eps should be 1 / 65530 if using fp16
+        self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+        self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.out_proj = operations.Linear(qdim, qdim, bias=qkv_bias, **factory_kwargs)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, y, freqs_cis_img=None):
+        """
+        Parameters
+        ----------
+        x: torch.Tensor
+            (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim)
+        y: torch.Tensor
+            (batch, seqlen2, hidden_dim2)
+        freqs_cis_img: torch.Tensor
+            (batch, hidden_dim // 2), RoPE for image
+        """
+        b, s1, c = x.shape     # [b, s1, D]
+        _, s2, c = y.shape     # [b, s2, 1024]
+
+        q = self.q_proj(x).view(b, s1, self.num_heads, self.head_dim)   # [b, s1, h, d]
+        kv = self.kv_proj(y).view(b, s2, 2, self.num_heads, self.head_dim)    # [b, s2, 2, h, d]
+        k, v = kv.unbind(dim=2) # [b, s, h, d]
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        # Apply RoPE if needed
+        if freqs_cis_img is not None:
+            qq, _ = apply_rotary_emb(q, None, freqs_cis_img)
+            assert qq.shape == q.shape, f'qq: {qq.shape}, q: {q.shape}'
+            q = qq
+
+        q = q.transpose(-2, -3).contiguous()        # q ->  B, L1, H, C - B, H, L1, C
+        k = k.transpose(-2, -3).contiguous()      # k ->  B, L2, H, C - B, H, C, L2
+        v = v.transpose(-2, -3).contiguous() 
+
+        context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)
+
+        out = self.out_proj(context)  # context.reshape - B, L1, -1
+        out = self.proj_drop(out)
+
+        out_tuple = (out,)
+
+        return out_tuple
+
+
+class Attention(nn.Module):
+    """
+    We rename some layer names to align with flash attention
+    """
+    def __init__(self, dim, num_heads, qkv_bias=True, qk_norm=False, attn_drop=0., proj_drop=0., attn_precision=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.attn_precision = attn_precision
+        self.dim = dim
+        self.num_heads = num_heads
+        assert self.dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.head_dim = self.dim // num_heads
+        # This assertion is aligned with flash attention
+        assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8"
+        self.scale = self.head_dim ** -0.5
+
+        # qkv --> Wqkv
+        self.Wqkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        # TODO: eps should be 1 / 65530 if using fp16
+        self.q_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+        self.k_norm = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device) if qk_norm else nn.Identity()
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.out_proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, freqs_cis_img=None):
+        B, N, C = x.shape
+        qkv = self.Wqkv(x).reshape(B, N, 3, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)   # [3, b, h, s, d]
+        q, k, v = qkv.unbind(0)     # [b, h, s, d]
+        q = self.q_norm(q)          # [b, h, s, d]
+        k = self.k_norm(k)          # [b, h, s, d]
+
+        # Apply RoPE if needed
+        if freqs_cis_img is not None:
+            qq, kk = apply_rotary_emb(q, k, freqs_cis_img, head_first=True)
+            assert qq.shape == q.shape and kk.shape == k.shape, \
+                f'qq: {qq.shape}, q: {q.shape}, kk: {kk.shape}, k: {k.shape}'
+            q, k = qq, kk
+
+        x = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)
+        x = self.out_proj(x)
+        x = self.proj_drop(x)
+
+        out_tuple = (x,)
+
+        return out_tuple
diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py
new file mode 100644
index 000000000..4bd7abab6
--- /dev/null
+++ b/comfy/ldm/hydit/models.py
@@ -0,0 +1,402 @@
+from typing import Any
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm
+from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
+from torch.utils import checkpoint
+
+from .attn_layers import Attention, CrossAttention
+from .poolers import AttentionPool
+from .posemb_layers import get_2d_rotary_pos_embed, get_fill_resize_and_crop
+
+def calc_rope(x, patch_size, head_size):
+    th = (x.shape[2] + (patch_size // 2)) // patch_size
+    tw = (x.shape[3] + (patch_size // 2)) // patch_size
+    base_size = 512 // 8 // patch_size
+    start, stop = get_fill_resize_and_crop((th, tw), base_size)
+    sub_args = [start, stop, (th, tw)]
+    # head_size = HUNYUAN_DIT_CONFIG['DiT-g/2']['hidden_size'] // HUNYUAN_DIT_CONFIG['DiT-g/2']['num_heads']
+    rope = get_2d_rotary_pos_embed(head_size, *sub_args)
+    return rope
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+class HunYuanDiTBlock(nn.Module):
+    """
+    A HunYuanDiT block with `add` conditioning.
+    """
+    def __init__(self,
+                 hidden_size,
+                 c_emb_size,
+                 num_heads,
+                 mlp_ratio=4.0,
+                 text_states_dim=1024,
+                 qk_norm=False,
+                 norm_type="layer",
+                 skip=False,
+                 attn_precision=None,
+                 dtype=None,
+                 device=None,
+                 operations=None,
+                 ):
+        super().__init__()
+        use_ele_affine = True
+
+        if norm_type == "layer":
+            norm_layer = operations.LayerNorm
+        elif norm_type == "rms":
+            norm_layer = RMSNorm
+        else:
+            raise ValueError(f"Unknown norm_type: {norm_type}")
+
+        # ========================= Self-Attention =========================
+        self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)
+        self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=True, qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
+
+        # ========================= FFN =========================
+        self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6, dtype=dtype, device=device)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(in_features=hidden_size, hidden_features=mlp_hidden_dim, act_layer=approx_gelu, drop=0, dtype=dtype, device=device, operations=operations)
+
+        # ========================= Add =========================
+        # Simply use add like SDXL.
+        self.default_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(c_emb_size, hidden_size, bias=True, dtype=dtype, device=device)
+        )
+
+        # ========================= Cross-Attention =========================
+        self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=True,
+                                        qk_norm=qk_norm, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
+        self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
+
+        # ========================= Skip Connection =========================
+        if skip:
+            self.skip_norm = norm_layer(2 * hidden_size, elementwise_affine=True, eps=1e-6, dtype=dtype, device=device)
+            self.skip_linear = operations.Linear(2 * hidden_size, hidden_size, dtype=dtype, device=device)
+        else:
+            self.skip_linear = None
+
+        self.gradient_checkpointing = False
+
+    def _forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
+        # Long Skip Connection
+        if self.skip_linear is not None:
+            cat = torch.cat([x, skip], dim=-1)
+            cat = self.skip_norm(cat)
+            x = self.skip_linear(cat)
+
+        # Self-Attention
+        shift_msa = self.default_modulation(c).unsqueeze(dim=1)
+        attn_inputs = (
+            self.norm1(x) + shift_msa, freq_cis_img,
+        )
+        x = x + self.attn1(*attn_inputs)[0]
+
+        # Cross-Attention
+        cross_inputs = (
+            self.norm3(x), text_states, freq_cis_img
+        )
+        x = x + self.attn2(*cross_inputs)[0]
+
+        # FFN Layer
+        mlp_inputs = self.norm2(x)
+        x = x + self.mlp(mlp_inputs)
+
+        return x
+
+    def forward(self, x, c=None, text_states=None, freq_cis_img=None, skip=None):
+        if self.gradient_checkpointing and self.training:
+            return checkpoint.checkpoint(self._forward, x, c, text_states, freq_cis_img, skip)
+        return self._forward(x, c, text_states, freq_cis_img, skip)
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of HunYuanDiT.
+    """
+    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)
+        )
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class HunYuanDiT(nn.Module):
+    """
+    HunYuanDiT: Diffusion model with a Transformer backbone.
+
+    Inherit ModelMixin and ConfigMixin to be compatible with the sampler StableDiffusionPipeline of diffusers.
+
+    Inherit PeftAdapterMixin to be compatible with the PEFT training pipeline.
+
+    Parameters
+    ----------
+    args: argparse.Namespace
+        The arguments parsed by argparse.
+    input_size: tuple
+        The size of the input image.
+    patch_size: int
+        The size of the patch.
+    in_channels: int
+        The number of input channels.
+    hidden_size: int
+        The hidden size of the transformer backbone.
+    depth: int
+        The number of transformer blocks.
+    num_heads: int
+        The number of attention heads.
+    mlp_ratio: float
+        The ratio of the hidden size of the MLP in the transformer block.
+    log_fn: callable
+        The logging function.
+    """
+    #@register_to_config
+    def __init__(self,
+                 input_size: tuple = 32,
+                 patch_size: int = 2,
+                 in_channels: int = 4,
+                 hidden_size: int = 1152,
+                 depth: int = 28,
+                 num_heads: int = 16,
+                 mlp_ratio: float = 4.0,
+                 text_states_dim = 1024,
+                 text_states_dim_t5 = 2048,
+                 text_len = 77,
+                 text_len_t5 = 256,
+                 qk_norm = True,# See http://arxiv.org/abs/2302.05442 for details.
+                 size_cond = False,
+                 use_style_cond = False,
+                 learn_sigma = True,
+                 norm = "layer",
+                 log_fn: callable = print,
+                 attn_precision=None,
+                 dtype=None,
+                 device=None,
+                 operations=None,
+                 **kwargs,
+    ):
+        super().__init__()
+        self.log_fn = log_fn
+        self.depth = depth
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.text_states_dim = text_states_dim
+        self.text_states_dim_t5 = text_states_dim_t5
+        self.text_len = text_len
+        self.text_len_t5 = text_len_t5
+        self.size_cond = size_cond
+        self.use_style_cond = use_style_cond
+        self.norm = norm
+        self.dtype = dtype
+        #import pdb
+        #pdb.set_trace()
+
+        self.mlp_t5 = nn.Sequential(
+            operations.Linear(self.text_states_dim_t5, self.text_states_dim_t5 * 4, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(self.text_states_dim_t5 * 4, self.text_states_dim, bias=True, dtype=dtype, device=device),
+        )
+        # learnable replace
+        self.text_embedding_padding = nn.Parameter(
+            torch.empty(self.text_len + self.text_len_t5, self.text_states_dim, dtype=dtype, device=device))
+
+        # Attention pooling
+        pooler_out_dim = 1024
+        self.pooler = AttentionPool(self.text_len_t5, self.text_states_dim_t5, num_heads=8, output_dim=pooler_out_dim, dtype=dtype, device=device, operations=operations)
+
+        # Dimension of the extra input vectors
+        self.extra_in_dim = pooler_out_dim
+
+        if self.size_cond:
+            # Image size and crop size conditions
+            self.extra_in_dim += 6 * 256
+
+        if self.use_style_cond:
+            # Here we use a default learned embedder layer for future extension.
+            self.style_embedder = nn.Embedding(1, hidden_size, dtype=dtype, device=device)
+            self.extra_in_dim += hidden_size
+
+        # Text embedding for `add`
+        self.x_embedder = PatchEmbed(input_size, patch_size, in_channels, hidden_size, dtype=dtype, device=device, operations=operations)
+        self.t_embedder = TimestepEmbedder(hidden_size, dtype=dtype, device=device, operations=operations)
+        self.extra_embedder = nn.Sequential(
+            operations.Linear(self.extra_in_dim, hidden_size * 4, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size * 4, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+        # Image embedding
+        num_patches = self.x_embedder.num_patches
+
+        # HUnYuanDiT Blocks
+        self.blocks = nn.ModuleList([
+            HunYuanDiTBlock(hidden_size=hidden_size,
+                            c_emb_size=hidden_size,
+                            num_heads=num_heads,
+                            mlp_ratio=mlp_ratio,
+                            text_states_dim=self.text_states_dim,
+                            qk_norm=qk_norm,
+                            norm_type=self.norm,
+                            skip=layer > depth // 2,
+                            attn_precision=attn_precision,
+                            dtype=dtype,
+                            device=device,
+                            operations=operations,
+                            )
+            for layer in range(depth)
+        ])
+
+        self.final_layer = FinalLayer(hidden_size, hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+        self.unpatchify_channels = self.out_channels
+
+
+
+    def forward(self,
+                x,
+                t,
+                context,#encoder_hidden_states=None,
+                text_embedding_mask=None,
+                encoder_hidden_states_t5=None,
+                text_embedding_mask_t5=None,
+                image_meta_size=None,
+                style=None,
+                return_dict=False,
+                control=None,
+                transformer_options=None,
+                ):
+        """
+        Forward pass of the encoder.
+
+        Parameters
+        ----------
+        x: torch.Tensor
+            (B, D, H, W)
+        t: torch.Tensor
+            (B)
+        encoder_hidden_states: torch.Tensor
+            CLIP text embedding, (B, L_clip, D)
+        text_embedding_mask: torch.Tensor
+            CLIP text embedding mask, (B, L_clip)
+        encoder_hidden_states_t5: torch.Tensor
+            T5 text embedding, (B, L_t5, D)
+        text_embedding_mask_t5: torch.Tensor
+            T5 text embedding mask, (B, L_t5)
+        image_meta_size: torch.Tensor
+            (B, 6)
+        style: torch.Tensor
+            (B)
+        cos_cis_img: torch.Tensor
+        sin_cis_img: torch.Tensor
+        return_dict: bool
+            Whether to return a dictionary.
+        """
+        #import pdb
+        #pdb.set_trace()
+        encoder_hidden_states = context
+        text_states = encoder_hidden_states                     # 2,77,1024
+        text_states_t5 = encoder_hidden_states_t5               # 2,256,2048
+        text_states_mask = text_embedding_mask.bool()           # 2,77
+        text_states_t5_mask = text_embedding_mask_t5.bool()     # 2,256
+        b_t5, l_t5, c_t5 = text_states_t5.shape
+        text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5))
+        text_states = torch.cat([text_states, text_states_t5.view(b_t5, l_t5, -1)], dim=1)  # 2,205，1024
+
+        clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1)
+
+        clip_t5_mask = clip_t5_mask
+        text_states = torch.where(clip_t5_mask.unsqueeze(2), text_states, self.text_embedding_padding.to(text_states))
+
+        _, _, oh, ow = x.shape
+        th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size
+
+
+        # Get image RoPE embedding according to `reso`lution.
+        freqs_cis_img = calc_rope(x, self.patch_size, self.hidden_size // self.num_heads) #(cos_cis_img, sin_cis_img)
+
+        # ========================= Build time and image embedding =========================
+        t = self.t_embedder(t, dtype=x.dtype)
+        x = self.x_embedder(x)
+
+        # ========================= Concatenate all extra vectors =========================
+        # Build text tokens with pooling
+        extra_vec = self.pooler(encoder_hidden_states_t5)
+
+        # Build image meta size tokens if applicable
+        if self.size_cond:
+            image_meta_size = timestep_embedding(image_meta_size.view(-1), 256).to(x.dtype)   # [B * 6, 256]
+            image_meta_size = image_meta_size.view(-1, 6 * 256)
+            extra_vec = torch.cat([extra_vec, image_meta_size], dim=1)  # [B, D + 6 * 256]
+
+        # Build style tokens
+        if self.use_style_cond:
+            if style is None:
+                style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int)
+            style_embedding = self.style_embedder(style)
+            extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
+
+        # Concatenate all extra vectors
+        c = t + self.extra_embedder(extra_vec)  # [B, D]
+
+        controls = None
+        # ========================= Forward pass through HunYuanDiT blocks =========================
+        skips = []
+        for layer, block in enumerate(self.blocks):
+            if layer > self.depth // 2:
+                if controls is not None:
+                    skip = skips.pop() + controls.pop()
+                else:
+                    skip = skips.pop()
+                x = block(x, c, text_states, freqs_cis_img, skip)   # (N, L, D)
+            else:
+                x = block(x, c, text_states, freqs_cis_img)         # (N, L, D)
+
+            if layer < (self.depth // 2 - 1):
+                skips.append(x)
+        if controls is not None and len(controls) != 0:
+            raise ValueError("The number of controls is not equal to the number of skip connections.")
+
+        # ========================= Final layer =========================
+        x = self.final_layer(x, c)                              # (N, L, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x, th, tw)                          # (N, out_channels, H, W)
+
+        if return_dict:
+            return {'x': x}
+        if self.learn_sigma:
+            return x[:,:self.out_channels // 2,:oh,:ow]
+        return x[:,:,:oh,:ow]
+
+    def unpatchify(self, x, h, w):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.unpatchify_channels
+        p = self.x_embedder.patch_size[0]
+        # h = w = int(x.shape[1] ** 0.5)
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs
diff --git a/comfy/ldm/hydit/poolers.py b/comfy/ldm/hydit/poolers.py
new file mode 100644
index 000000000..3470041b5
--- /dev/null
+++ b/comfy/ldm/hydit/poolers.py
@@ -0,0 +1,36 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.modules.attention import optimized_attention #TODO
+
+
+class AttentionPool(nn.Module):
+    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.positional_embedding = nn.Parameter(torch.empty(spacial_dim + 1, embed_dim, dtype=dtype, device=device))
+        self.k_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+        self.q_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+        self.v_proj = operations.Linear(embed_dim, embed_dim, dtype=dtype, device=device)
+        self.c_proj = operations.Linear(embed_dim, output_dim or embed_dim, dtype=dtype, device=device)
+        self.num_heads = num_heads
+        self.embed_dim = embed_dim
+
+    def forward(self, x):
+        x = x.permute(1, 0, 2)  # NLC -> LNC
+        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
+        x = x + self.positional_embedding[:, None, :].to(dtype=x.dtype, device=x.device)  # (L+1)NC
+
+        q = self.q_proj(x[:1])
+        k = self.k_proj(x)
+        v = self.v_proj(x)
+
+        batch_size = q.shape[1]
+        head_dim = self.embed_dim // self.num_heads
+        q = q.view(1, batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+        k = k.view(k.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+        v = v.view(v.shape[0], batch_size * self.num_heads, head_dim).transpose(0, 1).view(batch_size, self.num_heads, -1, head_dim)
+
+        attn_output = optimized_attention(q, k, v, self.num_heads, skip_reshape=True).transpose(0, 1)
+
+        attn_output = self.c_proj(attn_output)
+        return attn_output.squeeze(0)
diff --git a/comfy/ldm/hydit/posemb_layers.py b/comfy/ldm/hydit/posemb_layers.py
new file mode 100644
index 000000000..dcb41a713
--- /dev/null
+++ b/comfy/ldm/hydit/posemb_layers.py
@@ -0,0 +1,224 @@
+import torch
+import numpy as np
+from typing import Union
+
+
+def _to_tuple(x):
+    if isinstance(x, int):
+        return x, x
+    else:
+        return x
+
+
+def get_fill_resize_and_crop(src, tgt):
+    th, tw = _to_tuple(tgt)
+    h, w = _to_tuple(src)
+
+    tr = th / tw        # base resolution
+    r = h / w           # target resolution
+
+    # resize
+    if r > tr:
+        resize_height = th
+        resize_width = int(round(th / h * w))
+    else:
+        resize_width = tw
+        resize_height = int(round(tw / w * h))    # resize the target resolution down based on the base resolution
+
+    crop_top = int(round((th - resize_height) / 2.0))
+    crop_left = int(round((tw - resize_width) / 2.0))
+
+    return (crop_top, crop_left), (crop_top + resize_height, crop_left + resize_width)
+
+
+def get_meshgrid(start, *args):
+    if len(args) == 0:
+        # start is grid_size
+        num = _to_tuple(start)
+        start = (0, 0)
+        stop = num
+    elif len(args) == 1:
+        # start is start, args[0] is stop, step is 1
+        start = _to_tuple(start)
+        stop = _to_tuple(args[0])
+        num = (stop[0] - start[0], stop[1] - start[1])
+    elif len(args) == 2:
+        # start is start, args[0] is stop, args[1] is num
+        start = _to_tuple(start)
+        stop = _to_tuple(args[0])
+        num = _to_tuple(args[1])
+    else:
+        raise ValueError(f"len(args) should be 0, 1 or 2, but got {len(args)}")
+
+    grid_h = np.linspace(start[0], stop[0], num[0], endpoint=False, dtype=np.float32)
+    grid_w = np.linspace(start[1], stop[1], num[1], endpoint=False, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)   # [2, W, H]
+    return grid
+
+#################################################################################
+#                   Sine/Cosine Positional Embedding Functions                  #
+#################################################################################
+# https://github.com/facebookresearch/mae/blob/main/util/pos_embed.py
+
+def get_2d_sincos_pos_embed(embed_dim, start, *args, cls_token=False, extra_tokens=0):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid = get_meshgrid(start, *args)   # [2, H, w]
+    # grid_h = np.arange(grid_size, dtype=np.float32)
+    # grid_w = np.arange(grid_size, dtype=np.float32)
+    # grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    # grid = np.stack(grid, axis=0)   # [2, W, H]
+
+    grid = grid.reshape([2, 1, *grid.shape[1:]])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate([np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0)
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)    # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (W,H)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.
+    omega = 1. / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum('m,d->md', pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)   # (M, D/2)
+    emb_cos = np.cos(out)   # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+
+#################################################################################
+#                   Rotary Positional Embedding Functions                       #
+#################################################################################
+# https://github.com/facebookresearch/llama/blob/main/llama/model.py#L443
+
+def get_2d_rotary_pos_embed(embed_dim, start, *args, use_real=True):
+    """
+    This is a 2d version of precompute_freqs_cis, which is a RoPE for image tokens with 2d structure.
+
+    Parameters
+    ----------
+    embed_dim: int
+        embedding dimension size
+    start: int or tuple of int
+        If len(args) == 0, start is num; If len(args) == 1, start is start, args[0] is stop, step is 1;
+        If len(args) == 2, start is start, args[0] is stop, args[1] is num.
+    use_real: bool
+        If True, return real part and imaginary part separately. Otherwise, return complex numbers.
+
+    Returns
+    -------
+    pos_embed: torch.Tensor
+        [HW, D/2]
+    """
+    grid = get_meshgrid(start, *args)   # [2, H, w]
+    grid = grid.reshape([2, 1, *grid.shape[1:]])   # Returns a sampling matrix with the same resolution as the target resolution
+    pos_embed = get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=use_real)
+    return pos_embed
+
+
+def get_2d_rotary_pos_embed_from_grid(embed_dim, grid, use_real=False):
+    assert embed_dim % 4 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_rotary_pos_embed(embed_dim // 2, grid[0].reshape(-1), use_real=use_real)  # (H*W, D/4)
+    emb_w = get_1d_rotary_pos_embed(embed_dim // 2, grid[1].reshape(-1), use_real=use_real)  # (H*W, D/4)
+
+    if use_real:
+        cos = torch.cat([emb_h[0], emb_w[0]], dim=1)    # (H*W, D/2)
+        sin = torch.cat([emb_h[1], emb_w[1]], dim=1)    # (H*W, D/2)
+        return cos, sin
+    else:
+        emb = torch.cat([emb_h, emb_w], dim=1)    # (H*W, D/2)
+        return emb
+
+
+def get_1d_rotary_pos_embed(dim: int, pos: Union[np.ndarray, int], theta: float = 10000.0, use_real=False):
+    """
+    Precompute the frequency tensor for complex exponentials (cis) with given dimensions.
+
+    This function calculates a frequency tensor with complex exponentials using the given dimension 'dim'
+    and the end index 'end'. The 'theta' parameter scales the frequencies.
+    The returned tensor contains complex values in complex64 data type.
+
+    Args:
+        dim (int): Dimension of the frequency tensor.
+        pos (np.ndarray, int): Position indices for the frequency tensor. [S] or scalar
+        theta (float, optional): Scaling factor for frequency computation. Defaults to 10000.0.
+        use_real (bool, optional): If True, return real part and imaginary part separately.
+                                   Otherwise, return complex numbers.
+
+    Returns:
+        torch.Tensor: Precomputed frequency tensor with complex exponentials. [S, D/2]
+
+    """
+    if isinstance(pos, int):
+        pos = np.arange(pos)
+    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))  # [D/2]
+    t = torch.from_numpy(pos).to(freqs.device)  # type: ignore  # [S]
+    freqs = torch.outer(t, freqs).float()  # type: ignore   # [S, D/2]
+    if use_real:
+        freqs_cos = freqs.cos().repeat_interleave(2, dim=1)  # [S, D]
+        freqs_sin = freqs.sin().repeat_interleave(2, dim=1)  # [S, D]
+        return freqs_cos, freqs_sin
+    else:
+        freqs_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64     # [S, D/2]
+        return freqs_cis
+
+
+
+def calc_sizes(rope_img, patch_size, th, tw):
+    if rope_img == 'extend':
+        # Expansion mode
+        sub_args = [(th, tw)]
+    elif rope_img.startswith('base'):
+        # Based on the specified dimensions, other dimensions are obtained through interpolation.
+        base_size = int(rope_img[4:]) // 8 // patch_size
+        start, stop = get_fill_resize_and_crop((th, tw), base_size)
+        sub_args = [start, stop, (th, tw)]
+    else:
+        raise ValueError(f"Unknown rope_img: {rope_img}")
+    return sub_args
+
+
+def init_image_posemb(rope_img,
+                      resolutions,
+                      patch_size,
+                      hidden_size,
+                      num_heads,
+                      log_fn,
+                      rope_real=True,
+                      ):
+    freqs_cis_img = {}
+    for reso in resolutions:
+        th, tw = reso.height // 8 // patch_size, reso.width // 8 // patch_size
+        sub_args = calc_sizes(rope_img, patch_size, th, tw)
+        freqs_cis_img[str(reso)] = get_2d_rotary_pos_embed(hidden_size // num_heads, *sub_args, use_real=rope_real)
+        log_fn(f"    Using image RoPE ({rope_img}) ({'real' if rope_real else 'complex'}): {sub_args} | ({reso}) "
+               f"{freqs_cis_img[str(reso)][0].shape if rope_real else freqs_cis_img[str(reso)].shape}")
+    return freqs_cis_img
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 0e0e69d3b..9d60c1fb4 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -7,6 +7,7 @@ from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugme
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
 import comfy.ldm.aura.mmdit
+import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
 import comfy.model_management
@@ -648,3 +649,35 @@ class StableAudio1(BaseModel):
             for l in s:
                 sd["{}{}".format(k, l)] = s[l]
         return sd
+
+class HunyuanDiT(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.V_PREDICTION, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.hydit.models.HunYuanDiT)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['text_embedding_mask'] = comfy.conds.CONDRegular(attention_mask)
+
+        conditioning_mt5xl = kwargs.get("conditioning_mt5xl", None)
+        if conditioning_mt5xl is not None:
+            out['encoder_hidden_states_t5'] = comfy.conds.CONDRegular(conditioning_mt5xl)
+
+        attention_mask_mt5xl = kwargs.get("attention_mask_mt5xl", None)
+        if attention_mask_mt5xl is not None:
+            out['text_embedding_mask_t5'] = comfy.conds.CONDRegular(attention_mask_mt5xl)
+
+        width = kwargs.get("width", 768)
+        height = kwargs.get("height", 768)
+        crop_w = kwargs.get("crop_w", 0)
+        crop_h = kwargs.get("crop_h", 0)
+        target_width = kwargs.get("target_width", width)
+        target_height = kwargs.get("target_height", height)
+
+        out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
+        return out
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index ae88eeb97..ea4959370 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -115,6 +115,19 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config["n_layers"] = double_layers + single_layers
         return unet_config
 
+    if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT
+        unet_config = {}
+        unet_config["image_model"] = "hydit"
+        unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+        unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0]
+        if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2
+            unet_config["mlp_ratio"] = 4.3637
+        if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968:
+            unet_config["size_cond"] = True
+            unet_config["use_style_cond"] = True
+            unet_config["image_model"] = "hydit1"
+        return unet_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
diff --git a/comfy/sd.py b/comfy/sd.py
index c6fcd810c..b1d38c4b2 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -22,6 +22,7 @@ from . import sdxl_clip
 import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
+import comfy.text_encoders.hydit
 
 import comfy.model_patcher
 import comfy.lora
@@ -385,6 +386,7 @@ class CLIPType(Enum):
     STABLE_CASCADE = 2
     SD3 = 3
     STABLE_AUDIO = 4
+    HUNYUAN_DIT = 5
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
@@ -433,6 +435,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
         if clip_type == CLIPType.SD3:
             clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
             clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
+        elif clip_type == CLIPType.HUNYUAN_DIT:
+            clip_target.clip = comfy.text_encoders.hydit.HyditModel
+            clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index b4d1059ef..40417eb4d 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -8,6 +8,7 @@ from . import sdxl_clip
 import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
+import comfy.text_encoders.hydit
 
 from . import supported_models_base
 from . import latent_formats
@@ -580,6 +581,45 @@ class AuraFlow(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow]
+class HunyuanDiT(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "hydit",
+    }
+
+    unet_extra_config = {
+        "attn_precision": torch.float32,
+    }
+
+    sampling_settings = {
+        "linear_start": 0.00085,
+        "linear_end": 0.018,
+    }
+
+    latent_format = latent_formats.SDXL
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.HunyuanDiT(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel)
+
+class HunyuanDiT1(HunyuanDiT):
+    unet_config = {
+        "image_model": "hydit1",
+    }
+
+    unet_extra_config = {}
+
+    sampling_settings = {
+        "linear_start" : 0.00085,
+        "linear_end" : 0.03,
+    }
+
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1]
 
 models += [SVD_img2vid]
diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py
new file mode 100644
index 000000000..e47c8cb20
--- /dev/null
+++ b/comfy/text_encoders/hydit.py
@@ -0,0 +1,120 @@
+from comfy import sd1_clip
+from transformers import T5TokenizerFast, BertTokenizer, BertModel, modeling_utils, BertConfig
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.t5
+import os
+
+import torch
+import contextlib
+
+@contextlib.contextmanager
+def use_comfy_ops(ops, device=None, dtype=None):
+    old_torch_nn_linear = torch.nn.Linear
+    force_device = device
+    force_dtype = dtype
+    def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
+        if force_device is not None:
+            device = force_device
+        if force_dtype is not None:
+            dtype = force_dtype
+        return ops.Linear(in_features, out_features, bias=bias, device=device, dtype=dtype)
+
+    torch.nn.Linear = linear_with_dtype
+    try:
+        yield
+    finally:
+        torch.nn.Linear = old_torch_nn_linear
+
+
+class RobertaWrapper(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = BertConfig(**config_dict)
+        with use_comfy_ops(operations, device, dtype):
+            with modeling_utils.no_init_weights():
+                self.bert = BertModel(config, add_pooling_layer=False)
+
+        self.num_layers = config.num_hidden_layers
+
+    def get_input_embeddings(self):
+        return self.bert.get_input_embeddings()
+
+    def set_input_embeddings(self, value):
+        return self.bert.set_input_embeddings(value)
+
+    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
+        intermediate = None
+        out = self.bert(input_ids=input_tokens, output_hidden_states=intermediate_output is not None, attention_mask=attention_mask)
+        return out.last_hidden_state, intermediate, out.pooler_output
+
+class HyditBertModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=RobertaWrapper, enable_attention_masks=True, return_attention_masks=True)
+
+class HyditBertTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer)
+
+
+class MT5XLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True)
+
+class MT5XLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_tokenizer"), "spiece.model")
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+class HyditTokenizer:
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None)
+        self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory)
+        self.mt5xl = MT5XLTokenizer(tokenizer_data={"spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        out = {}
+        out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids)
+        out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.hydit_clip.untokenize(token_weight_pair)
+
+    def state_dict(self):
+        return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]}
+
+class HyditModel(torch.nn.Module):
+    def __init__(self, device="cpu", dtype=None):
+        super().__init__()
+        self.hydit_clip = HyditBertModel()
+        self.mt5xl = MT5XLModel()
+
+        self.dtypes = set()
+        if dtype is not None:
+            self.dtypes.add(dtype)
+
+    def encode_token_weights(self, token_weight_pairs):
+        hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"])
+        mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"])
+        return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]}
+
+    def load_sd(self, sd):
+        if "bert.encoder.layer.0.attention.self.query.weight" in sd:
+            return self.hydit_clip.load_sd(sd)
+        else:
+            return self.mt5xl.load_sd(sd)
+
+    def set_clip_options(self, options):
+        self.hydit_clip.set_clip_options(options)
+        self.mt5xl.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.hydit_clip.reset_clip_options()
+        self.mt5xl.reset_clip_options()
diff --git a/comfy/text_encoders/hydit_clip.json b/comfy/text_encoders/hydit_clip.json
new file mode 100644
index 000000000..c41c7c1ff
--- /dev/null
+++ b/comfy/text_encoders/hydit_clip.json
@@ -0,0 +1,35 @@
+{
+  "_name_or_path": "hfl/chinese-roberta-wwm-ext-large",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.22.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 47020
+}
+
diff --git a/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json
new file mode 100644
index 000000000..a8b3208c2
--- /dev/null
+++ b/comfy/text_encoders/hydit_clip_tokenizer/special_tokens_map.json
@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}
diff --git a/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json
new file mode 100644
index 000000000..a14356073
--- /dev/null
+++ b/comfy/text_encoders/hydit_clip_tokenizer/tokenizer_config.json
@@ -0,0 +1,16 @@
+{
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "name_or_path": "hfl/chinese-roberta-wwm-ext",
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "special_tokens_map_file": "/home/chenweifeng/.cache/huggingface/hub/models--hfl--chinese-roberta-wwm-ext/snapshots/5c58d0b8ec1d9014354d691c538661bf00bfdb44/special_tokens_map.json",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]",
+  "model_max_length": 77
+}
diff --git a/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt
new file mode 100644
index 000000000..624690680
--- /dev/null
+++ b/comfy/text_encoders/hydit_clip_tokenizer/vocab.txt
@@ -0,0 +1,47020 @@
+[PAD]
+[unused1]
+[unused2]
+[unused3]
+[unused4]
+[unused5]
+[unused6]
+[unused7]
+[unused8]
+[unused9]
+[unused10]
+[unused11]
+[unused12]
+[unused13]
+[unused14]
+[unused15]
+[unused16]
+[unused17]
+[unused18]
+[unused19]
+[unused20]
+[unused21]
+[unused22]
+[unused23]
+[unused24]
+[unused25]
+[unused26]
+[unused27]
+[unused28]
+[unused29]
+[unused30]
+[unused31]
+[unused32]
+[unused33]
+[unused34]
+[unused35]
+[unused36]
+[unused37]
+[unused38]
+[unused39]
+[unused40]
+[unused41]
+[unused42]
+[unused43]
+[unused44]
+[unused45]
+[unused46]
+[unused47]
+[unused48]
+[unused49]
+[unused50]
+[unused51]
+[unused52]
+[unused53]
+[unused54]
+[unused55]
+[unused56]
+[unused57]
+[unused58]
+[unused59]
+[unused60]
+[unused61]
+[unused62]
+[unused63]
+[unused64]
+[unused65]
+[unused66]
+[unused67]
+[unused68]
+[unused69]
+[unused70]
+[unused71]
+[unused72]
+[unused73]
+[unused74]
+[unused75]
+[unused76]
+[unused77]
+[unused78]
+[unused79]
+[unused80]
+[unused81]
+[unused82]
+[unused83]
+[unused84]
+[unused85]
+[unused86]
+[unused87]
+[unused88]
+[unused89]
+[unused90]
+[unused91]
+[unused92]
+[unused93]
+[unused94]
+[unused95]
+[unused96]
+[unused97]
+[unused98]
+[unused99]
+[UNK]
+[CLS]
+[SEP]
+[MASK]
+<S>
+<T>
+!
+"
+#
+$
+%
+&
+'
+(
+)
+*
++
+,
+-
+.
+/
+0
+1
+2
+3
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+7
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+9
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+##rise
+irritating
+nay
+amazingly
+bancroft
+apologetic
+compassionate
+kata
+symphonies
+##lovic
+airspace
+##lch
+gifford
+precautions
+fulfillment
+sevilla
+vulgar
+martinique
+##urities
+looting
+piccolo
+tidy
+##dermott
+quadrant
+armchair
+incomes
+mathematicians
+stampede
+nilsson
+##inking
+##scan
+foo
+quarterfinal
+##ostal
+shang
+shouldered
+squirrels
+##owe
+vinegar
+##bner
+##rchy
+##systems
+delaying
+##trics
+ars
+dwyer
+rhapsody
+sponsoring
+##gration
+bipolar
+cinder
+starters
+##olio
+##urst
+signage
+##nty
+aground
+figurative
+mons
+acquaintances
+duets
+erroneously
+soyuz
+elliptic
+recreated
+##cultural
+##quette
+##ssed
+##tma
+##zcz
+moderator
+scares
+##itaire
+##stones
+##udence
+juniper
+sighting
+##just
+##nsen
+britten
+calabria
+ry
+bop
+cramer
+forsyth
+stillness
+airmen
+gathers
+unfit
+##umber
+##upt
+taunting
+seeker
+streamlined
+##bution
+holster
+schumann
+tread
+vox
+##gano
+##onzo
+strive
+dil
+reforming
+covent
+newbury
+predicting
+##orro
+decorate
+tre
+##puted
+andover
+asahi
+dept
+dunkirk
+gills
+##tori
+buren
+huskies
+##stis
+##stov
+abstracts
+bets
+loosen
+##opa
+1682
+yearning
+##glio
+##sir
+berman
+effortlessly
+enamel
+napoli
+persist
+##peration
+##uez
+attache
+elisa
+invitations
+##kic
+accelerating
+reindeer
+boardwalk
+clutches
+nelly
+polka
+##kei
+adamant
+huey
+lough
+unbroken
+adventurer
+embroidery
+inspecting
+stanza
+##ducted
+naia
+taluka
+##pone
+##roids
+chases
+deprivation
+florian
+##ppet
+earthly
+##lib
+##ssee
+colossal
+foreigner
+vet
+freaks
+patrice
+rosewood
+triassic
+upstate
+##pkins
+dominates
+ata
+chants
+ks
+vo
+##bley
+##raya
+##rmed
+agra
+infiltrate
+##ailing
+##ilation
+##tzer
+##uppe
+##werk
+binoculars
+enthusiast
+fujian
+squeak
+##avs
+abolitionist
+almeida
+boredom
+hampstead
+marsden
+rations
+##ands
+inflated
+bonuses
+rosalie
+patna
+##rco
+detachments
+penitentiary
+54th
+flourishing
+woolf
+##dion
+##etched
+papyrus
+##lster
+##nsor
+##toy
+bobbed
+dismounted
+endelle
+inhuman
+motorola
+wince
+wreath
+##ticus
+hideout
+inspections
+sanjay
+disgrace
+infused
+pudding
+stalks
+##urbed
+arsenic
+leases
+##hyl
+##rrard
+collarbone
+##waite
+##wil
+dowry
+##bant
+##edance
+genealogical
+nitrate
+salamanca
+scandals
+thyroid
+necessitated
+##`
+##¡
+##¢
+##¦
+##¨
+##ª
+##¬
+##´
+##¶
+##¾
+##¿
+##ð
+##þ
+##ħ
+##œ
+##ƒ
+##ɐ
+##ɑ
+##ɒ
+##ɕ
+##ɣ
+##ɨ
+##ɪ
+##ɫ
+##ɬ
+##ɯ
+##ɲ
+##ɴ
+##ɹ
+##ɾ
+##ʀ
+##ʁ
+##ʂ
+##ʃ
+##ʉ
+##ʊ
+##ʋ
+##ʌ
+##ʎ
+##ʐ
+##ʑ
+##ʒ
+##ʔ
+##ʲ
+##ʳ
+##ʷ
+##ʸ
+##ʻ
+##ʼ
+##ʾ
+##ʿ
+##ˡ
+##ˣ
+##ˤ
+##ζ
+##ξ
+##щ
+##ъ
+##э
+##ю
+##ђ
+##є
+##ј
+##љ
+##њ
+##ћ
+##ӏ
+##ա
+##բ
+##գ
+##դ
+##ե
+##թ
+##ի
+##լ
+##կ
+##հ
+##մ
+##յ
+##ն
+##ո
+##պ
+##ս
+##վ
+##տ
+##ր
+##ւ
+##ք
+##־
+##א
+##ב
+##ג
+##ד
+##ו
+##ז
+##ח
+##ט
+##י
+##ך
+##כ
+##ל
+##ם
+##מ
+##ן
+##נ
+##ס
+##ע
+##ף
+##פ
+##ץ
+##צ
+##ק
+##ר
+##ש
+##ת
+##،
+##ء
+##ث
+##ج
+##ح
+##خ
+##ذ
+##ز
+##ش
+##ص
+##ض
+##ط
+##ظ
+##غ
+##ـ
+##ف
+##ق
+##ك
+##ى
+##ٹ
+##پ
+##چ
+##ک
+##گ
+##ں
+##ھ
+##ہ
+##ے
+##अ
+##आ
+##उ
+##ए
+##क
+##ख
+##ग
+##च
+##ज
+##ट
+##ड
+##ण
+##त
+##थ
+##द
+##ध
+##न
+##प
+##ब
+##भ
+##म
+##य
+##र
+##ल
+##व
+##श
+##ष
+##स
+##ह
+##ा
+##ि
+##ी
+##ो
+##।
+##॥
+##ং
+##অ
+##আ
+##ই
+##উ
+##এ
+##ও
+##ক
+##খ
+##গ
+##চ
+##ছ
+##জ
+##ট
+##ড
+##ণ
+##ত
+##থ
+##দ
+##ধ
+##ন
+##প
+##ব
+##ভ
+##ম
+##য
+##র
+##ল
+##শ
+##ষ
+##স
+##হ
+##া
+##ি
+##ী
+##ে
+##க
+##ச
+##ட
+##த
+##ந
+##ன
+##ப
+##ம
+##ய
+##ர
+##ல
+##ள
+##வ
+##ா
+##ி
+##ு
+##ே
+##ை
+##ನ
+##ರ
+##ಾ
+##ක
+##ය
+##ර
+##ල
+##ව
+##ා
+##ต
+##ท
+##พ
+##ล
+##ว
+##ส
+##།
+##ག
+##ང
+##ད
+##ན
+##པ
+##བ
+##མ
+##འ
+##ར
+##ལ
+##ས
+##မ
+##ა
+##ბ
+##გ
+##დ
+##ე
+##ვ
+##თ
+##ი
+##კ
+##ლ
+##მ
+##ნ
+##ო
+##რ
+##ს
+##ტ
+##უ
+##ᄊ
+##ᴬ
+##ᴮ
+##ᴰ
+##ᴵ
+##ᴺ
+##ᵀ
+##ᵇ
+##ᵈ
+##ᵖ
+##ᵗ
+##ᵣ
+##ᵤ
+##ᵥ
+##ᶜ
+##ᶠ
+##‐
+##‑
+##‒
+##–
+##—
+##―
+##‘
+##’
+##‚
+##“
+##”
+##‡
+##…
+##⁰
+##⁴
+##⁵
+##⁶
+##⁷
+##⁸
+##⁹
+##⁻
+##₅
+##₆
+##₇
+##₈
+##₉
+##₊
+##₍
+##₎
+##ₐ
+##ₑ
+##ₒ
+##ₓ
+##ₕ
+##ₖ
+##ₗ
+##ₘ
+##ₚ
+##ₛ
+##ₜ
+##₤
+##₩
+##₱
+##₹
+##ℓ
+##ℝ
+##⅓
+##⅔
+##↦
+##⇄
+##⇌
+##∂
+##∅
+##∆
+##∇
+##∈
+##∗
+##∘
+##∧
+##∨
+##∪
+##⊂
+##⊆
+##⊕
+##⊗
+##☉
+##♯
+##⟨
+##⟩
+##ⱼ
+##⺩
+##⺼
+##⽥
+##亻
+##宀
+##彳
+##忄
+##扌
+##氵
+##疒
+##糹
+##訁
+##辶
+##阝
+##龸
+##fi
+##fl
diff --git a/comfy/text_encoders/mt5_config_xl.json b/comfy/text_encoders/mt5_config_xl.json
new file mode 100644
index 000000000..092fefd6e
--- /dev/null
+++ b/comfy/text_encoders/mt5_config_xl.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "mt5",
+  "num_decoder_layers": 24,
+  "num_heads": 32,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 250112
+}
diff --git a/comfy/text_encoders/spiece_tokenizer.py b/comfy/text_encoders/spiece_tokenizer.py
index deb026ffd..73739553d 100644
--- a/comfy/text_encoders/spiece_tokenizer.py
+++ b/comfy/text_encoders/spiece_tokenizer.py
@@ -27,3 +27,6 @@ class SPieceTokenizer:
     def __call__(self, string):
         out = self.tokenizer.encode(string)
         return {"input_ids": out}
+
+    def serialize_model(self):
+        return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))

From 61a2b00bc2f06dec3b570dfde2eb43890613d054 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 25 Jul 2024 19:06:43 -0400
Subject: [PATCH 233/315] Add HunyuanDiT support to readme.

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 5f747321f..c2100cab2 100644
--- a/README.md
+++ b/README.md
@@ -33,6 +33,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
 - [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
 - [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
+- [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
 - Starts up very fast.
 - Works fully offline: will never download anything.

From 25b51b1a8b6be9c3dadd1d755c78394009c4d1d4 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 25 Jul 2024 22:42:54 -0400
Subject: [PATCH 234/315] Hunyuan DiT lora support.

---
 comfy/lora.py | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/comfy/lora.py b/comfy/lora.py
index e36b354f0..fdc128c09 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -282,4 +282,10 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format
                 key_map[key_lora] = to
 
+    if isinstance(model, comfy.model_base.HunyuanDiT):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"):
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format
+
     return key_map

From a9ac56fc0db5777de0edf2fe4b8ed628ccab1293 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 04:32:33 -0400
Subject: [PATCH 235/315] Own BertModel implementation that works with lowvram.

---
 comfy/text_encoders/bert.py  | 139 +++++++++++++++++++++++++++++++++++
 comfy/text_encoders/hydit.py |  47 +-----------
 2 files changed, 142 insertions(+), 44 deletions(-)
 create mode 100644 comfy/text_encoders/bert.py

diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py
new file mode 100644
index 000000000..b76e76660
--- /dev/null
+++ b/comfy/text_encoders/bert.py
@@ -0,0 +1,139 @@
+import torch
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+class BertAttention(torch.nn.Module):
+    def __init__(self, embed_dim, heads, dtype, device, operations):
+        super().__init__()
+
+        self.heads = heads
+        self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+        self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+        self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+
+
+    def forward(self, x, mask=None, optimized_attention=None):
+        q = self.query(x)
+        k = self.key(x)
+        v = self.value(x)
+
+        out = optimized_attention(q, k, v, self.heads, mask)
+        return out
+
+class BertOutput(torch.nn.Module):
+    def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device)
+        self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device)
+        # self.dropout = nn.Dropout(0.0)
+
+    def forward(self, x, y):
+        x = self.dense(x)
+        # hidden_states = self.dropout(hidden_states)
+        x = self.LayerNorm(x + y)
+        return x
+
+class BertAttentionBlock(torch.nn.Module):
+    def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.self = BertAttention(embed_dim, heads, dtype, device, operations)
+        self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+    def forward(self, x, mask, optimized_attention):
+        y = self.self(x, mask, optimized_attention)
+        return self.output(y, x)
+
+class BertIntermediate(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_dim, dtype, device, operations):
+        super().__init__()
+        self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device)
+
+    def forward(self, x):
+        x = self.dense(x)
+        return torch.nn.functional.gelu(x)
+
+
+class BertBlock(torch.nn.Module):
+    def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations)
+        self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations)
+        self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+    def forward(self, x, mask, optimized_attention):
+        x = self.attention(x, mask, optimized_attention)
+        y = self.intermediate(x)
+        return self.output(y, x)
+
+class BertEncoder(torch.nn.Module):
+    def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)])
+
+    def forward(self, x, mask=None, intermediate_output=None):
+        optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
+
+        if intermediate_output is not None:
+            if intermediate_output < 0:
+                intermediate_output = len(self.layer) + intermediate_output
+
+        intermediate = None
+        for i, l in enumerate(self.layer):
+            x = l(x, mask, optimized_attention)
+            if i == intermediate_output:
+                intermediate = x.clone()
+        return x, intermediate
+
+class BertEmbeddings(torch.nn.Module):
+    def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations):
+        super().__init__()
+        self.word_embeddings = torch.nn.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
+        self.position_embeddings = torch.nn.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
+        self.token_type_embeddings = torch.nn.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
+
+        self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device)
+
+    def forward(self, input_tokens, token_type_ids=None):
+        x = self.word_embeddings(input_tokens)
+        x += self.position_embeddings.weight[:x.shape[1]]
+        if token_type_ids is not None:
+            x += self.token_type_embeddings(token_type_ids)
+        else:
+            x += self.token_type_embeddings.weight[0]
+        x = self.LayerNorm(x)
+        return x
+
+
+class BertModel_(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        embed_dim = config_dict["hidden_size"]
+        layer_norm_eps = config_dict["layer_norm_eps"]
+
+        self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations)
+        self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations)
+
+    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
+        x = self.embeddings(input_tokens)
+        mask = None
+        if attention_mask is not None:
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+
+        x, i = self.encoder(x, mask, intermediate_output)
+        return x, i
+
+
+class BertModel(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.bert = BertModel_(config_dict, dtype, device, operations)
+        self.num_layers = config_dict["num_hidden_layers"]
+
+    def get_input_embeddings(self):
+        return self.bert.embeddings.word_embeddings
+
+    def set_input_embeddings(self, embeddings):
+        self.bert.embeddings.word_embeddings = embeddings
+
+    def forward(self, *args, **kwargs):
+        return self.bert(*args, **kwargs)
diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py
index e47c8cb20..fc1d3c75d 100644
--- a/comfy/text_encoders/hydit.py
+++ b/comfy/text_encoders/hydit.py
@@ -1,56 +1,15 @@
 from comfy import sd1_clip
-from transformers import T5TokenizerFast, BertTokenizer, BertModel, modeling_utils, BertConfig
+from transformers import BertTokenizer
 from .spiece_tokenizer import SPieceTokenizer
+from .bert import BertModel
 import comfy.text_encoders.t5
 import os
-
 import torch
-import contextlib
-
-@contextlib.contextmanager
-def use_comfy_ops(ops, device=None, dtype=None):
-    old_torch_nn_linear = torch.nn.Linear
-    force_device = device
-    force_dtype = dtype
-    def linear_with_dtype(in_features: int, out_features: int, bias: bool = True, device=None, dtype=None):
-        if force_device is not None:
-            device = force_device
-        if force_dtype is not None:
-            dtype = force_dtype
-        return ops.Linear(in_features, out_features, bias=bias, device=device, dtype=dtype)
-
-    torch.nn.Linear = linear_with_dtype
-    try:
-        yield
-    finally:
-        torch.nn.Linear = old_torch_nn_linear
-
-
-class RobertaWrapper(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        config = BertConfig(**config_dict)
-        with use_comfy_ops(operations, device, dtype):
-            with modeling_utils.no_init_weights():
-                self.bert = BertModel(config, add_pooling_layer=False)
-
-        self.num_layers = config.num_hidden_layers
-
-    def get_input_embeddings(self):
-        return self.bert.get_input_embeddings()
-
-    def set_input_embeddings(self, value):
-        return self.bert.set_input_embeddings(value)
-
-    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
-        intermediate = None
-        out = self.bert(input_ids=input_tokens, output_hidden_states=intermediate_output is not None, attention_mask=attention_mask)
-        return out.last_hidden_state, intermediate, out.pooler_output
 
 class HyditBertModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json")
-        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=RobertaWrapper, enable_attention_masks=True, return_attention_masks=True)
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True)
 
 class HyditBertTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):

From afe732bef960d753661acb5b886ba42573dd3720 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 11:52:58 -0400
Subject: [PATCH 236/315] Hunyuan dit can now accept longer prompts.

---
 comfy/ldm/hydit/models.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py
index 4bd7abab6..bde9687e2 100644
--- a/comfy/ldm/hydit/models.py
+++ b/comfy/ldm/hydit/models.py
@@ -319,13 +319,15 @@ class HunYuanDiT(nn.Module):
         text_states_mask = text_embedding_mask.bool()           # 2,77
         text_states_t5_mask = text_embedding_mask_t5.bool()     # 2,256
         b_t5, l_t5, c_t5 = text_states_t5.shape
-        text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5))
-        text_states = torch.cat([text_states, text_states_t5.view(b_t5, l_t5, -1)], dim=1)  # 2,205，1024
+        text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)
 
-        clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1)
+        padding = self.text_embedding_padding.to(text_states)
 
-        clip_t5_mask = clip_t5_mask
-        text_states = torch.where(clip_t5_mask.unsqueeze(2), text_states, self.text_embedding_padding.to(text_states))
+        text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len])
+        text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:])
+
+        text_states = torch.cat([text_states, text_states_t5], dim=1)  # 2,205，1024
+        # clip_t5_mask = torch.cat([text_states_mask, text_states_t5_mask], dim=-1)
 
         _, _, oh, ow = x.shape
         th, tw = (oh + (self.patch_size // 2)) // self.patch_size, (ow + (self.patch_size // 2)) // self.patch_size

From 8328a2d8cdabd0e42b856dd0193ebc24ea41c359 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 12:11:32 -0400
Subject: [PATCH 237/315] Let hunyuan dit work with all prompt lengths.

---
 comfy/ldm/hydit/poolers.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/comfy/ldm/hydit/poolers.py b/comfy/ldm/hydit/poolers.py
index 3470041b5..2c6e46e67 100644
--- a/comfy/ldm/hydit/poolers.py
+++ b/comfy/ldm/hydit/poolers.py
@@ -16,6 +16,7 @@ class AttentionPool(nn.Module):
         self.embed_dim = embed_dim
 
     def forward(self, x):
+        x = x[:,:self.positional_embedding.shape[0] - 1]
         x = x.permute(1, 0, 2)  # NLC -> LNC
         x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
         x = x + self.positional_embedding[:, None, :].to(dtype=x.dtype, device=x.device)  # (L+1)NC

From b6779d8df310bcac115d9949fcc6c7502b4c9551 Mon Sep 17 00:00:00 2001
From: filtered <176114999+webfiltered@users.noreply.github.com>
Date: Sat, 27 Jul 2024 02:25:42 +1000
Subject: [PATCH 238/315] Fix undo incorrectly undoing text input (#4114)

Fixes an issue where under certain conditions, the ComfyUI custom undo / redo functions would not run when intended to.

When trying to undo an action like deleting several nodes, instead the native browser undo runs - e.g. a textarea gets focus and the last typed text is undone.  Clicking outside the text area and typing again just keeps doing the same thing.
---
 web/scripts/changeTracker.js | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js
index 39bc4a810..d7798dbe9 100644
--- a/web/scripts/changeTracker.js
+++ b/web/scripts/changeTracker.js
@@ -105,15 +105,16 @@ export class ChangeTracker {
 		window.addEventListener(
 			"keydown",
 			(e) => {
+				const activeEl = document.activeElement;
 				requestAnimationFrame(async () => {
-					let activeEl;
+					let bindInputEl;
 					// If we are auto queue in change mode then we do want to trigger on inputs
 					if (!app.ui.autoQueueEnabled || app.ui.autoQueueMode === "instant") {
-						activeEl = document.activeElement;
 						if (activeEl?.tagName === "INPUT" || activeEl?.["type"] === "textarea") {
 							// Ignore events on inputs, they have their native history
 							return;
 						}
+						bindInputEl = activeEl;
 					}
 
 					keyIgnored = e.key === "Control" || e.key === "Shift" || e.key === "Alt" || e.key === "Meta";
@@ -123,7 +124,7 @@ export class ChangeTracker {
 					if (await changeTracker().undoRedo(e)) return;
 
 					// If our active element is some type of input then handle changes after they're done
-					if (ChangeTracker.bindInput(activeEl)) return;
+					if (ChangeTracker.bindInput(bindInputEl)) return;
 					changeTracker().checkState();
 				});
 			},

From 6225a7827c17bc237167f8500029d282f4c91950 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 13:04:48 -0400
Subject: [PATCH 239/315] Add CLIPTextEncodeHunyuanDiT.

Useful for testing what each text encoder does.
---
 comfy_extras/nodes_hunyuan.py | 24 ++++++++++++++++++++++++
 nodes.py                      |  1 +
 2 files changed, 25 insertions(+)
 create mode 100644 comfy_extras/nodes_hunyuan.py

diff --git a/comfy_extras/nodes_hunyuan.py b/comfy_extras/nodes_hunyuan.py
new file mode 100644
index 000000000..a3ac8cb06
--- /dev/null
+++ b/comfy_extras/nodes_hunyuan.py
@@ -0,0 +1,24 @@
+class CLIPTextEncodeHunyuanDiT:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "clip": ("CLIP", ),
+            "bert": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning"
+
+    def encode(self, clip, bert, mt5xl):
+        tokens = clip.tokenize(bert)
+        tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]
+
+        output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
+        cond = output.pop("cond")
+        return ([[cond, output]], )
+
+NODE_CLASS_MAPPINGS = {
+    "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
+}
diff --git a/nodes.py b/nodes.py
index fbdcb6c91..dc66dd3ef 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2037,6 +2037,7 @@ def init_builtin_extra_nodes():
         "nodes_sd3.py",
         "nodes_gits.py",
         "nodes_controlnet.py",
+        "nodes_hunyuan.py",
     ]
 
     import_failed = []

From cf4418b806af5f7f67e3ce5b6ee386360b410bbb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 13:07:39 -0400
Subject: [PATCH 240/315] Don't treat Bert model like CLIP.

Bert can accept up to 512 tokens so any prompt with more than 77 should
just be passed to it as is instead of splitting it up like CLIP.
---
 comfy/text_encoders/hydit.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py
index fc1d3c75d..ac0c893bc 100644
--- a/comfy/text_encoders/hydit.py
+++ b/comfy/text_encoders/hydit.py
@@ -14,7 +14,7 @@ class HyditBertModel(sd1_clip.SDClipModel):
 class HyditBertTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer")
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer)
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77)
 
 
 class MT5XLModel(sd1_clip.SDClipModel):

From 17b41f622ef64ba9a9ce38da0f1038e718508dc6 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Fri, 26 Jul 2024 11:37:40 -0700
Subject: [PATCH 241/315] Change windows standalone URL to stable release.
 (#4065)

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c2100cab2..6a68f268a 100644
--- a/README.md
+++ b/README.md
@@ -78,7 +78,7 @@ Ctrl can also be replaced with Cmd instead for macOS users
 
 There is a portable standalone build for Windows that should work for running on Nvidia GPUs or for running on your CPU only on the [releases page](https://github.com/comfyanonymous/ComfyUI/releases).
 
-### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/download/latest/ComfyUI_windows_portable_nvidia_cu121_or_cpu.7z)
+### [Direct link to download](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia.7z)
 
 Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
 

From 45a2842d7fe2ed92c3402c0a17b55bd46366b407 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 14:52:00 -0400
Subject: [PATCH 242/315] Set stable releases as a prerelease initially.

This should give time to test the standalone package before making it live.
---
 .github/workflows/stable-release.yml | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml
index 1fd76b530..19035c02c 100644
--- a/.github/workflows/stable-release.yml
+++ b/.github/workflows/stable-release.yml
@@ -106,4 +106,5 @@ jobs:
           file: ComfyUI_windows_portable_nvidia.7z
           tag: ${{ github.ref }}
           overwrite: true
-        
+          prerelease: true
+          make_latest: false

From e746965c5051660ffa10330c00461094270f0207 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 27 Jul 2024 01:20:18 -0400
Subject: [PATCH 243/315] Update nightly package workflow.

---
 .github/workflows/windows_release_nightly_pytorch.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml
index e68011b64..ba388cd44 100644
--- a/.github/workflows/windows_release_nightly_pytorch.yml
+++ b/.github/workflows/windows_release_nightly_pytorch.yml
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "3"
+        default: "4"
 #  push:
 #    branches:
 #      - master
@@ -49,7 +49,7 @@ jobs:
             echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
             curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
             ./python.exe get-pip.py
-            python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 numpy==1.26.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
+            python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
             ls ../temp_wheel_dir
             ./python.exe -s -m pip install --pre ../temp_wheel_dir/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth

From 07f6a1a685d16a1b7e2c4b05d94670d2543ec29e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 27 Jul 2024 03:15:22 -0400
Subject: [PATCH 244/315] Handle case in the updater when master branch is not
 in local repo.

---
 .ci/update_windows/update.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/.ci/update_windows/update.py b/.ci/update_windows/update.py
index 127247b2f..cc79a914e 100755
--- a/.ci/update_windows/update.py
+++ b/.ci/update_windows/update.py
@@ -62,8 +62,15 @@ except:
 
 print("checking out master branch")
 branch = repo.lookup_branch('master')
-ref = repo.lookup_reference(branch.name)
-repo.checkout(ref)
+if branch is None:
+    ref = repo.lookup_reference('refs/remotes/origin/master')
+    repo.checkout(ref)
+    branch = repo.lookup_branch('master')
+    if branch is None:
+        repo.create_branch('master', repo.get(ref.target))
+else:
+    ref = repo.lookup_reference(branch.name)
+    repo.checkout(ref)
 
 print("pulling latest changes")
 pull(repo)

From e6829e7ac5bef5db8099005b5b038c49e173e87c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 27 Jul 2024 04:41:46 -0400
Subject: [PATCH 245/315] Add a way to set custom dependencies in the release
 workflow.

---
 .github/workflows/windows_release_dependencies.yml | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml
index 5aa57e7d7..8d3a8665d 100644
--- a/.github/workflows/windows_release_dependencies.yml
+++ b/.github/workflows/windows_release_dependencies.yml
@@ -8,11 +8,16 @@ on:
         required: false
         type: string
         default: ""
+      extra_dependencies:
+        description: 'extra dependencies'
+        required: false
+        type: string
+        default: "\"numpy<2\""
       cu:
         description: 'cuda version'
         required: true
         type: string
-        default: "121"
+        default: "124"
 
       python_minor:
         description: 'python minor version'
@@ -24,7 +29,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "8"
+        default: "9"
 #  push:
 #    branches:
 #      - master
@@ -51,7 +56,7 @@ jobs:
             ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
             pause" > update_comfyui_and_python_dependencies.bat
 
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
+            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
             python -m pip install --no-cache-dir ./temp_wheel_dir/*
             echo installed basic
             ls -lah temp_wheel_dir

From f82d09c9b40fd9ebbc080bc5662ddb39787b1ec9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 27 Jul 2024 04:48:19 -0400
Subject: [PATCH 246/315] Update packaging workflow.

---
 .github/workflows/windows_release_package.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml
index 020741c41..5aed73e55 100644
--- a/.github/workflows/windows_release_package.yml
+++ b/.github/workflows/windows_release_package.yml
@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "121"
+        default: "124"
 
       python_minor:
         description: 'python minor version'
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "8"
+        default: "9"
 #  push:
 #    branches:
 #      - master

From 93000580261971971ebb12aff03f6bc3ce30a9f2 Mon Sep 17 00:00:00 2001
From: Silver <65376327+silveroxides@users.noreply.github.com>
Date: Sat, 27 Jul 2024 22:19:50 +0200
Subject: [PATCH 247/315] Add dpmpp_2s_ancestral as custom sampler (#4101)

Adding dpmpp_2s_ancestral as custom sampler node to enable its use with eta and s_noise when using custom sampling.
---
 comfy_extras/nodes_custom_sampler.py | 20 +++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
index b7ab88c27..219975e27 100644
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -295,6 +295,23 @@ class SamplerDPMPP_SDE:
         sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
         return (sampler, )
 
+class SamplerDPMPP_2S_Ancestral:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta, s_noise):
+        sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise})
+        return (sampler, )
+
 class SamplerEulerAncestral:
     @classmethod
     def INPUT_TYPES(s):
@@ -666,6 +683,7 @@ NODE_CLASS_MAPPINGS = {
     "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE,
     "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
     "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
+    "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral,
     "SamplerDPMAdaptative": SamplerDPMAdaptative,
     "SplitSigmas": SplitSigmas,
     "SplitSigmasDenoise": SplitSigmasDenoise,
@@ -682,4 +700,4 @@ NODE_CLASS_MAPPINGS = {
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++",
-}
\ No newline at end of file
+}

From ab76abc7676ea30726661b85c4fb85f84a3ff5aa Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Sat, 27 Jul 2024 20:34:19 -0700
Subject: [PATCH 248/315] Active workflow use primary fg color (#4090)

---
 web/scripts/ui/menu/menu.css | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/web/scripts/ui/menu/menu.css b/web/scripts/ui/menu/menu.css
index afaed3fb0..1d8b9ad0a 100644
--- a/web/scripts/ui/menu/menu.css
+++ b/web/scripts/ui/menu/menu.css
@@ -330,6 +330,7 @@
 
 .comfyui-workflows-open .active {
 	font-weight: bold;
+	color: var(--primary-fg);
 }
 
 .comfyui-workflows-favorites:empty {
@@ -417,6 +418,10 @@
 	padding: 2px 4px;
 }
 
+.comfyui-workflows-tree-file.active .comfyui-workflows-file-action {
+	color: var(--primary-fg);
+}
+
 .lg ~ .comfyui-workflows-popup .comfyui-workflows-tree-file:not(:hover) .comfyui-workflows-file-action {
 	opacity: 0;
 }

From 4ba7fa0244badcf901f2b8ddbfb8539c6398672f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 28 Jul 2024 01:19:20 -0400
Subject: [PATCH 249/315] Refactor: Move sd2_clip.py to text_encoders folder.

---
 comfy/sd.py                                    | 6 +++---
 comfy/supported_models.py                      | 4 ++--
 comfy/{ => text_encoders}/sd2_clip.py          | 0
 comfy/{ => text_encoders}/sd2_clip_config.json | 0
 4 files changed, 5 insertions(+), 5 deletions(-)
 rename comfy/{ => text_encoders}/sd2_clip.py (100%)
 rename comfy/{ => text_encoders}/sd2_clip_config.json (100%)

diff --git a/comfy/sd.py b/comfy/sd.py
index b1d38c4b2..8bf8d1087 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -17,8 +17,8 @@ from . import diffusers_convert
 from . import model_detection
 
 from . import sd1_clip
-from . import sd2_clip
 from . import sdxl_clip
+import comfy.text_encoders.sd2_clip
 import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
@@ -414,8 +414,8 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
                 clip_target.clip = sdxl_clip.SDXLRefinerClipModel
                 clip_target.tokenizer = sdxl_clip.SDXLTokenizer
         elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
-            clip_target.clip = sd2_clip.SD2ClipModel
-            clip_target.tokenizer = sd2_clip.SD2Tokenizer
+            clip_target.clip = comfy.text_encoders.sd2_clip.SD2ClipModel
+            clip_target.tokenizer = comfy.text_encoders.sd2_clip.SD2Tokenizer
         elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
             weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
             dtype_t5 = weight.dtype
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 40417eb4d..ddd0c173b 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -3,8 +3,8 @@ from . import model_base
 from . import utils
 
 from . import sd1_clip
-from . import sd2_clip
 from . import sdxl_clip
+import comfy.text_encoders.sd2_clip
 import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
@@ -101,7 +101,7 @@ class SD20(supported_models_base.BASE):
         return state_dict
 
     def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
+        return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel)
 
 class SD21UnclipL(SD20):
     unet_config = {
diff --git a/comfy/sd2_clip.py b/comfy/text_encoders/sd2_clip.py
similarity index 100%
rename from comfy/sd2_clip.py
rename to comfy/text_encoders/sd2_clip.py
diff --git a/comfy/sd2_clip_config.json b/comfy/text_encoders/sd2_clip_config.json
similarity index 100%
rename from comfy/sd2_clip_config.json
rename to comfy/text_encoders/sd2_clip_config.json

From c75b50607b4ab78ef9c7c5c9e0c8672146ead91b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 29 Jul 2024 11:15:37 -0400
Subject: [PATCH 250/315] Less confusing exception if pillow() function fails.

---
 node_helpers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/node_helpers.py b/node_helpers.py
index fee628790..4b38bfff8 100644
--- a/node_helpers.py
+++ b/node_helpers.py
@@ -25,7 +25,7 @@ def pillow(fn, arg):
     finally:
         if prev_value is not None:
             ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
-        return x
+    return x
 
 def hasher():
     hashfuncs = {

From 66d35c07ce44b07011314ad7a28b2bdbcbb4e4cc Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 29 Jul 2024 20:27:40 -0400
Subject: [PATCH 251/315] Improve artifacts on hydit, auraflow and SD3 on
 specific resolutions.

This breaks seeds for resolutions that are not a multiple of 16 in pixel
resolution by using circular padding instead of reflection padding but
should lower the amount of artifacts when doing img2img at those
resolutions.
---
 comfy/ldm/aura/mmdit.py                     | 2 +-
 comfy/ldm/modules/diffusionmodules/mmdit.py | 4 +++-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py
index c465619bd..2564166a4 100644
--- a/comfy/ldm/aura/mmdit.py
+++ b/comfy/ldm/aura/mmdit.py
@@ -409,7 +409,7 @@ class MMDiT(nn.Module):
         pad_h = (self.patch_size - H % self.patch_size) % self.patch_size
         pad_w = (self.patch_size - W % self.patch_size) % self.patch_size
 
-        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
+        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='circular')
         x = x.view(
             B,
             C,
diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
index f37f7ff77..aac48a7f6 100644
--- a/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -69,12 +69,14 @@ class PatchEmbed(nn.Module):
             bias: bool = True,
             strict_img_size: bool = True,
             dynamic_img_pad: bool = True,
+            padding_mode='circular',
             dtype=None,
             device=None,
             operations=None,
     ):
         super().__init__()
         self.patch_size = (patch_size, patch_size)
+        self.padding_mode = padding_mode
         if img_size is not None:
             self.img_size = (img_size, img_size)
             self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
@@ -110,7 +112,7 @@ class PatchEmbed(nn.Module):
         if self.dynamic_img_pad:
             pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
             pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
-            x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
+            x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode=self.padding_mode)
         x = self.proj(x)
         if self.flatten:
             x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC

From 79040635dace9466a9f3fe20b5604b8e8e79f44f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 30 Jul 2024 05:01:34 -0400
Subject: [PATCH 252/315] Remove unnecessary code.

---
 comfy/ldm/modules/diffusionmodules/openaimodel.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py
index ba8fc2c4a..6535e899c 100644
--- a/comfy/ldm/modules/diffusionmodules/openaimodel.py
+++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py
@@ -809,7 +809,7 @@ class UNetModel(nn.Module):
         self.out = nn.Sequential(
             operations.GroupNorm(32, ch, dtype=self.dtype, device=device),
             nn.SiLU(),
-            zero_module(operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device)),
+            operations.conv_nd(dims, model_channels, out_channels, 3, padding=1, dtype=self.dtype, device=device),
         )
         if self.predict_codebook_ids:
             self.id_predictor = nn.Sequential(

From 25853d0be8be6622195afaba2bc92e49e518bdcc Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 30 Jul 2024 05:03:20 -0400
Subject: [PATCH 253/315] Use common function for casting weights to input.

---
 comfy/ldm/audio/dit.py                      | 23 ++++++++++---------
 comfy/ldm/aura/mmdit.py                     |  5 +++--
 comfy/ldm/cascade/common.py                 | 15 ++++---------
 comfy/ldm/hydit/models.py                   |  5 +++--
 comfy/ldm/hydit/poolers.py                  |  6 ++---
 comfy/ldm/modules/diffusionmodules/mmdit.py |  3 ++-
 comfy/ops.py                                | 25 +++++++++++++++++++--
 7 files changed, 51 insertions(+), 31 deletions(-)

diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py
index 1c1112c5e..4d2185be8 100644
--- a/comfy/ldm/audio/dit.py
+++ b/comfy/ldm/audio/dit.py
@@ -9,6 +9,7 @@ from einops import rearrange
 from torch import nn
 from torch.nn import functional as F
 import math
+import comfy.ops
 
 class FourierFeatures(nn.Module):
     def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
@@ -18,7 +19,7 @@ class FourierFeatures(nn.Module):
             [out_features // 2, in_features], dtype=dtype, device=device))
 
     def forward(self, input):
-        f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device)
+        f = 2 * math.pi * input @ comfy.ops.cast_to_input(self.weight.T, input)
         return torch.cat([f.cos(), f.sin()], dim=-1)
 
 # norms
@@ -38,9 +39,9 @@ class LayerNorm(nn.Module):
 
     def forward(self, x):
         beta = self.beta
-        if self.beta is not None:
-            beta = beta.to(dtype=x.dtype, device=x.device)
-        return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta)
+        if beta is not None:
+            beta = comfy.ops.cast_to_input(beta, x)
+        return F.layer_norm(x, x.shape[-1:], weight=comfy.ops.cast_to_input(self.gamma, x), bias=beta)
 
 class GLU(nn.Module):
     def __init__(
@@ -123,7 +124,9 @@ class RotaryEmbedding(nn.Module):
         scale_base = 512,
         interpolation_factor = 1.,
         base = 10000,
-        base_rescale_factor = 1.
+        base_rescale_factor = 1.,
+        dtype=None,
+        device=None,
     ):
         super().__init__()
         # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
@@ -131,8 +134,8 @@ class RotaryEmbedding(nn.Module):
         # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
         base *= base_rescale_factor ** (dim / (dim - 2))
 
-        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
-        self.register_buffer('inv_freq', inv_freq)
+        # inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', torch.empty((dim // 2,), device=device, dtype=dtype))
 
         assert interpolation_factor >= 1.
         self.interpolation_factor = interpolation_factor
@@ -161,14 +164,14 @@ class RotaryEmbedding(nn.Module):
 
         t = t / self.interpolation_factor
 
-        freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device))
+        freqs = torch.einsum('i , j -> i j', t, comfy.ops.cast_to_input(self.inv_freq, t))
         freqs = torch.cat((freqs, freqs), dim = -1)
 
         if self.scale is None:
             return freqs, 1.
 
         power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
-        scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1')
+        scale = comfy.ops.cast_to_input(self.scale, t) ** rearrange(power, 'n -> n 1')
         scale = torch.cat((scale, scale), dim = -1)
 
         return freqs, scale
@@ -568,7 +571,7 @@ class ContinuousTransformer(nn.Module):
         self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
 
         if rotary_pos_emb:
-            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
+            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32), device=device, dtype=dtype)
         else:
             self.rotary_pos_emb = None
 
diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py
index 2564166a4..9956d3638 100644
--- a/comfy/ldm/aura/mmdit.py
+++ b/comfy/ldm/aura/mmdit.py
@@ -8,6 +8,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from comfy.ldm.modules.attention import optimized_attention
+import comfy.ops
 
 def modulate(x, shift, scale):
     return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
@@ -427,7 +428,7 @@ class MMDiT(nn.Module):
         max_dim = max(h, w)
 
         cur_dim = self.h_max
-        pos_encoding = self.positional_encoding.reshape(1, cur_dim, cur_dim, -1).to(device=x.device, dtype=x.dtype)
+        pos_encoding = comfy.ops.cast_to_input(self.positional_encoding.reshape(1, cur_dim, cur_dim, -1), x)
 
         if max_dim > cur_dim:
             pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
@@ -455,7 +456,7 @@ class MMDiT(nn.Module):
         t = timestep
 
         c = self.cond_seq_linear(c_seq)  # B, T_c, D
-        c = torch.cat([self.register_tokens.to(device=c.device, dtype=c.dtype).repeat(c.size(0), 1, 1), c], dim=1)
+        c = torch.cat([comfy.ops.cast_to_input(self.register_tokens, c).repeat(c.size(0), 1, 1), c], dim=1)
 
         global_cond = self.t_embedder(t, x.dtype)  # B, D
 
diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py
index 124902c09..3eaa0c821 100644
--- a/comfy/ldm/cascade/common.py
+++ b/comfy/ldm/cascade/common.py
@@ -19,14 +19,7 @@
 import torch
 import torch.nn as nn
 from comfy.ldm.modules.attention import optimized_attention
-
-class Linear(torch.nn.Linear):
-    def reset_parameters(self):
-        return None
-
-class Conv2d(torch.nn.Conv2d):
-    def reset_parameters(self):
-        return None
+import comfy.ops
 
 class OptimizedAttention(nn.Module):
     def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
@@ -78,13 +71,13 @@ class GlobalResponseNorm(nn.Module):
     "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105"
     def __init__(self, dim, dtype=None, device=None):
         super().__init__()
-        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
-        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
+        self.gamma = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device))
+        self.beta = nn.Parameter(torch.empty(1, 1, 1, dim, dtype=dtype, device=device))
 
     def forward(self, x):
         Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
         Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
-        return self.gamma.to(device=x.device, dtype=x.dtype) * (x * Nx) + self.beta.to(device=x.device, dtype=x.dtype) + x
+        return comfy.ops.cast_to_input(self.gamma, x) * (x * Nx) + comfy.ops.cast_to_input(self.beta, x) + x
 
 
 class ResBlock(nn.Module):
diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py
index bde9687e2..37836fc31 100644
--- a/comfy/ldm/hydit/models.py
+++ b/comfy/ldm/hydit/models.py
@@ -4,6 +4,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
+import comfy.ops
 from comfy.ldm.modules.diffusionmodules.mmdit import Mlp, TimestepEmbedder, PatchEmbed, RMSNorm
 from comfy.ldm.modules.diffusionmodules.util import timestep_embedding
 from torch.utils import checkpoint
@@ -234,7 +235,7 @@ class HunYuanDiT(nn.Module):
 
         if self.use_style_cond:
             # Here we use a default learned embedder layer for future extension.
-            self.style_embedder = nn.Embedding(1, hidden_size, dtype=dtype, device=device)
+            self.style_embedder = operations.Embedding(1, hidden_size, dtype=dtype, device=device)
             self.extra_in_dim += hidden_size
 
         # Text embedding for `add`
@@ -321,7 +322,7 @@ class HunYuanDiT(nn.Module):
         b_t5, l_t5, c_t5 = text_states_t5.shape
         text_states_t5 = self.mlp_t5(text_states_t5.view(-1, c_t5)).view(b_t5, l_t5, -1)
 
-        padding = self.text_embedding_padding.to(text_states)
+        padding = comfy.ops.cast_to_input(self.text_embedding_padding, text_states)
 
         text_states[:,-self.text_len:] = torch.where(text_states_mask[:,-self.text_len:].unsqueeze(2), text_states[:,-self.text_len:], padding[:self.text_len])
         text_states_t5[:,-self.text_len_t5:] = torch.where(text_states_t5_mask[:,-self.text_len_t5:].unsqueeze(2), text_states_t5[:,-self.text_len_t5:], padding[self.text_len:])
diff --git a/comfy/ldm/hydit/poolers.py b/comfy/ldm/hydit/poolers.py
index 2c6e46e67..f5e5b406f 100644
--- a/comfy/ldm/hydit/poolers.py
+++ b/comfy/ldm/hydit/poolers.py
@@ -1,8 +1,8 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from comfy.ldm.modules.attention import optimized_attention #TODO
-
+from comfy.ldm.modules.attention import optimized_attention
+import comfy.ops
 
 class AttentionPool(nn.Module):
     def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None, dtype=None, device=None, operations=None):
@@ -19,7 +19,7 @@ class AttentionPool(nn.Module):
         x = x[:,:self.positional_embedding.shape[0] - 1]
         x = x.permute(1, 0, 2)  # NLC -> LNC
         x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (L+1)NC
-        x = x + self.positional_embedding[:, None, :].to(dtype=x.dtype, device=x.device)  # (L+1)NC
+        x = x + comfy.ops.cast_to_input(self.positional_embedding[:, None, :], x) # (L+1)NC
 
         q = self.q_proj(x[:1])
         k = self.k_proj(x)
diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
index aac48a7f6..ea1b5aa05 100644
--- a/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -8,6 +8,7 @@ import torch.nn as nn
 from .. import attention
 from einops import rearrange, repeat
 from .util import timestep_embedding
+import comfy.ops
 
 def default(x, y):
     if x is not None:
@@ -926,7 +927,7 @@ class MMDiT(nn.Module):
             context = self.context_processor(context)
 
         hw = x.shape[-2:]
-        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
+        x = self.x_embedder(x) + comfy.ops.cast_to_input(self.cropped_pos_embed(hw, device=x.device), x)
         c = self.t_embedder(t, dtype=x.dtype)  # (N, D)
         if y is not None and self.y_embedder is not None:
             y = self.y_embedder(y)  # (N, D)
diff --git a/comfy/ops.py b/comfy/ops.py
index 0f1ceb574..6d88a1b9b 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -19,14 +19,17 @@
 import torch
 import comfy.model_management
 
+def cast_to_input(weight, input, non_blocking=False):
+    return weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
+
 def cast_bias_weight(s, input):
     bias = None
     non_blocking = comfy.model_management.device_should_use_non_blocking(input.device)
     if s.bias is not None:
-        bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
+        bias = cast_to_input(s.bias, input, non_blocking=non_blocking)
         if s.bias_function is not None:
             bias = s.bias_function(bias)
-    weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
+    weight = cast_to_input(s.weight, input, non_blocking=non_blocking)
     if s.weight_function is not None:
         weight = s.weight_function(weight)
     return weight, bias
@@ -168,6 +171,21 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
+    class Embedding(torch.nn.Embedding, CastWeightBiasOp):
+        def reset_parameters(self):
+            self.bias = None
+            return None
+
+        def forward_comfy_cast_weights(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse)
+
+        def forward(self, *args, **kwargs):
+            if self.comfy_cast_weights:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
     @classmethod
     def conv_nd(s, dims, *args, **kwargs):
         if dims == 2:
@@ -202,3 +220,6 @@ class manual_cast(disable_weight_init):
 
     class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
         comfy_cast_weights = True
+
+    class Embedding(disable_weight_init.Embedding):
+        comfy_cast_weights = True

From 82cae45d44df3bd2d042c32d9b56cd3056bd0f7f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 30 Jul 2024 14:20:28 -0400
Subject: [PATCH 254/315] Fix potential issue with non clip text embeddings.

---
 comfy/clip_config_bigg.json              | 2 +-
 comfy/clip_model.py                      | 3 ++-
 comfy/sd1_clip.py                        | 7 ++-----
 comfy/sd1_clip_config.json               | 2 +-
 comfy/text_encoders/sd2_clip_config.json | 2 +-
 5 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/comfy/clip_config_bigg.json b/comfy/clip_config_bigg.json
index 32d82ff39..35261deef 100644
--- a/comfy/clip_config_bigg.json
+++ b/comfy/clip_config_bigg.json
@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1280,
   "initializer_factor": 1.0,
diff --git a/comfy/clip_model.py b/comfy/clip_model.py
index 14f43c568..ab775309c 100644
--- a/comfy/clip_model.py
+++ b/comfy/clip_model.py
@@ -87,6 +87,7 @@ class CLIPTextModel_(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
+        self.eos_token_id = config_dict["eos_token_id"]
 
         super().__init__()
         self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
@@ -111,7 +112,7 @@ class CLIPTextModel_(torch.nn.Module):
         if i is not None and final_layer_norm_intermediate:
             i = self.final_layer_norm(i)
 
-        pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]
+        pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),]
         return x, i, pooled_output
 
 class CLIPTextModel(torch.nn.Module):
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index c7bc1e4dc..f209bed41 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -140,15 +140,13 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
     def set_up_textual_embeddings(self, tokens, current_embeds):
         out_tokens = []
-        next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
+        next_new_token = token_dict_size = current_embeds.weight.shape[0]
         embedding_weights = []
 
         for x in tokens:
             tokens_temp = []
             for y in x:
                 if isinstance(y, numbers.Integral):
-                    if y == token_dict_size: #EOS token
-                        y = -1
                     tokens_temp += [int(y)]
                 else:
                     if y.shape[0] == current_embeds.weight.shape[1]:
@@ -164,11 +162,10 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         n = token_dict_size
         if len(embedding_weights) > 0:
             new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
-            new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1]
+            new_embedding.weight[:token_dict_size] = current_embeds.weight
             for x in embedding_weights:
                 new_embedding.weight[n] = x
                 n += 1
-            new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding
             self.transformer.set_input_embeddings(new_embedding)
 
         processed_tokens = []
diff --git a/comfy/sd1_clip_config.json b/comfy/sd1_clip_config.json
index 0158a1fd5..3ba8c6b5b 100644
--- a/comfy/sd1_clip_config.json
+++ b/comfy/sd1_clip_config.json
@@ -6,7 +6,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "quick_gelu",
   "hidden_size": 768,
   "initializer_factor": 1.0,
diff --git a/comfy/text_encoders/sd2_clip_config.json b/comfy/text_encoders/sd2_clip_config.json
index 85cec832b..00893cfdc 100644
--- a/comfy/text_encoders/sd2_clip_config.json
+++ b/comfy/text_encoders/sd2_clip_config.json
@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1024,
   "initializer_factor": 1.0,

From b85216a3c0d4fc443c87cd2af362c1f4d3be50ce Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 31 Jul 2024 00:52:34 -0400
Subject: [PATCH 255/315] Lower T5 memory usage by a few hundred MB.

---
 comfy/ldm/hydit/models.py |  2 +-
 comfy/ops.py              | 33 ++++++++++++++++++++++++---------
 comfy/text_encoders/t5.py | 15 ++++++++-------
 3 files changed, 33 insertions(+), 17 deletions(-)

diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py
index 37836fc31..c70dbf92a 100644
--- a/comfy/ldm/hydit/models.py
+++ b/comfy/ldm/hydit/models.py
@@ -355,7 +355,7 @@ class HunYuanDiT(nn.Module):
         if self.use_style_cond:
             if style is None:
                 style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int)
-            style_embedding = self.style_embedder(style)
+            style_embedding = self.style_embedder(style, out_dtype=x.dtype)
             extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
 
         # Concatenate all extra vectors
diff --git a/comfy/ops.py b/comfy/ops.py
index 6d88a1b9b..47e8d7a9d 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -19,17 +19,27 @@
 import torch
 import comfy.model_management
 
-def cast_to_input(weight, input, non_blocking=False):
-    return weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
 
-def cast_bias_weight(s, input):
+def cast_to(weight, dtype=None, device=None, non_blocking=False):
+    return weight.to(device=device, dtype=dtype, non_blocking=non_blocking)
+
+def cast_to_input(weight, input, non_blocking=False):
+    return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking)
+
+def cast_bias_weight(s, input=None, dtype=None, device=None):
+    if input is not None:
+        if dtype is None:
+            dtype = input.dtype
+        if device is None:
+            device = input.device
+
     bias = None
-    non_blocking = comfy.model_management.device_should_use_non_blocking(input.device)
+    non_blocking = comfy.model_management.device_should_use_non_blocking(device)
     if s.bias is not None:
-        bias = cast_to_input(s.bias, input, non_blocking=non_blocking)
+        bias = cast_to(s.bias, dtype, device, non_blocking=non_blocking)
         if s.bias_function is not None:
             bias = s.bias_function(bias)
-    weight = cast_to_input(s.weight, input, non_blocking=non_blocking)
+    weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking)
     if s.weight_function is not None:
         weight = s.weight_function(weight)
     return weight, bias
@@ -176,14 +186,19 @@ class disable_weight_init:
             self.bias = None
             return None
 
-        def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse)
+        def forward_comfy_cast_weights(self, input, out_dtype=None):
+            output_dtype = out_dtype
+            if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
+                out_dtype = None
+            weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
+            return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
 
         def forward(self, *args, **kwargs):
             if self.comfy_cast_weights:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
+                if "out_dtype" in kwargs:
+                    kwargs.pop("out_dtype")
                 return super().forward(*args, **kwargs)
 
     @classmethod
diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
index ba628478d..2109f4eaa 100644
--- a/comfy/text_encoders/t5.py
+++ b/comfy/text_encoders/t5.py
@@ -1,6 +1,7 @@
 import torch
 import math
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
 
 class T5LayerNorm(torch.nn.Module):
     def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
@@ -11,7 +12,7 @@ class T5LayerNorm(torch.nn.Module):
     def forward(self, x):
         variance = x.pow(2).mean(-1, keepdim=True)
         x = x * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight.to(device=x.device, dtype=x.dtype) * x
+        return comfy.ops.cast_to_input(self.weight, x) * x
 
 activations = {
     "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
@@ -82,7 +83,7 @@ class T5Attention(torch.nn.Module):
         if relative_attention_bias:
             self.relative_attention_num_buckets = 32
             self.relative_attention_max_distance = 128
-            self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
+            self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype)
 
     @staticmethod
     def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
@@ -132,7 +133,7 @@ class T5Attention(torch.nn.Module):
         relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
         return relative_buckets
 
-    def compute_bias(self, query_length, key_length, device):
+    def compute_bias(self, query_length, key_length, device, dtype):
         """Compute binned relative position bias"""
         context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
         memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
@@ -143,7 +144,7 @@ class T5Attention(torch.nn.Module):
             num_buckets=self.relative_attention_num_buckets,
             max_distance=self.relative_attention_max_distance,
         )
-        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
+        values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype)  # shape (query_length, key_length, num_heads)
         values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
         return values
 
@@ -152,7 +153,7 @@ class T5Attention(torch.nn.Module):
         k = self.k(x)
         v = self.v(x)
         if self.relative_attention_bias is not None:
-            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
+            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype)
 
         if past_bias is not None:
             if mask is not None:
@@ -225,7 +226,7 @@ class T5(torch.nn.Module):
 
         self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations)
         self.dtype = dtype
-        self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
+        self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype)
 
     def get_input_embeddings(self):
         return self.shared
@@ -234,5 +235,5 @@ class T5(torch.nn.Module):
         self.shared = embeddings
 
     def forward(self, input_ids, *args, **kwargs):
-        x = self.shared(input_ids)
+        x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
         return self.encoder(x, *args, **kwargs)

From 2c038ccef0f819ee8693a94dd880f05a4eb3808c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 31 Jul 2024 01:32:35 -0400
Subject: [PATCH 256/315] Lower CLIP memory usage by a bit.

---
 comfy/clip_model.py         | 23 ++++++++++++-----------
 comfy/sd1_clip.py           |  7 ++++---
 comfy/text_encoders/bert.py | 21 +++++++++++----------
 comfy/text_encoders/t5.py   |  2 +-
 4 files changed, 28 insertions(+), 25 deletions(-)

diff --git a/comfy/clip_model.py b/comfy/clip_model.py
index ab775309c..3c67b737a 100644
--- a/comfy/clip_model.py
+++ b/comfy/clip_model.py
@@ -1,5 +1,6 @@
 import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
 
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -71,13 +72,13 @@ class CLIPEncoder(torch.nn.Module):
         return x, intermediate
 
 class CLIPEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):
+    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None):
         super().__init__()
-        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens):
-        return self.token_embedding(input_tokens) + self.position_embedding.weight
+    def forward(self, input_tokens, dtype=torch.float32):
+        return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)
 
 
 class CLIPTextModel_(torch.nn.Module):
@@ -90,12 +91,12 @@ class CLIPTextModel_(torch.nn.Module):
         self.eos_token_id = config_dict["eos_token_id"]
 
         super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
+        self.embeddings = CLIPEmbeddings(embed_dim, dtype=dtype, device=device, operations=operations)
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
-        x = self.embeddings(input_tokens)
+    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32):
+        x = self.embeddings(input_tokens, dtype=dtype)
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
@@ -154,11 +155,11 @@ class CLIPVisionEmbeddings(torch.nn.Module):
 
         num_patches = (image_size // patch_size) ** 2
         num_positions = num_patches + 1
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
     def forward(self, pixel_values):
         embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device)
+        return torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
 
 
 class CLIPVision(torch.nn.Module):
@@ -170,7 +171,7 @@ class CLIPVision(torch.nn.Module):
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
 
-        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations)
+        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=dtype, device=device, operations=operations)
         self.pre_layrnorm = operations.LayerNorm(embed_dim)
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.post_layernorm = operations.LayerNorm(embed_dim)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index f209bed41..d32121d1b 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -94,7 +94,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         with open(textmodel_json_config) as f:
             config = json.load(f)
 
-        self.transformer = model_class(config, dtype, device, comfy.ops.manual_cast)
+        self.operations = comfy.ops.manual_cast
+        self.transformer = model_class(config, dtype, device, self.operations)
         self.num_layers = self.transformer.num_layers
 
         self.max_length = max_length
@@ -161,7 +162,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
         n = token_dict_size
         if len(embedding_weights) > 0:
-            new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
+            new_embedding = self.operations.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
             new_embedding.weight[:token_dict_size] = current_embeds.weight
             for x in embedding_weights:
                 new_embedding.weight[n] = x
@@ -194,7 +195,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         if self.enable_attention_masks:
             attention_mask_model = attention_mask
 
-        outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
+        outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32)
         self.transformer.set_input_embeddings(backup_embeds)
 
         if self.layer == "last":
diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py
index b76e76660..fc9bac1d2 100644
--- a/comfy/text_encoders/bert.py
+++ b/comfy/text_encoders/bert.py
@@ -1,5 +1,6 @@
 import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
 
 class BertAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -86,19 +87,19 @@ class BertEncoder(torch.nn.Module):
 class BertEmbeddings(torch.nn.Module):
     def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations):
         super().__init__()
-        self.word_embeddings = torch.nn.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
-        self.position_embeddings = torch.nn.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
-        self.token_type_embeddings = torch.nn.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
+        self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
+        self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
+        self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
 
         self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
-    def forward(self, input_tokens, token_type_ids=None):
-        x = self.word_embeddings(input_tokens)
-        x += self.position_embeddings.weight[:x.shape[1]]
+    def forward(self, input_tokens, token_type_ids=None, dtype=None):
+        x = self.word_embeddings(input_tokens, out_dtype=dtype)
+        x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x)
         if token_type_ids is not None:
-            x += self.token_type_embeddings(token_type_ids)
+            x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype)
         else:
-            x += self.token_type_embeddings.weight[0]
+            x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x)
         x = self.LayerNorm(x)
         return x
 
@@ -112,8 +113,8 @@ class BertModel_(torch.nn.Module):
         self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations)
         self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations)
 
-    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
-        x = self.embeddings(input_tokens)
+    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
+        x = self.embeddings(input_tokens, dtype=dtype)
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
index 2109f4eaa..b6491090a 100644
--- a/comfy/text_encoders/t5.py
+++ b/comfy/text_encoders/t5.py
@@ -200,7 +200,7 @@ class T5Stack(torch.nn.Module):
         self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
         # self.dropout = nn.Dropout(config.dropout_rate)
 
-    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
+    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])

From a5991a7aa6f5dae3af820151abe15cb63ac86ac8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 31 Jul 2024 01:34:57 -0400
Subject: [PATCH 257/315] Fix hunyuan dit text encoder weights always being in
 fp32.

---
 comfy/text_encoders/hydit.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py
index ac0c893bc..9dfa288b1 100644
--- a/comfy/text_encoders/hydit.py
+++ b/comfy/text_encoders/hydit.py
@@ -52,8 +52,8 @@ class HyditTokenizer:
 class HyditModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None):
         super().__init__()
-        self.hydit_clip = HyditBertModel()
-        self.mt5xl = MT5XLModel()
+        self.hydit_clip = HyditBertModel(dtype=dtype)
+        self.mt5xl = MT5XLModel(dtype=dtype)
 
         self.dtypes = set()
         if dtype is not None:

From c24f897352238f040e162a81d253c290635d44fd Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 31 Jul 2024 02:00:19 -0400
Subject: [PATCH 258/315] Fix to get fp8 working on T5 base.

---
 comfy/text_encoders/t5.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
index b6491090a..a1420c6cd 100644
--- a/comfy/text_encoders/t5.py
+++ b/comfy/text_encoders/t5.py
@@ -236,4 +236,6 @@ class T5(torch.nn.Module):
 
     def forward(self, input_ids, *args, **kwargs):
         x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
+        if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+            x = torch.nan_to_num(x) #Fix for fp8 T5 base
         return self.encoder(x, *args, **kwargs)

From e2382b6adb70c65416f3e90a168cbbc5ffe491bd Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 03:58:58 -0400
Subject: [PATCH 259/315] Make lowvram less aggressive when there are large
 amounts of free memory.

---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 047193290..c54a360a7 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -446,7 +446,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
+            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - extra_mem)))
             if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 

From 7ad574bffd31b80c7c89c828c87e5b0557a29b99 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 09:42:17 -0400
Subject: [PATCH 260/315] Mac supports bf16 just make sure you are using the
 latest pytorch.

---
 comfy/model_management.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index c54a360a7..bcd86a033 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -649,12 +649,12 @@ def supports_cast(device, dtype): #TODO
         return True
     if dtype == torch.float16:
         return True
-    if is_device_mps(device):
-        return False
     if directml_enabled: #TODO: test this
         return False
     if dtype == torch.bfloat16:
         return True
+    if is_device_mps(device):
+        return False
     if dtype == torch.float8_e4m3fn:
         return True
     if dtype == torch.float8_e5m2:
@@ -876,9 +876,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
         if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow
             return False
 
-    if device is not None: #TODO not sure about mps bf16 support
+    if device is not None:
         if is_device_mps(device):
-            return False
+            return True
 
     if FORCE_FP32:
         return False

From 1589b58d3e29e44623a1f3f595917b98f2301c3e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 04:03:59 -0400
Subject: [PATCH 261/315] Basic Flux Schnell and Flux Dev model implementation.

---
 comfy/latent_formats.py     |  11 ++
 comfy/ldm/flux/layers.py    | 257 ++++++++++++++++++++++++++++++++++++
 comfy/ldm/flux/math.py      |  29 ++++
 comfy/ldm/flux/model.py     | 136 +++++++++++++++++++
 comfy/model_base.py         |  21 +++
 comfy/model_detection.py    |  17 +++
 comfy/model_sampling.py     |  40 ++++++
 comfy/sd.py                 |   5 +
 comfy/supported_models.py   |  41 +++++-
 comfy/text_encoders/flux.py |  64 +++++++++
 folder_paths.py             |   2 +-
 nodes.py                    |   4 +-
 12 files changed, 624 insertions(+), 3 deletions(-)
 create mode 100644 comfy/ldm/flux/layers.py
 create mode 100644 comfy/ldm/flux/math.py
 create mode 100644 comfy/ldm/flux/model.py
 create mode 100644 comfy/text_encoders/flux.py

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 4b4a9eda2..34c7bb3da 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -139,3 +139,14 @@ class SD3(LatentFormat):
 
 class StableAudio1(LatentFormat):
     latent_channels = 64
+
+class Flux(SD3):
+    def __init__(self):
+        self.scale_factor = 0.3611
+        self.shift_factor = 0.1159
+
+    def process_in(self, latent):
+        return (latent - self.shift_factor) * self.scale_factor
+
+    def process_out(self, latent):
+        return (latent / self.scale_factor) + self.shift_factor
diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
new file mode 100644
index 000000000..bb5e02b6d
--- /dev/null
+++ b/comfy/ldm/flux/layers.py
@@ -0,0 +1,257 @@
+import math
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+from .math import attention, rope
+import comfy.ops
+
+
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: Tensor) -> Tensor:
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+            dim=-3,
+        )
+
+        return emb.unsqueeze(1)
+
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+    """
+    Create sinusoidal timestep embeddings.
+    :param t: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an (N, D) Tensor of positional embeddings.
+    """
+    t = time_factor * t
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
+        t.device
+    )
+
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    if torch.is_floating_point(t):
+        embedding = embedding.to(t)
+    return embedding
+
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+        self.silu = nn.SiLU()
+        self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))
+
+    def forward(self, x: Tensor):
+        x_dtype = x.dtype
+        x = x.float()
+        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
+        return (x * rrms).to(dtype=x_dtype) * comfy.ops.cast_to(self.scale, dtype=x_dtype, device=x.device)
+
+
+class QKNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+        self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+        q = self.query_norm(q)
+        k = self.key_norm(k)
+        return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+
+    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
+        qkv = self.qkv(x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+        x = attention(q, k, v, pe=pe)
+        x = self.proj(x)
+        return x
+
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate: Tensor
+
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.is_double = double
+        self.multiplier = 6 if double else 3
+        self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
+
+    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
+
+        return (
+            ModulationOut(*out[:3]),
+            ModulationOut(*out[3:]) if self.is_double else None,
+        )
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+        self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
+        img_mod1, img_mod2 = self.img_mod(vec)
+        txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+        # prepare image for attention
+        img_modulated = self.img_norm1(img)
+        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_qkv = self.img_attn.qkv(img_modulated)
+        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+        # prepare txt for attention
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_qkv = self.txt_attn.qkv(txt_modulated)
+        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        # run actual attention
+        q = torch.cat((txt_q, img_q), dim=2)
+        k = torch.cat((txt_k, img_k), dim=2)
+        v = torch.cat((txt_v, img_v), dim=2)
+
+        attn = attention(q, k, v, pe=pe)
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+        # calculate the img bloks
+        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+
+        # calculate the txt bloks
+        txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
+        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: float | None = None,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
+        # proj and mlp_out
+        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
+
+        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+        self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+        mod, _ = self.modulation(vec)
+        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        return x + mod.gate * output
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x
diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
new file mode 100644
index 000000000..e4ef624ef
--- /dev/null
+++ b/comfy/ldm/flux/math.py
@@ -0,0 +1,29 @@
+import torch
+from einops import rearrange
+from torch import Tensor
+from comfy.ldm.modules.attention import optimized_attention
+
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
+    q, k = apply_rope(q, k, pe)
+
+    heads = q.shape[1]
+    x = optimized_attention(q, k, v, heads, skip_reshape=True)
+    return x
+
+
+def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
+    assert dim % 2 == 0
+    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+    omega = 1.0 / (theta**scale)
+    out = torch.einsum("...n,d->...nd", pos, omega)
+    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+    return out.float()
+
+
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
new file mode 100644
index 000000000..f77834c15
--- /dev/null
+++ b/comfy/ldm/flux/model.py
@@ -0,0 +1,136 @@
+#Original code can be found on: https://github.com/black-forest-labs/flux
+
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+
+from .layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    LastLayer,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+
+from einops import rearrange, repeat
+
+@dataclass
+class FluxParams:
+    in_channels: int
+    vec_in_dim: int
+    context_in_dim: int
+    hidden_size: int
+    mlp_ratio: float
+    num_heads: int
+    depth: int
+    depth_single_blocks: int
+    axes_dim: list[int]
+    theta: int
+    qkv_bias: bool
+    guidance_embed: bool
+
+
+class Flux(nn.Module):
+    """
+    Transformer model for flow matching on sequences.
+    """
+
+    def __init__(self, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+        params = FluxParams(**kwargs)
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(
+                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
+            )
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
+        )
+        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
+
+    def forward_orig(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor | None = None,
+    ) -> Tensor:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+        vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
+
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+
+        img = torch.cat((txt, img), 1)
+        for block in self.single_blocks:
+            img = block(img, vec=vec, pe=pe)
+        img = img[:, txt.shape[1] :, ...]
+
+        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
+        return img
+
+    def forward(self, x, timestep, context, y, guidance, **kwargs):
+        bs, c, h, w = x.shape
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+
+        img_ids = torch.zeros((h // 2, w // 2, 3), device=x.device, dtype=x.dtype)
+        img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=x.device, dtype=x.dtype)[None, :]
+        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance)
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h // 2, w=w // 2, ph=2, pw=2)
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 9d60c1fb4..7c7b4c3ff 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -10,6 +10,8 @@ import comfy.ldm.aura.mmdit
 import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
+import comfy.ldm.flux.model
+
 import comfy.model_management
 import comfy.conds
 import comfy.ops
@@ -26,6 +28,7 @@ class ModelType(Enum):
     EDM = 5
     FLOW = 6
     V_PREDICTION_CONTINUOUS = 7
+    FLUX = 8
 
 
 from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
@@ -53,6 +56,9 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
         c = V_PREDICTION
         s = ModelSamplingContinuousV
+    elif model_type == ModelType.FLUX:
+        c = comfy.model_sampling.CONST
+        s = comfy.model_sampling.ModelSamplingFlux
 
     class ModelSampling(s, c):
         pass
@@ -681,3 +687,18 @@ class HunyuanDiT(BaseModel):
 
         out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
         return out
+
+class Flux(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux)
+
+    def encode_adm(self, **kwargs):
+        return kwargs["pooled_output"]
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+        return out
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index ea4959370..dda9797b7 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -128,6 +128,23 @@ def detect_unet_config(state_dict, key_prefix):
             unet_config["image_model"] = "hydit1"
         return unet_config
 
+    if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux
+        dit_config = {}
+        dit_config["image_model"] = "flux"
+        dit_config["in_channels"] = 64
+        dit_config["vec_in_dim"] = 768
+        dit_config["context_in_dim"] = 4096
+        dit_config["hidden_size"] = 3072
+        dit_config["mlp_ratio"] = 4.0
+        dit_config["num_heads"] = 24
+        dit_config["depth"] = 19
+        dit_config["depth_single_blocks"] = 38
+        dit_config["axes_dim"] = [16, 56, 56]
+        dit_config["theta"] = 10000
+        dit_config["qkv_bias"] = True
+        dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
+        return dit_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index 25bb7e043..4a0f2db60 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -272,3 +272,43 @@ class StableCascadeSampling(ModelSamplingDiscrete):
 
         percent = 1.0 - percent
         return self.sigma(torch.tensor(percent))
+
+
+def flux_time_shift(mu: float, sigma: float, t):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+class ModelSamplingFlux(torch.nn.Module):
+    def __init__(self, model_config=None):
+        super().__init__()
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(shift=sampling_settings.get("shift", 1.15))
+
+    def set_parameters(self, shift=1.15, timesteps=10000):
+        self.shift = shift
+        ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps))
+        self.register_buffer('sigmas', ts)
+
+    @property
+    def sigma_min(self):
+        return self.sigmas[0]
+
+    @property
+    def sigma_max(self):
+        return self.sigmas[-1]
+
+    def timestep(self, sigma):
+        return sigma
+
+    def sigma(self, timestep):
+        return flux_time_shift(self.shift, 1.0, timestep)
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 1.0
+        if percent >= 1.0:
+            return 0.0
+        return 1.0 - percent
diff --git a/comfy/sd.py b/comfy/sd.py
index 8bf8d1087..c9bc16397 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -23,6 +23,7 @@ import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
 
 import comfy.model_patcher
 import comfy.lora
@@ -387,6 +388,7 @@ class CLIPType(Enum):
     SD3 = 3
     STABLE_AUDIO = 4
     HUNYUAN_DIT = 5
+    FLUX = 6
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
@@ -438,6 +440,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
         elif clip_type == CLIPType.HUNYUAN_DIT:
             clip_target.clip = comfy.text_encoders.hydit.HyditModel
             clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
+        elif clip_type == CLIPType.FLUX:
+            clip_target.clip = comfy.text_encoders.flux.FluxClipModel
+            clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index ddd0c173b..43e8f5d1b 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -9,6 +9,7 @@ import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
 
 from . import supported_models_base
 from . import latent_formats
@@ -619,7 +620,45 @@ class HunyuanDiT1(HunyuanDiT):
         "linear_end" : 0.03,
     }
 
+class Flux(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "flux",
+        "guidance_embed": True,
+    }
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1]
+    sampling_settings = {
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Flux(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.FluxClipModel)
+
+class FluxSchnell(Flux):
+    unet_config = {
+        "image_model": "flux",
+        "guidance_embed": False,
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 1.0,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
+        return out
+
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell]
 
 models += [SVD_img2vid]
diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
new file mode 100644
index 000000000..2759a38a8
--- /dev/null
+++ b/comfy/text_encoders/flux.py
@@ -0,0 +1,64 @@
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+from transformers import T5TokenizerFast
+import torch
+import os
+
+class T5XXLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5)
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+
+
+class FluxTokenizer:
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        out = {}
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.clip_g.untokenize(token_weight_pair)
+
+    def state_dict(self):
+        return {}
+
+
+class FluxClipModel(torch.nn.Module):
+    def __init__(self, device="cpu", dtype=None):
+        super().__init__()
+        self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False)
+        self.t5xxl = T5XXLModel(device=device, dtype=dtype)
+        self.dtypes = set([dtype])
+
+    def set_clip_options(self, options):
+        self.clip_l.set_clip_options(options)
+        self.t5xxl.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.clip_l.reset_clip_options()
+        self.t5xxl.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_l = token_weight_pairs["l"]
+        token_weight_pars_t5 = token_weight_pairs["t5xxl"]
+
+        t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
+        l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+        return t5_out, l_pooled
+
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+            return self.clip_l.load_sd(sd)
+        else:
+            return self.t5xxl.load_sd(sd)
+
diff --git a/folder_paths.py b/folder_paths.py
index 2baf8ce1c..71faa2df4 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -3,7 +3,7 @@ import time
 import logging
 from typing import Set, List, Dict, Tuple
 
-supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
+supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'])
 
 SupportedFileExtensionsType = Set[str]
 ScanPathType = List[str]
diff --git a/nodes.py b/nodes.py
index dc66dd3ef..93d24ae52 100644
--- a/nodes.py
+++ b/nodes.py
@@ -859,7 +859,7 @@ class DualCLIPLoader:
     def INPUT_TYPES(s):
         return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
                               "clip_name2": (folder_paths.get_filename_list("clip"), ),
-                              "type": (["sdxl", "sd3"], ),
+                              "type": (["sdxl", "sd3", "flux"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -873,6 +873,8 @@ class DualCLIPLoader:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         elif type == "sd3":
             clip_type = comfy.sd.CLIPType.SD3
+        elif type == "flux":
+            clip_type = comfy.sd.CLIPType.FLUX
 
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)

From 8d34211a7abd06d659279cfa2d9d3d0cada75f58 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 09:57:01 -0400
Subject: [PATCH 262/315] Fix old python versions no longer working.

---
 comfy/ldm/flux/layers.py | 11 +++++------
 comfy/ldm/flux/math.py   |  2 +-
 comfy/ldm/flux/model.py  |  4 ++--
 3 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
index bb5e02b6d..99f498106 100644
--- a/comfy/ldm/flux/layers.py
+++ b/comfy/ldm/flux/layers.py
@@ -8,9 +8,8 @@ from torch import Tensor, nn
 from .math import attention, rope
 import comfy.ops
 
-
 class EmbedND(nn.Module):
-    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+    def __init__(self, dim: int, theta: int, axes_dim: list):
         super().__init__()
         self.dim = dim
         self.theta = theta
@@ -79,7 +78,7 @@ class QKNorm(torch.nn.Module):
         self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
         self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
 
-    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
         q = self.query_norm(q)
         k = self.key_norm(k)
         return q.to(v), k.to(v)
@@ -118,7 +117,7 @@ class Modulation(nn.Module):
         self.multiplier = 6 if double else 3
         self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
 
-    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+    def forward(self, vec: Tensor) -> tuple:
         out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
 
         return (
@@ -156,7 +155,7 @@ class DoubleStreamBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor):
         img_mod1, img_mod2 = self.img_mod(vec)
         txt_mod1, txt_mod2 = self.txt_mod(vec)
 
@@ -203,7 +202,7 @@ class SingleStreamBlock(nn.Module):
         hidden_size: int,
         num_heads: int,
         mlp_ratio: float = 4.0,
-        qk_scale: float | None = None,
+        qk_scale: float = None,
         dtype=None,
         device=None,
         operations=None
diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index e4ef624ef..d9bb568ac 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -21,7 +21,7 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     return out.float()
 
 
-def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
     xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
     xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
     xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index f77834c15..ae34052df 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -26,7 +26,7 @@ class FluxParams:
     num_heads: int
     depth: int
     depth_single_blocks: int
-    axes_dim: list[int]
+    axes_dim: list
     theta: int
     qkv_bias: bool
     guidance_embed: bool
@@ -92,7 +92,7 @@ class Flux(nn.Module):
         txt_ids: Tensor,
         timesteps: Tensor,
         y: Tensor,
-        guidance: Tensor | None = None,
+        guidance: Tensor = None,
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")

From 5f98de7697de9aed79561a3f764c0e7d9766f8f1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 11:05:56 -0400
Subject: [PATCH 263/315] Load flux t5 in fp8 if weights are in fp8.

---
 comfy/model_management.py       | 11 +++++++++++
 comfy/sd.py                     |  8 +++++++-
 comfy/text_encoders/flux.py     | 13 ++++++++++---
 comfy/text_encoders/sd3_clip.py |  9 +--------
 4 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index bcd86a033..07c137270 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -661,6 +661,17 @@ def supports_cast(device, dtype): #TODO
         return True
     return False
 
+def pick_weight_dtype(dtype, fallback_dtype, device=None):
+    if dtype is None:
+        dtype = fallback_dtype
+    elif dtype_size(dtype) > dtype_size(fallback_dtype):
+        dtype = fallback_dtype
+
+    if not supports_cast(device, dtype):
+        dtype = fallback_dtype
+
+    return dtype
+
 def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
diff --git a/comfy/sd.py b/comfy/sd.py
index c9bc16397..2be8edef2 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -441,7 +441,13 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             clip_target.clip = comfy.text_encoders.hydit.HyditModel
             clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
         elif clip_type == CLIPType.FLUX:
-            clip_target.clip = comfy.text_encoders.flux.FluxClipModel
+            weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+            weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None))
+            dtype_t5 = None
+            if weight is not None:
+                dtype_t5 = weight.dtype
+
+            clip_target.clip = comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5)
             clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
index 2759a38a8..849214ce0 100644
--- a/comfy/text_encoders/flux.py
+++ b/comfy/text_encoders/flux.py
@@ -1,5 +1,6 @@
 from comfy import sd1_clip
 import comfy.text_encoders.t5
+import comfy.model_management
 from transformers import T5TokenizerFast
 import torch
 import os
@@ -34,11 +35,12 @@ class FluxTokenizer:
 
 
 class FluxClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None):
+    def __init__(self, dtype_t5=None, device="cpu", dtype=None):
         super().__init__()
+        dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
         self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False)
-        self.t5xxl = T5XXLModel(device=device, dtype=dtype)
-        self.dtypes = set([dtype])
+        self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
+        self.dtypes = set([dtype, dtype_t5])
 
     def set_clip_options(self, options):
         self.clip_l.set_clip_options(options)
@@ -62,3 +64,8 @@ class FluxClipModel(torch.nn.Module):
         else:
             return self.t5xxl.load_sd(sd)
 
+def flux_clip(dtype_t5=None):
+    class FluxClipModel_(FluxClipModel):
+        def __init__(self, device="cpu", dtype=None):
+            super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype)
+    return FluxClipModel_
diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
index b01fad221..143d884cb 100644
--- a/comfy/text_encoders/sd3_clip.py
+++ b/comfy/text_encoders/sd3_clip.py
@@ -54,14 +54,7 @@ class SD3ClipModel(torch.nn.Module):
             self.clip_g = None
 
         if t5:
-            if dtype_t5 is None:
-                dtype_t5 = dtype
-            elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype):
-                dtype_t5 = dtype
-
-            if not comfy.model_management.supports_cast(device, dtype_t5):
-                dtype_t5 = dtype
-
+            dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
             self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
             self.dtypes.add(dtype_t5)
         else:

From eb96c3bd82ba7eda5cac343ea2465baf6715b6d0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 11:32:58 -0400
Subject: [PATCH 264/315] Fix .sft file loading (they are safetensors files).

---
 comfy/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/utils.py b/comfy/utils.py
index 1e4b5ef88..0db9fbb62 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -11,7 +11,7 @@ import itertools
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
         device = torch.device("cpu")
-    if ckpt.lower().endswith(".safetensors"):
+    if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
         sd = safetensors.torch.load_file(ckpt, device=device.type)
     else:
         if safe_load:

From 2f88d19ef311dc078a89ef39c1f9bc9265d6435d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 11:48:19 -0400
Subject: [PATCH 265/315] Add link to Flux examples to readme.

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 6a68f268a..a542ed4d6 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Smart memory management: can automatically run models on GPUs with as low as 1GB vram.

From 1aa9cf3292499303260533780d25bbff99e076c8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 12:11:57 -0400
Subject: [PATCH 266/315] Make lowvram more aggressive on low memory machines.

---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 07c137270..de4bd4426 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -318,7 +318,7 @@ class LoadedModel:
         return self.model is other.model
 
 def minimum_inference_memory():
-    return (1024 * 1024 * 1024)
+    return (1024 * 1024 * 1024) * 1.2
 
 def unload_model_clones(model, unload_weights_only=True, force_unload=True):
     to_unload = []

From f2b80f95d2a3384609c1ffdec08457c4724d1d20 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 12:55:28 -0400
Subject: [PATCH 267/315] Better Mac support on flux model.

---
 comfy/ldm/flux/math.py  |  4 ++--
 comfy/ldm/flux/model.py | 10 ++++++----
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index d9bb568ac..7cf87947a 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -13,9 +13,9 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=pos.device)
     omega = 1.0 / (theta**scale)
-    out = torch.einsum("...n,d->...nd", pos, omega)
+    out = torch.einsum("...n,d->...nd", pos.float(), omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
     return out.float()
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index ae34052df..21f1a877e 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -126,11 +126,13 @@ class Flux(nn.Module):
         bs, c, h, w = x.shape
         img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
 
-        img_ids = torch.zeros((h // 2, w // 2, 3), device=x.device, dtype=x.dtype)
-        img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=x.device, dtype=x.dtype)[:, None]
-        img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=x.device, dtype=x.dtype)[None, :]
+        h_len = (h // 2)
+        w_len = (w // 2)
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
         img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance)
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h // 2, w=w // 2, ph=2, pw=2)
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)

From d7430a1651a300e8230867ce3e6d86cc0101facc Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 13:28:41 -0400
Subject: [PATCH 268/315] Add a way to load the diffusion model in fp8 with
 UNETLoader node.

---
 comfy/sd.py | 13 ++++++++-----
 nodes.py    |  6 ++++--
 2 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 2be8edef2..41ce18c80 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -567,7 +567,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_unet_state_dict(sd): #load unet in diffusers or regular format
+def load_unet_state_dict(sd, dtype=None): #load unet in diffusers or regular format
 
     #Allow loading unets from checkpoint files
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
@@ -576,7 +576,6 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
         sd = temp_sd
 
     parameters = comfy.utils.calculate_parameters(sd)
-    unet_dtype = model_management.unet_dtype(model_params=parameters)
     load_device = model_management.get_torch_device()
     model_config = model_detection.model_config_from_unet(sd, "")
 
@@ -603,7 +602,11 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
                     logging.warning("{} {}".format(diffusers_keys[k], k))
 
     offload_device = model_management.unet_offload_device()
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    if dtype is None:
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    else:
+        unet_dtype = dtype
+
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
     model = model_config.get_model(new_sd, "")
@@ -614,9 +617,9 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
         logging.info("left over keys in unet: {}".format(left_over))
     return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
-def load_unet(unet_path):
+def load_unet(unet_path, dtype=None):
     sd = comfy.utils.load_torch_file(unet_path)
-    model = load_unet_state_dict(sd)
+    model = load_unet_state_dict(sd, dtype=dtype)
     if model is None:
         logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
diff --git a/nodes.py b/nodes.py
index 93d24ae52..fbd0c6ce0 100644
--- a/nodes.py
+++ b/nodes.py
@@ -818,15 +818,17 @@ class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ),
+                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e5m2"],)
                              }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"
 
     CATEGORY = "advanced/loaders"
 
-    def load_unet(self, unet_name):
+    def load_unet(self, unet_name, weight_dtype):
+        weight_dtype = {"default":None, "fp8_e4m3fn":torch.float8_e4m3fn, "fp8_e5m2":torch.float8_e4m3fn}[weight_dtype]
         unet_path = folder_paths.get_full_path("unet", unet_name)
-        model = comfy.sd.load_unet(unet_path)
+        model = comfy.sd.load_unet(unet_path, dtype=weight_dtype)
         return (model,)
 
 class CLIPLoader:

From b4f6ebb2e88a43876caa1d0b2b8eb1e99ac57adb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 13:33:30 -0400
Subject: [PATCH 269/315] Rename UNETLoader node to "Load Diffusion Model".

---
 nodes.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/nodes.py b/nodes.py
index fbd0c6ce0..5b6321b69 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1847,6 +1847,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "StyleModelLoader": "Load Style Model",
     "CLIPVisionLoader": "Load CLIP Vision",
     "UpscaleModelLoader": "Load Upscale Model",
+    "UNETLoader": "Load Diffusion Model",
     # Conditioning
     "CLIPVisionEncode": "CLIP Vision Encode",
     "StyleModelApply": "Apply Style Model",

From 48eb1399c02bdae7e14b2208c448b69b382d0090 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 13:41:27 -0400
Subject: [PATCH 270/315] Try to fix mac issue.

---
 comfy/ldm/flux/math.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index 7cf87947a..88c2b6bb4 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -2,6 +2,7 @@ import torch
 from einops import rearrange
 from torch import Tensor
 from comfy.ldm.modules.attention import optimized_attention
+import comfy.model_management
 
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
     q, k = apply_rope(q, k, pe)
@@ -13,12 +14,17 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=pos.device)
+    if comfy.model_management.is_device_mps(pos.device):
+        device = torch.device("cpu")
+    else:
+        device = pos.device
+
+    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
     omega = 1.0 / (theta**scale)
-    out = torch.einsum("...n,d->...nd", pos.float(), omega)
+    out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
-    return out.float()
+    return out.to(dtype=torch.float32, device=pos.device)
 
 
 def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):

From a6decf1e620907347c9c5d8c815172f349b19c21 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 16:18:14 -0400
Subject: [PATCH 271/315] Fix bfloat16 potentially not being enabled on mps.

---
 comfy/model_management.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index de4bd4426..b4f32d648 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -897,7 +897,10 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     if directml_enabled:
         return False
 
-    if cpu_mode() or mps_mode():
+    if mps_mode():
+        return True
+
+    if cpu_mode():
         return False
 
     if is_intel_xpu():

From 1c61361fd2478068e69816e78a0689db6664b65d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 16:28:11 -0400
Subject: [PATCH 272/315] Fast preview support for Flux.

---
 comfy/latent_formats.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 34c7bb3da..ecb03b010 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -144,6 +144,24 @@ class Flux(SD3):
     def __init__(self):
         self.scale_factor = 0.3611
         self.shift_factor = 0.1159
+        self.latent_rgb_factors =[
+            [-0.0404,  0.0159,  0.0609],
+            [ 0.0043,  0.0298,  0.0850],
+            [ 0.0328, -0.0749, -0.0503],
+            [-0.0245,  0.0085,  0.0549],
+            [ 0.0966,  0.0894,  0.0530],
+            [ 0.0035,  0.0399,  0.0123],
+            [ 0.0583,  0.1184,  0.1262],
+            [-0.0191, -0.0206, -0.0306],
+            [-0.0324,  0.0055,  0.1001],
+            [ 0.0955,  0.0659, -0.0545],
+            [-0.0504,  0.0231, -0.0013],
+            [ 0.0500, -0.0008, -0.0088],
+            [ 0.0982,  0.0941,  0.0976],
+            [-0.1233, -0.0280, -0.0897],
+            [-0.0005, -0.0530, -0.0020],
+            [-0.1273, -0.0932, -0.0680]
+        ]
 
     def process_in(self, latent):
         return (latent - self.shift_factor) * self.scale_factor

From d965474aaae2f1b461e0925a7e1519b740393994 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 16:39:59 -0400
Subject: [PATCH 273/315] Make ComfyUI split batches a higher priority than
 weight offload.

---
 comfy/model_management.py | 10 +++++++---
 comfy/sampler_helpers.py  |  4 +++-
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index b4f32d648..da0b989a8 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -379,11 +379,15 @@ def free_memory(memory_required, device, keep_loaded=[]):
             if mem_free_torch > mem_free_total * 0.25:
                 soft_empty_cache()
 
-def load_models_gpu(models, memory_required=0, force_patch_weights=False):
+def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None):
     global vram_state
 
     inference_memory = minimum_inference_memory()
     extra_mem = max(inference_memory, memory_required)
+    if minimum_memory_required is None:
+        minimum_memory_required = extra_mem
+    else:
+        minimum_memory_required = max(inference_memory, minimum_memory_required)
 
     models = set(models)
 
@@ -446,8 +450,8 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - extra_mem)))
-            if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary
+            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required)))
+            if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 
         if vram_set_state == VRAMState.NO_VRAM:
diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py
index a18abd9e9..4a2ec123b 100644
--- a/comfy/sampler_helpers.py
+++ b/comfy/sampler_helpers.py
@@ -61,7 +61,9 @@ def prepare_sampling(model, noise_shape, conds):
     device = model.load_device
     real_model = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
-    comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)
+    memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory
+    minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory
+    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required)
     real_model = model.model
 
     return real_model, conds, models

From d420bc792af0b61a6ef7410c65fa2d4dcc646c56 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 17:49:46 -0400
Subject: [PATCH 274/315] Tweak the memory usage formulas for Flux and SD.

---
 comfy/model_base.py | 14 +++++++++++++-
 1 file changed, 13 insertions(+), 1 deletion(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index 7c7b4c3ff..994b414cc 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -256,7 +256,7 @@ class BaseModel(torch.nn.Module):
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
             area = input_shape[0] * math.prod(input_shape[2:])
-            return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
+            return (area * 0.3) * (1024 * 1024)
 
 
 def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
@@ -702,3 +702,15 @@ class Flux(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
         return out
+
+    def memory_required(self, input_shape):
+        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
+            dtype = self.get_dtype()
+            if self.manual_cast_dtype is not None:
+                dtype = self.manual_cast_dtype
+            #TODO: this probably needs to be tweaked
+            area = input_shape[0] * input_shape[2] * input_shape[3]
+            return (area * comfy.model_management.dtype_size(dtype) * 0.020) * (1024 * 1024)
+        else:
+            area = input_shape[0] * input_shape[2] * input_shape[3]
+            return (area * 0.3) * (1024 * 1024)

From a531001cc772305364a319a760fcd5034e28411a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 18:53:25 -0400
Subject: [PATCH 275/315] Add CLIPTextEncodeFlux.

---
 comfy_extras/nodes_flux.py | 27 +++++++++++++++++++++++++++
 nodes.py                   |  1 +
 2 files changed, 28 insertions(+)
 create mode 100644 comfy_extras/nodes_flux.py

diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py
new file mode 100644
index 000000000..6c1e8b0e0
--- /dev/null
+++ b/comfy_extras/nodes_flux.py
@@ -0,0 +1,27 @@
+
+class CLIPTextEncodeFlux:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "clip": ("CLIP", ),
+            "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
+            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning"
+
+    def encode(self, clip, clip_l, t5xxl, guidance):
+        tokens = clip.tokenize(clip_l)
+        tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+
+        output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
+        cond = output.pop("cond")
+        output["guidance"] = guidance
+        return ([[cond, output]], )
+
+NODE_CLASS_MAPPINGS = {
+    "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
+}
diff --git a/nodes.py b/nodes.py
index 5b6321b69..1994f1190 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2043,6 +2043,7 @@ def init_builtin_extra_nodes():
         "nodes_gits.py",
         "nodes_controlnet.py",
         "nodes_hunyuan.py",
+        "nodes_flux.py",
     ]
 
     import_failed = []

From e638f2858a93ea3b94edc2938b213ebc1fcf4e20 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 21:03:26 -0400
Subject: [PATCH 276/315] Hack to make all resolutions work on Flux models.

---
 comfy/ldm/flux/model.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index 21f1a877e..e7931c16d 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -124,10 +124,16 @@ class Flux(nn.Module):
 
     def forward(self, x, timestep, context, y, guidance, **kwargs):
         bs, c, h, w = x.shape
-        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+        patch_size = 2
+        pad_h = (patch_size - h % 2) % patch_size
+        pad_w = (patch_size - w % 2) % patch_size
 
-        h_len = (h // 2)
-        w_len = (w // 2)
+        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='circular')
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+        h_len = ((h + (patch_size // 2)) // patch_size)
+        w_len = ((w + (patch_size // 2)) // patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
         img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
@@ -135,4 +141,4 @@ class Flux(nn.Module):
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance)
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]

From ce9ac2fe0581288d4a24869dae2e04a3c2b67061 Mon Sep 17 00:00:00 2001
From: Alexander Brown <DrJKL0424@gmail.com>
Date: Thu, 1 Aug 2024 18:40:56 -0700
Subject: [PATCH 277/315] Fix clip_g/clip_l mixup (#4168)

---
 comfy/text_encoders/flux.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
index 849214ce0..0590741bb 100644
--- a/comfy/text_encoders/flux.py
+++ b/comfy/text_encoders/flux.py
@@ -28,7 +28,7 @@ class FluxTokenizer:
         return out
 
     def untokenize(self, token_weight_pair):
-        return self.clip_g.untokenize(token_weight_pair)
+        return self.clip_l.untokenize(token_weight_pair)
 
     def state_dict(self):
         return {}

From 369f459b2058f793c1230472f04edc9fd9471b46 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 22:19:53 -0400
Subject: [PATCH 278/315] Fix no longer working on old pytorch.

---
 nodes.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/nodes.py b/nodes.py
index 1994f1190..e296597c5 100644
--- a/nodes.py
+++ b/nodes.py
@@ -826,9 +826,14 @@ class UNETLoader:
     CATEGORY = "advanced/loaders"
 
     def load_unet(self, unet_name, weight_dtype):
-        weight_dtype = {"default":None, "fp8_e4m3fn":torch.float8_e4m3fn, "fp8_e5m2":torch.float8_e4m3fn}[weight_dtype]
+        dtype = None
+        if weight_dtype == "fp8_e4m3fn":
+            dtype = torch.float8_e4m3fn
+        elif weight_dtype == "fp8_e5m2":
+            dtype = torch.float8_e5m2
+
         unet_path = folder_paths.get_full_path("unet", unet_name)
-        model = comfy.sd.load_unet(unet_path, dtype=weight_dtype)
+        model = comfy.sd.load_unet(unet_path, dtype=dtype)
         return (model,)
 
 class CLIPLoader:

From c1696cd1b5f572d7694dde223764861255d2398b Mon Sep 17 00:00:00 2001
From: Jairo Correa <jn.j41r0@gmail.com>
Date: Fri, 2 Aug 2024 10:34:12 -0300
Subject: [PATCH 279/315] Add missing import (#4174)

---
 comfy_extras/nodes_audio.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
index 6f0e26365..762b48279 100644
--- a/comfy_extras/nodes_audio.py
+++ b/comfy_extras/nodes_audio.py
@@ -7,6 +7,7 @@ import io
 import json
 import struct
 import random
+import hashlib
 from comfy.cli_args import args
 
 class EmptyLatentAudio:

From eca962c6dae395cab1258456529030880c188734 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 2 Aug 2024 10:24:53 -0400
Subject: [PATCH 280/315] Add FluxGuidance node.

This lets you adjust the guidance on the dev model which is a parameter
that is passed to the diffusion model.
---
 comfy_extras/nodes_flux.py | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py
index 6c1e8b0e0..b690432b5 100644
--- a/comfy_extras/nodes_flux.py
+++ b/comfy_extras/nodes_flux.py
@@ -1,3 +1,4 @@
+import node_helpers
 
 class CLIPTextEncodeFlux:
     @classmethod
@@ -11,7 +12,7 @@ class CLIPTextEncodeFlux:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "encode"
 
-    CATEGORY = "advanced/conditioning"
+    CATEGORY = "advanced/conditioning/flux"
 
     def encode(self, clip, clip_l, t5xxl, guidance):
         tokens = clip.tokenize(clip_l)
@@ -22,6 +23,25 @@ class CLIPTextEncodeFlux:
         output["guidance"] = guidance
         return ([[cond, output]], )
 
+class FluxGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "conditioning": ("CONDITIONING", ),
+            "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
+            }}
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "advanced/conditioning/flux"
+
+    def append(self, conditioning, guidance):
+        c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
+        return (c, )
+
+
 NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
+    "FluxGuidance": FluxGuidance,
 }

From bfb52de866ce659c281a6c243c8485109e4d8b8b Mon Sep 17 00:00:00 2001
From: fgdfgfthgr-fox <60460773+fgdfgfthgr-fox@users.noreply.github.com>
Date: Sat, 3 Aug 2024 02:29:03 +1200
Subject: [PATCH 281/315] Lower SAG scale step for finer control (#4158)

* Lower SAG step for finer control

Since the introduction of cfg++ which uses very low cfg value, a step of 0.1 in SAG might be too high for finer control. Even SAG of 0.1 can be too high when cfg is only 0.6, so I change the step to 0.01.

* Lower PAG step as well.

* Update nodes_sag.py
---
 comfy_extras/nodes_pag.py | 2 +-
 comfy_extras/nodes_sag.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py
index aec78bd8a..eb28196f4 100644
--- a/comfy_extras/nodes_pag.py
+++ b/comfy_extras/nodes_pag.py
@@ -12,7 +12,7 @@ class PerturbedAttentionGuidance:
         return {
             "required": {
                 "model": ("MODEL",),
-                "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+                "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.01}),
             }
         }
 
diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py
index 010e99744..5e15b99e5 100644
--- a/comfy_extras/nodes_sag.py
+++ b/comfy_extras/nodes_sag.py
@@ -96,7 +96,7 @@ class SelfAttentionGuidance:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                             "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.1}),
+                             "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.01}),
                              "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}),
                               }}
     RETURN_TYPES = ("MODEL",)

From 17bbd83176268c76a8597bb3a88768d325536651 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 2 Aug 2024 13:14:28 -0400
Subject: [PATCH 282/315] Fix bug loading flac workflow when it contains =
 character.

---
 web/scripts/pnginfo.js | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js
index 8b1b2c61c..4477ed7a1 100644
--- a/web/scripts/pnginfo.js
+++ b/web/scripts/pnginfo.js
@@ -190,9 +190,10 @@ function parseVorbisComment(dataView) {
 		const comment = getString(dataView, offset, commentLength);
 		offset += commentLength;
 
-		const [key, value] = comment.split('=');
+		const ind = comment.indexOf('=')
+		const key = comment.substring(0, ind);
 
-		comments[key] = value;
+		comments[key] = comment.substring(ind+1);
 	}
 
 	return comments;

From 47da42d9283815a58636bd6b42c0434f70b24c9c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 2 Aug 2024 17:02:35 -0400
Subject: [PATCH 283/315] Better Flux vram estimation.

---
 comfy/model_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index 994b414cc..9989a4c2b 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -710,7 +710,7 @@ class Flux(BaseModel):
                 dtype = self.manual_cast_dtype
             #TODO: this probably needs to be tweaked
             area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * comfy.model_management.dtype_size(dtype) * 0.020) * (1024 * 1024)
+            return (area * comfy.model_management.dtype_size(dtype) * 0.026) * (1024 * 1024)
         else:
             area = input_shape[0] * input_shape[2] * input_shape[3]
             return (area * 0.3) * (1024 * 1024)

From 3a9ee995cfbb9425227df9aff534dea12c1af532 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 2 Aug 2024 17:34:30 -0400
Subject: [PATCH 284/315] Tweak regular SD memory formula.

---
 comfy/model_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index 9989a4c2b..eb2b935df 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -252,7 +252,7 @@ class BaseModel(torch.nn.Module):
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
             area = input_shape[0] * math.prod(input_shape[2:])
-            return (area * comfy.model_management.dtype_size(dtype) / 50) * (1024 * 1024)
+            return (area * comfy.model_management.dtype_size(dtype) * 0.01) * (1024 * 1024)
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
             area = input_shape[0] * math.prod(input_shape[2:])

From ea03c9dcd2e2b223c0eb25f24be6b3e1995e2c44 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 2 Aug 2024 18:08:21 -0400
Subject: [PATCH 285/315] Better per model memory usage estimations.

---
 comfy/model_base.py            | 29 ++++-------------------------
 comfy/supported_models.py      | 11 +++++++++++
 comfy/supported_models_base.py |  2 ++
 3 files changed, 17 insertions(+), 25 deletions(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index eb2b935df..ec15e9fcf 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -94,6 +94,7 @@ class BaseModel(torch.nn.Module):
         self.concat_keys = ()
         logging.info("model_type {}".format(model_type.name))
         logging.debug("adm {}".format(self.adm_channels))
+        self.memory_usage_factor = model_config.memory_usage_factor
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
         sigma = t
@@ -252,11 +253,11 @@ class BaseModel(torch.nn.Module):
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
             area = input_shape[0] * math.prod(input_shape[2:])
-            return (area * comfy.model_management.dtype_size(dtype) * 0.01) * (1024 * 1024)
+            return (area * comfy.model_management.dtype_size(dtype) * 0.01 * self.memory_usage_factor) * (1024 * 1024)
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
             area = input_shape[0] * math.prod(input_shape[2:])
-            return (area * 0.3) * (1024 * 1024)
+            return (area * 0.15 * self.memory_usage_factor) * (1024 * 1024)
 
 
 def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
@@ -354,6 +355,7 @@ class SDXL(BaseModel):
         flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0).repeat(clip_pooled.shape[0], 1)
         return torch.cat((clip_pooled.to(flat.device), flat), dim=1)
 
+
 class SVD_img2vid(BaseModel):
     def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):
         super().__init__(model_config, model_type, device=device)
@@ -594,17 +596,6 @@ class SD3(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out
 
-    def memory_required(self, input_shape):
-        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
-            dtype = self.get_dtype()
-            if self.manual_cast_dtype is not None:
-                dtype = self.manual_cast_dtype
-            #TODO: this probably needs to be tweaked
-            area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * comfy.model_management.dtype_size(dtype) * 0.012) * (1024 * 1024)
-        else:
-            area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * 0.3) * (1024 * 1024)
 
 class AuraFlow(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
@@ -702,15 +693,3 @@ class Flux(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
         return out
-
-    def memory_required(self, input_shape):
-        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
-            dtype = self.get_dtype()
-            if self.manual_cast_dtype is not None:
-                dtype = self.manual_cast_dtype
-            #TODO: this probably needs to be tweaked
-            area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * comfy.model_management.dtype_size(dtype) * 0.026) * (1024 * 1024)
-        else:
-            area = input_shape[0] * input_shape[2] * input_shape[3]
-            return (area * 0.3) * (1024 * 1024)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 43e8f5d1b..681ef95c9 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -31,6 +31,7 @@ class SD15(supported_models_base.BASE):
     }
 
     latent_format = latent_formats.SD15
+    memory_usage_factor = 1.0
 
     def process_clip_state_dict(self, state_dict):
         k = list(state_dict.keys())
@@ -77,6 +78,7 @@ class SD20(supported_models_base.BASE):
     }
 
     latent_format = latent_formats.SD15
+    memory_usage_factor = 1.0
 
     def model_type(self, state_dict, prefix=""):
         if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
@@ -140,6 +142,7 @@ class SDXLRefiner(supported_models_base.BASE):
     }
 
     latent_format = latent_formats.SDXL
+    memory_usage_factor = 1.0
 
     def get_model(self, state_dict, prefix="", device=None):
         return model_base.SDXLRefiner(self, device=device)
@@ -178,6 +181,8 @@ class SDXL(supported_models_base.BASE):
 
     latent_format = latent_formats.SDXL
 
+    memory_usage_factor = 0.7
+
     def model_type(self, state_dict, prefix=""):
         if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5
             self.latent_format = latent_formats.SDXL_Playground_2_5()
@@ -505,6 +510,9 @@ class SD3(supported_models_base.BASE):
 
     unet_extra_config = {}
     latent_format = latent_formats.SD3
+
+    memory_usage_factor = 1.2
+
     text_encoder_key_prefix = ["text_encoders."]
 
     def get_model(self, state_dict, prefix="", device=None):
@@ -631,6 +639,9 @@ class Flux(supported_models_base.BASE):
 
     unet_extra_config = {}
     latent_format = latent_formats.Flux
+
+    memory_usage_factor = 2.6
+
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
     vae_key_prefix = ["vae."]
diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py
index cf7cdff34..bc0a7e311 100644
--- a/comfy/supported_models_base.py
+++ b/comfy/supported_models_base.py
@@ -27,6 +27,8 @@ class BASE:
     text_encoder_key_prefix = ["cond_stage_model."]
     supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
 
+    memory_usage_factor = 2.0
+
     manual_cast_dtype = None
 
     @classmethod

From 7cd0cdfce601a52c52252ace517b9f52f6237fdb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 2 Aug 2024 23:20:30 -0400
Subject: [PATCH 286/315] Add advanced model merge node for Flux model.

---
 .../nodes_model_merging_model_specific.py     | 27 +++++++++++++++++++
 1 file changed, 27 insertions(+)

diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py
index df111bd60..9557d9b1f 100644
--- a/comfy_extras/nodes_model_merging_model_specific.py
+++ b/comfy_extras/nodes_model_merging_model_specific.py
@@ -75,9 +75,36 @@ class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
+class ModelMergeFlux1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["img_in."] = argument
+        arg_dict["time_in."] = argument
+        arg_dict["guidance_in"] = argument
+        arg_dict["vector_in."] = argument
+        arg_dict["txt_in."] = argument
+
+        for i in range(19):
+            arg_dict["double_blocks.{}.".format(i)] = argument
+
+        for i in range(38):
+            arg_dict["single_blocks.{}.".format(i)] = argument
+
+        arg_dict["final_layer."] = argument
+
+        return {"required": arg_dict}
+
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
     "ModelMergeSDXL": ModelMergeSDXL,
     "ModelMergeSD3_2B": ModelMergeSD3_2B,
+    "ModelMergeFlux1": ModelMergeFlux1,
 }

From 0eea47d58086d31695f3e8e9d7ef36c6a6986faa Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 03:54:38 -0400
Subject: [PATCH 287/315] Add ModelSamplingFlux to experiment with the shift
 value.

Default shift on Flux Schnell is 0.0
---
 comfy_extras/nodes_model_advanced.py | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)

diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 22ba9547b..fef8a4873 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -170,6 +170,33 @@ class ModelSamplingAuraFlow(ModelSamplingSD3):
     def patch_aura(self, model, shift):
         return self.patch(model, shift, multiplier=1.0)
 
+class ModelSamplingFlux:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, shift):
+        m = model.clone()
+
+        sampling_base = comfy.model_sampling.ModelSamplingFlux
+        sampling_type = comfy.model_sampling.CONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift=shift)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
+
 class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
@@ -284,5 +311,6 @@ NODE_CLASS_MAPPINGS = {
     "ModelSamplingStableCascade": ModelSamplingStableCascade,
     "ModelSamplingSD3": ModelSamplingSD3,
     "ModelSamplingAuraFlow": ModelSamplingAuraFlow,
+    "ModelSamplingFlux": ModelSamplingFlux,
     "RescaleCFG": RescaleCFG,
 }

From 63a7e8edba76b30e3c01190345126ae75c94777d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 11:53:30 -0400
Subject: [PATCH 288/315] More aggressive batch splitting.

---
 comfy/samplers.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/samplers.py b/comfy/samplers.py
index 3f7633814..ce4371d50 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -171,7 +171,7 @@ def calc_cond_batch(model, conds, x_in, timestep, model_options):
         for i in range(1, len(to_batch_temp) + 1):
             batch_amount = to_batch_temp[:len(to_batch_temp)//i]
             input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
-            if model.memory_required(input_shape) < free_memory:
+            if model.memory_required(input_shape) * 1.5 < free_memory:
                 to_batch = batch_amount
                 break
 

From f123328b826dcd122d307b75288f89ea301fa25b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 12:39:33 -0400
Subject: [PATCH 289/315] Load T5 in fp8 if it's in fp8 in the Flux checkpoint.

---
 comfy/supported_models.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 681ef95c9..94fdcc0d2 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -652,7 +652,11 @@ class Flux(supported_models_base.BASE):
         return out
 
     def clip_target(self, state_dict={}):
-        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.FluxClipModel)
+        pref = self.text_encoder_key_prefix[0]
+        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        if t5_key in state_dict:
+            dtype_t5 = state_dict[t5_key].dtype
+        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5))
 
 class FluxSchnell(Flux):
     unet_config = {

From ba9095e5bd7914c2456b2dfe939c06180e97b1ad Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 13:45:19 -0400
Subject: [PATCH 290/315] Automatically use fp8 for diffusion model weights if:

Checkpoint contains weights in fp8.

There isn't enough memory to load the diffusion model in GPU vram.
---
 comfy/model_base.py       |  1 +
 comfy/model_management.py | 22 ++++++++++++++++++++--
 comfy/sd.py               |  3 ++-
 comfy/utils.py            | 12 +++++++++++-
 4 files changed, 34 insertions(+), 4 deletions(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index ec15e9fcf..94f4d333c 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -94,6 +94,7 @@ class BaseModel(torch.nn.Module):
         self.concat_keys = ()
         logging.info("model_type {}".format(model_type.name))
         logging.debug("adm {}".format(self.adm_channels))
+        logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
         self.memory_usage_factor = model_config.memory_usage_factor
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
diff --git a/comfy/model_management.py b/comfy/model_management.py
index da0b989a8..c0fb15095 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -527,6 +527,9 @@ def unet_inital_load_device(parameters, dtype):
     else:
         return cpu_dev
 
+def maximum_vram_for_weights(device=None):
+    return (get_total_memory(device) * 0.8 - minimum_inference_memory())
+
 def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
     if args.bf16_unet:
         return torch.bfloat16
@@ -536,6 +539,21 @@ def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, tor
         return torch.float8_e4m3fn
     if args.fp8_e5m2_unet:
         return torch.float8_e5m2
+
+    fp8_dtype = None
+    try:
+        for dtype in [torch.float8_e4m3fn, torch.float8_e5m2]:
+            if dtype in supported_dtypes:
+                fp8_dtype = dtype
+                break
+    except:
+        pass
+
+    if fp8_dtype is not None:
+        free_model_memory = maximum_vram_for_weights(device)
+        if model_params * 2 > free_model_memory:
+            return fp8_dtype
+
     if should_use_fp16(device=device, model_params=model_params, manual_cast=True):
         if torch.float16 in supported_dtypes:
             return torch.float16
@@ -871,7 +889,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
             fp16_works = True
 
     if fp16_works or manual_cast:
-        free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
+        free_model_memory = maximum_vram_for_weights(device)
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True
 
@@ -920,7 +938,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     bf16_works = torch.cuda.is_bf16_supported()
 
     if bf16_works or manual_cast:
-        free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
+        free_model_memory = maximum_vram_for_weights(device)
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True
 
diff --git a/comfy/sd.py b/comfy/sd.py
index 41ce18c80..bf336c859 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -510,13 +510,14 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
 
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
     parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
+    weight_dtype = comfy.utils.weight_dtype(sd, diffusion_model_prefix)
     load_device = model_management.get_torch_device()
 
     model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix)
     if model_config is None:
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
 
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=[weight_dtype] + model_config.supported_inference_dtypes)
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
 
diff --git a/comfy/utils.py b/comfy/utils.py
index 0db9fbb62..d9fe36f91 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -40,9 +40,19 @@ def calculate_parameters(sd, prefix=""):
     params = 0
     for k in sd.keys():
         if k.startswith(prefix):
-            params += sd[k].nelement()
+            w = sd[k]
+            params += w.nelement()
     return params
 
+def weight_dtype(sd, prefix=""):
+    dtypes = {}
+    for k in sd.keys():
+        if k.startswith(prefix):
+            w = sd[k]
+            dtypes[w.dtype] = dtypes.get(w.dtype, 0) + 1
+
+    return max(dtypes, key=dtypes.get)
+
 def state_dict_key_replace(state_dict, keys_to_replace):
     for x in keys_to_replace:
         if x in state_dict:

From 1e68002b87a3fb70afc7030c1b4dc6a31fea965e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 14:50:20 -0400
Subject: [PATCH 291/315] Cap lowvram to half of free memory.

---
 comfy/model_management.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index c0fb15095..2008229f2 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -450,7 +450,8 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required)))
+            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required))
+            lowvram_model_memory = int(min(current_free_mem * 0.5, lowvram_model_memory))
             if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 

From 2ba5cc8b867bc1aabe59fdaf0a8489e65012d603 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 15:06:40 -0400
Subject: [PATCH 292/315] Fix some issues.

---
 comfy/model_management.py | 3 +--
 comfy/sd.py               | 6 +++++-
 comfy/utils.py            | 3 +++
 3 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 2008229f2..bb4bcbb21 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -450,8 +450,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required))
-            lowvram_model_memory = int(min(current_free_mem * 0.5, lowvram_model_memory))
+            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), current_free_mem * 0.5)
             if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 
diff --git a/comfy/sd.py b/comfy/sd.py
index bf336c859..fac1a487f 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -517,7 +517,11 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     if model_config is None:
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
 
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=[weight_dtype] + model_config.supported_inference_dtypes)
+    unet_weight_dtype = list(model_config.supported_inference_dtypes)
+    if weight_dtype is not None:
+        unet_weight_dtype.append(weight_dtype)
+
+    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=unet_weight_dtype)
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
 
diff --git a/comfy/utils.py b/comfy/utils.py
index d9fe36f91..06e09170a 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -51,6 +51,9 @@ def weight_dtype(sd, prefix=""):
             w = sd[k]
             dtypes[w.dtype] = dtypes.get(w.dtype, 0) + 1
 
+    if len(dtypes) == 0:
+        return None
+
     return max(dtypes, key=dtypes.get)
 
 def state_dict_key_replace(state_dict, keys_to_replace):

From 03c5018c98b9dd2654dc4942a0978ac53e755900 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 15:14:07 -0400
Subject: [PATCH 293/315] Lower lowvram memory to 1/3 of free memory.

---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index bb4bcbb21..c4402a8a7 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -450,7 +450,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), current_free_mem * 0.5)
+            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), current_free_mem * 0.33)
             if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 

From 91be9c2867ef9ae5b255f038665649536c1e1b8b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 16:34:27 -0400
Subject: [PATCH 294/315] Tweak lowvram memory formula.

---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index c4402a8a7..b280b149d 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -450,7 +450,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), current_free_mem * 0.33)
+            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), min(current_free_mem * 0.4, current_free_mem - minimum_inference_memory()))
             if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 

From f7a5107784cded39f92a4bb7553507575e78edbe Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 3 Aug 2024 16:55:38 -0400
Subject: [PATCH 295/315] Fix crash.

---
 comfy/model_management.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index b280b149d..fb2747015 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -928,10 +928,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     if is_intel_xpu():
         return True
 
-    if device is None:
-        device = torch.device("cuda")
-
-    props = torch.cuda.get_device_properties(device)
+    props = torch.cuda.get_device_properties("cuda")
     if props.major >= 8:
         return True
 

From 56f3c660bf79769bbfa003c0e4152dfb50feadc5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 4 Aug 2024 04:06:00 -0400
Subject: [PATCH 296/315] ModelSamplingFlux now takes a resolution and adjusts
 the shift with it.

If you want to sample Flux dev exactly how the reference code does use
the same resolution as your image in this node.
---
 comfy_extras/nodes_model_advanced.py | 14 ++++++++++++--
 1 file changed, 12 insertions(+), 2 deletions(-)

diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index fef8a4873..918e6085a 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -2,6 +2,7 @@ import folder_paths
 import comfy.sd
 import comfy.model_sampling
 import comfy.latent_formats
+import nodes
 import torch
 
 class LCM(comfy.model_sampling.EPS):
@@ -174,7 +175,10 @@ class ModelSamplingFlux:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "max_shift": ("FLOAT", {"default": 1.15, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "base_shift": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01}),
+                              "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               }}
 
     RETURN_TYPES = ("MODEL",)
@@ -182,9 +186,15 @@ class ModelSamplingFlux:
 
     CATEGORY = "advanced/model"
 
-    def patch(self, model, shift):
+    def patch(self, model, max_shift, base_shift, width, height):
         m = model.clone()
 
+        x1 = 256
+        x2 = 4096
+        mm = (max_shift - base_shift) / (x2 - x1)
+        b = base_shift - mm * x1
+        shift = (width * height / (8 * 8 * 2 * 2)) * mm + b
+
         sampling_base = comfy.model_sampling.ModelSamplingFlux
         sampling_type = comfy.model_sampling.CONST
 

From 0a6b0081176c6233015ec00d004c534c088ddcb0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 4 Aug 2024 10:03:33 -0400
Subject: [PATCH 297/315] Fix issue with some custom nodes.

---
 comfy/model_base.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index 94f4d333c..d19f5697a 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -84,6 +84,7 @@ class BaseModel(torch.nn.Module):
             if comfy.model_management.force_channels_last():
                 self.diffusion_model.to(memory_format=torch.channels_last)
                 logging.debug("using channels last mode for diffusion model")
+            logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
         self.model_type = model_type
         self.model_sampling = model_sampling(model_config, model_type)
 
@@ -94,7 +95,6 @@ class BaseModel(torch.nn.Module):
         self.concat_keys = ()
         logging.info("model_type {}".format(model_type.name))
         logging.debug("adm {}".format(self.adm_channels))
-        logging.info("model weight dtype {}, manual cast: {}".format(self.get_dtype(), self.manual_cast_dtype))
         self.memory_usage_factor = model_config.memory_usage_factor
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):

From 3b71f84b5051905be8f3311abeb39d725743d15b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 4 Aug 2024 15:45:43 -0400
Subject: [PATCH 298/315] ONNX tracing fixes.

---
 comfy/ldm/aura/mmdit.py                     | 6 ++----
 comfy/ldm/common_dit.py                     | 8 ++++++++
 comfy/ldm/flux/model.py                     | 8 +++-----
 comfy/ldm/modules/diffusionmodules/mmdit.py | 5 ++---
 comfy/model_detection.py                    | 2 +-
 5 files changed, 16 insertions(+), 13 deletions(-)
 create mode 100644 comfy/ldm/common_dit.py

diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py
index 9956d3638..cd9a42185 100644
--- a/comfy/ldm/aura/mmdit.py
+++ b/comfy/ldm/aura/mmdit.py
@@ -9,6 +9,7 @@ import torch.nn.functional as F
 
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.ops
+import comfy.ldm.common_dit
 
 def modulate(x, shift, scale):
     return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
@@ -407,10 +408,7 @@ class MMDiT(nn.Module):
 
     def patchify(self, x):
         B, C, H, W = x.size()
-        pad_h = (self.patch_size - H % self.patch_size) % self.patch_size
-        pad_w = (self.patch_size - W % self.patch_size) % self.patch_size
-
-        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='circular')
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
         x = x.view(
             B,
             C,
diff --git a/comfy/ldm/common_dit.py b/comfy/ldm/common_dit.py
new file mode 100644
index 000000000..990025521
--- /dev/null
+++ b/comfy/ldm/common_dit.py
@@ -0,0 +1,8 @@
+import torch
+
+def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):
+    if padding_mode == "circular" and torch.jit.is_tracing() or torch.jit.is_scripting():
+        padding_mode = "reflect"
+    pad_h = (patch_size[0] - img.shape[-2] % patch_size[0]) % patch_size[0]
+    pad_w = (patch_size[1] - img.shape[-1] % patch_size[1]) % patch_size[1]
+    return torch.nn.functional.pad(img, (0, pad_w, 0, pad_h), mode=padding_mode)
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index e7931c16d..db6cf3d22 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -15,6 +15,7 @@ from .layers import (
 )
 
 from einops import rearrange, repeat
+import comfy.ldm.common_dit
 
 @dataclass
 class FluxParams:
@@ -42,7 +43,7 @@ class Flux(nn.Module):
         self.dtype = dtype
         params = FluxParams(**kwargs)
         self.params = params
-        self.in_channels = params.in_channels
+        self.in_channels = params.in_channels * 2 * 2
         self.out_channels = self.in_channels
         if params.hidden_size % params.num_heads != 0:
             raise ValueError(
@@ -125,10 +126,7 @@ class Flux(nn.Module):
     def forward(self, x, timestep, context, y, guidance, **kwargs):
         bs, c, h, w = x.shape
         patch_size = 2
-        pad_h = (patch_size - h % 2) % patch_size
-        pad_w = (patch_size - w % 2) % patch_size
-
-        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='circular')
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))
 
         img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
 
diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
index ea1b5aa05..491a58a20 100644
--- a/comfy/ldm/modules/diffusionmodules/mmdit.py
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -9,6 +9,7 @@ from .. import attention
 from einops import rearrange, repeat
 from .util import timestep_embedding
 import comfy.ops
+import comfy.ldm.common_dit
 
 def default(x, y):
     if x is not None:
@@ -111,9 +112,7 @@ class PatchEmbed(nn.Module):
         #             f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
         #         )
         if self.dynamic_img_pad:
-            pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
-            pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
-            x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode=self.padding_mode)
+            x = comfy.ldm.common_dit.pad_to_patch_size(x, self.patch_size, padding_mode=self.padding_mode)
         x = self.proj(x)
         if self.flatten:
             x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index dda9797b7..c47119686 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -131,7 +131,7 @@ def detect_unet_config(state_dict, key_prefix):
     if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux
         dit_config = {}
         dit_config["image_model"] = "flux"
-        dit_config["in_channels"] = 64
+        dit_config["in_channels"] = 16
         dit_config["vec_in_dim"] = 768
         dit_config["context_in_dim"] = 4096
         dit_config["hidden_size"] = 3072

From ddb6a9f47cd2e680aa821f320d52e909f0a03fc3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 4 Aug 2024 15:59:02 -0400
Subject: [PATCH 299/315] Set the step in EmptySD3LatentImage to 16.

These models work better when the res is a multiple of 16.
---
 comfy_extras/nodes_sd3.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py
index 0aafa2426..ae9b85981 100644
--- a/comfy_extras/nodes_sd3.py
+++ b/comfy_extras/nodes_sd3.py
@@ -27,8 +27,8 @@ class EmptySD3LatentImage:
 
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
-                              "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+        return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                              "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
                               "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "generate"

From 7afa985fbafc15b2b603a4428917f4a600560699 Mon Sep 17 00:00:00 2001
From: Silver <65376327+silveroxides@users.noreply.github.com>
Date: Sun, 4 Aug 2024 23:10:02 +0200
Subject: [PATCH 300/315] Correct spelling 'token_weight_pars_t5' to
 'token_weight_pairs_t5' (#4200)

---
 comfy/text_encoders/flux.py     | 4 ++--
 comfy/text_encoders/sd3_clip.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
index 0590741bb..ee26f560d 100644
--- a/comfy/text_encoders/flux.py
+++ b/comfy/text_encoders/flux.py
@@ -52,9 +52,9 @@ class FluxClipModel(torch.nn.Module):
 
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs_l = token_weight_pairs["l"]
-        token_weight_pars_t5 = token_weight_pairs["t5xxl"]
+        token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
 
-        t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
+        t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
         l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
         return t5_out, l_pooled
 
diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
index 143d884cb..549c068e9 100644
--- a/comfy/text_encoders/sd3_clip.py
+++ b/comfy/text_encoders/sd3_clip.py
@@ -81,7 +81,7 @@ class SD3ClipModel(torch.nn.Module):
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs_l = token_weight_pairs["l"]
         token_weight_pairs_g = token_weight_pairs["g"]
-        token_weight_pars_t5 = token_weight_pairs["t5xxl"]
+        token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
         lg_out = None
         pooled = None
         out = None
@@ -108,7 +108,7 @@ class SD3ClipModel(torch.nn.Module):
             pooled = torch.cat((l_pooled, g_pooled), dim=-1)
 
         if self.t5xxl is not None:
-            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
+            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
             if lg_out is not None:
                 out = torch.cat([lg_out, t5_out], dim=-2)
             else:

From 78e133d0415784924cd2674e2ee48f3eeca8a2aa Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 4 Aug 2024 21:59:42 -0400
Subject: [PATCH 301/315] Support simple diffusers Flux loras.

---
 comfy/lora.py  |  8 ++++++++
 comfy/utils.py | 53 ++++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 61 insertions(+)

diff --git a/comfy/lora.py b/comfy/lora.py
index fdc128c09..04e8861c9 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -288,4 +288,12 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = k[len("diffusion_model."):-len(".weight")]
                 key_map["base_model.model.{}".format(key_lora)] = k #official hunyuan lora format
 
+    if isinstance(model, comfy.model_base.Flux): #Diffusers lora Flux
+        diffusers_keys = comfy.utils.flux_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers flux lora format
+                key_map[key_lora] = to
+
     return key_map
diff --git a/comfy/utils.py b/comfy/utils.py
index 06e09170a..ec7d36607 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -415,6 +415,59 @@ def auraflow_to_diffusers(mmdit_config, output_prefix=""):
 
     return key_map
 
+def flux_to_diffusers(mmdit_config, output_prefix=""):
+    n_double_layers = mmdit_config.get("depth", 0)
+    n_single_layers = mmdit_config.get("depth_single_blocks", 0)
+    hidden_size = mmdit_config.get("hidden_size", 0)
+
+    key_map = {}
+    for index in range(n_double_layers):
+        prefix_from = "transformer_blocks.{}".format(index)
+        prefix_to = "{}double_blocks.{}".format(output_prefix, index)
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(prefix_from)
+            qkv = "{}.img_attn.qkv.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+        block_map = {"attn.to_out.0.weight": "img_attn.proj.weight",
+                     "attn.to_out.0.bias": "img_attn.proj.bias",
+                    }
+
+        for k in block_map:
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
+
+    for index in range(n_single_layers):
+        prefix_from = "single_transformer_blocks.{}".format(index)
+        prefix_to = "{}single_blocks.{}".format(output_prefix, index)
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(prefix_from)
+            qkv = "{}.linear1.{}".format(prefix_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+            key_map["{}proj_mlp.{}".format(k, end)] = (qkv, (0, hidden_size * 3, hidden_size))
+
+        block_map = {#TODO
+                    }
+
+        for k in block_map:
+            key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
+
+    MAP_BASIC = { #TODO
+    }
+
+    for k in MAP_BASIC:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
 def repeat_to_batch_size(tensor, batch_size, dim=0):
     if tensor.shape[dim] > batch_size:
         return tensor.narrow(dim, 0, batch_size)

From a178e25912b01abf436eba1cfaab316ba02d272d Mon Sep 17 00:00:00 2001
From: a-One-Fan <100067309+a-One-Fan@users.noreply.github.com>
Date: Mon, 5 Aug 2024 08:26:20 +0300
Subject: [PATCH 302/315] Fix Flux FP64 math on XPU (#4210)

---
 comfy/ldm/flux/math.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index 88c2b6bb4..136ce2aa8 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -14,7 +14,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    if comfy.model_management.is_device_mps(pos.device):
+    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu():
         device = torch.device("cpu")
     else:
         device = pos.device

From 33e5203a2a7bc90dc4c6577ed645456abc530155 Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Mon, 5 Aug 2024 09:25:28 -0700
Subject: [PATCH 303/315] Don't cache index.html (#4211)

---
 server.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/server.py b/server.py
index 23ca2fd33..1c07f9781 100644
--- a/server.py
+++ b/server.py
@@ -127,7 +127,11 @@ class PromptServer():
 
         @routes.get("/")
         async def get_root(request):
-            return web.FileResponse(os.path.join(self.web_root, "index.html"))
+            response = web.FileResponse(os.path.join(self.web_root, "index.html"))
+            response.headers['Cache-Control'] = 'no-cache'
+            response.headers["Pragma"] = "no-cache"
+            response.headers["Expires"] = "0"
+            return response
 
         @routes.get("/embeddings")
         def get_embeddings(self):

From e545a636baae052000abb1250a69e1cac32b2bae Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 5 Aug 2024 12:31:12 -0400
Subject: [PATCH 304/315] This probably doesn't work anymore.

---
 README.md | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/README.md b/README.md
index a542ed4d6..d5ded7297 100644
--- a/README.md
+++ b/README.md
@@ -165,20 +165,6 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
 
 ```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
 
-### I already have another UI for Stable Diffusion installed do I really have to install all of these dependencies?
-
-You don't. If you have another UI installed and working with its own python venv you can use that venv to run ComfyUI. You can open up your favorite terminal and activate it:
-
-```source path_to_other_sd_gui/venv/bin/activate```
-
-or on Windows:
-
-With Powershell: ```"path_to_other_sd_gui\venv\Scripts\Activate.ps1"```
-
-With cmd.exe: ```"path_to_other_sd_gui\venv\Scripts\activate.bat"```
-
-And then you can use that terminal to run ComfyUI without installing any dependencies. Note that the venv folder might be called something else depending on the SD UI.
-
 # Running
 
 ```python main.py```

From 8edbcf520900112d4e11f510ba33949503b58f51 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 5 Aug 2024 16:24:04 -0400
Subject: [PATCH 305/315] Improve performance on some lowend GPUs.

---
 comfy/model_management.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index fb2747015..3d9ed5251 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -528,7 +528,7 @@ def unet_inital_load_device(parameters, dtype):
         return cpu_dev
 
 def maximum_vram_for_weights(device=None):
-    return (get_total_memory(device) * 0.8 - minimum_inference_memory())
+    return (get_total_memory(device) * 0.88 - minimum_inference_memory())
 
 def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
     if args.bf16_unet:

From 1abc9c8703abbb1f4d666cc2d6be34c9e13480c3 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Mon, 5 Aug 2024 17:07:16 -0700
Subject: [PATCH 306/315] Stable release uses cached dependencies (#4231)

* Release stable based on existing tag.

* Update default cuda to 12.1.
---
 .github/workflows/stable-release.yml | 92 +++++++++++++---------------
 1 file changed, 43 insertions(+), 49 deletions(-)

diff --git a/.github/workflows/stable-release.yml b/.github/workflows/stable-release.yml
index 19035c02c..658816afe 100644
--- a/.github/workflows/stable-release.yml
+++ b/.github/workflows/stable-release.yml
@@ -2,9 +2,28 @@
 name: "Release Stable Version"
 
 on:
-  push:
-    tags:
-      - 'v*'
+  workflow_dispatch:
+    inputs:
+      git_tag:
+        description: 'Git tag'
+        required: true
+        type: string
+      cu:
+        description: 'CUDA version'
+        required: true
+        type: string
+        default: "121"
+      python_minor:
+        description: 'Python minor version'
+        required: true
+        type: string
+        default: "11"
+      python_patch:
+        description: 'Python patch version'
+        required: true
+        type: string
+        default: "9"
+
 
 jobs:
   package_comfy_windows:
@@ -13,69 +32,44 @@ jobs:
       packages: "write"
       pull-requests: "read"
     runs-on: windows-latest
-    strategy:
-      matrix:
-        python_version: [3.11.8]
-        cuda_version: [121]
     steps:
-      - name: Calculate Minor Version
-        shell: bash
-        run: |
-          # Extract the minor version from the Python version
-          MINOR_VERSION=$(echo "${{ matrix.python_version }}" | cut -d'.' -f2)
-          echo "MINOR_VERSION=$MINOR_VERSION" >> $GITHUB_ENV
-      - name: Setup Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: ${{ matrix.python_version }}
-        
       - uses: actions/checkout@v4
         with:
+          ref: ${{ inputs.git_tag }}
           fetch-depth: 0
           persist-credentials: false
+      - uses: actions/cache/restore@v4
+        id: cache
+        with:
+          path: |
+            cu${{ inputs.cu }}_python_deps.tar
+            update_comfyui_and_python_dependencies.bat
+          key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
       - shell: bash
         run: |
-          echo "@echo off
-          call update_comfyui.bat nopause
-          echo -
-          echo This will try to update pytorch and all python dependencies.
-          echo -
-          echo If you just want to update normally, close this and run update_comfyui.bat instead.
-          echo -
-          pause
-          ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu${{ matrix.cuda_version }} -r ../ComfyUI/requirements.txt pygit2
-          pause" > update_comfyui_and_python_dependencies.bat
-
-          python -m pip wheel --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu${{ matrix.cuda_version }} -r requirements.txt pygit2 -w ./temp_wheel_dir
-          python -m pip install --no-cache-dir ./temp_wheel_dir/*
-          echo installed basic
-          ls -lah temp_wheel_dir
-          mv temp_wheel_dir cu${{ matrix.cuda_version }}_python_deps
-          mv cu${{ matrix.cuda_version }}_python_deps ../
+          mv cu${{ inputs.cu }}_python_deps.tar ../
           mv update_comfyui_and_python_dependencies.bat ../
           cd ..
+          tar xf cu${{ inputs.cu }}_python_deps.tar
           pwd
           ls
-          
+
+      - shell: bash
+        run: |
+          cd ..
           cp -r ComfyUI ComfyUI_copy
-          curl https://www.python.org/ftp/python/${{ matrix.python_version }}/python-${{ matrix.python_version }}-embed-amd64.zip -o python_embeded.zip
+          curl https://www.python.org/ftp/python/3.${{ inputs.python_minor }}.${{ inputs.python_patch }}/python-3.${{ inputs.python_minor }}.${{ inputs.python_patch }}-embed-amd64.zip -o python_embeded.zip
           unzip python_embeded.zip -d python_embeded
           cd python_embeded
           echo ${{ env.MINOR_VERSION }}
-          echo 'import site' >> ./python3${{ env.MINOR_VERSION }}._pth
+          echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
           curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
           ./python.exe get-pip.py
-          ./python.exe --version
-          echo "Pip version:"
-          ./python.exe -m pip --version
+          ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
+            sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
+            cd ..
 
-          set PATH=$PWD/Scripts:$PATH
-          echo $PATH
-          ./python.exe -s -m pip install ../cu${{ matrix.cuda_version }}_python_deps/*
-          sed -i '1i../ComfyUI' ./python3${{ env.MINOR_VERSION }}._pth
-          cd ..
-
-          git clone https://github.com/comfyanonymous/taesd
+          git clone --depth 1 https://github.com/comfyanonymous/taesd
           cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
 
           mkdir ComfyUI_windows_portable
@@ -104,7 +98,7 @@ jobs:
         with:
           repo_token: ${{ secrets.GITHUB_TOKEN }}
           file: ComfyUI_windows_portable_nvidia.7z
-          tag: ${{ github.ref }}
+          tag: ${{ inputs.git_tag }}
           overwrite: true
           prerelease: true
           make_latest: false

From 2d75df45e6eb354acb800707bbb6b91f184d4ede Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 5 Aug 2024 21:58:28 -0400
Subject: [PATCH 307/315] Flux tweak memory usage.

---
 comfy/supported_models.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 94fdcc0d2..6cecb9a02 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -640,7 +640,7 @@ class Flux(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.Flux
 
-    memory_usage_factor = 2.6
+    memory_usage_factor = 2.8
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 

From 841e74ac402e602471af48594d387496b0f76f4f Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 6 Aug 2024 01:27:28 -0400
Subject: [PATCH 308/315] Change browser test CI python to 3.8 (#4234)

---
 .github/workflows/test-browser.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml
index 7beb0c696..ce0bc37a3 100644
--- a/.github/workflows/test-browser.yml
+++ b/.github/workflows/test-browser.yml
@@ -32,7 +32,7 @@ jobs:
         node-version: lts/*
     - uses: actions/setup-python@v4
       with:
-        python-version: '3.10'
+        python-version: '3.8'
     - name: Install requirements
       run: |
         python -m pip install --upgrade pip

From f3bc40223a3bd58db51dc44da8bafe2aba8d6bc3 Mon Sep 17 00:00:00 2001
From: Silver <65376327+silveroxides@users.noreply.github.com>
Date: Tue, 6 Aug 2024 07:45:24 +0200
Subject: [PATCH 309/315] Add format metadata to CLIP save to make compatible
 with diffusers safetensors loading (#4233)

---
 comfy_extras/nodes_model_merging.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py
index b0d149c60..136f9a984 100644
--- a/comfy_extras/nodes_model_merging.py
+++ b/comfy_extras/nodes_model_merging.py
@@ -264,6 +264,7 @@ class CLIPSave:
 
         metadata = {}
         if not args.disable_metadata:
+            metadata["format"] = "pt"
             metadata["prompt"] = prompt_info
             if extra_pnginfo is not None:
                 for x in extra_pnginfo:

From 2894511893b0ad27151b615c7488380bc0aa73f8 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Mon, 5 Aug 2024 22:46:09 -0700
Subject: [PATCH 310/315] Clone taesd with depth of 1 to reduce download size.
 (#4232)

---
 .github/workflows/windows_release_nightly_pytorch.yml | 2 +-
 .github/workflows/windows_release_package.yml         | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml
index ba388cd44..0b29b4d91 100644
--- a/.github/workflows/windows_release_nightly_pytorch.yml
+++ b/.github/workflows/windows_release_nightly_pytorch.yml
@@ -55,7 +55,7 @@ jobs:
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
             cd ..
 
-            git clone https://github.com/comfyanonymous/taesd
+            git clone --depth 1 https://github.com/comfyanonymous/taesd
             cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
 
             mkdir ComfyUI_windows_portable_nightly_pytorch
diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml
index 5aed73e55..84d99b8a6 100644
--- a/.github/workflows/windows_release_package.yml
+++ b/.github/workflows/windows_release_package.yml
@@ -66,7 +66,7 @@ jobs:
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
             cd ..
 
-            git clone https://github.com/comfyanonymous/taesd
+            git clone --depth 1 https://github.com/comfyanonymous/taesd
             cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
 
             mkdir ComfyUI_windows_portable

From c14ac98fedd0176686d285d384abec5e4c0140c2 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 6 Aug 2024 03:22:39 -0400
Subject: [PATCH 311/315] Unload models and load them back in lowvram mode no
 free vram.

---
 comfy/model_management.py | 12 ++++++++++--
 1 file changed, 10 insertions(+), 2 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 3d9ed5251..cdbcd0be5 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -352,6 +352,7 @@ def unload_model_clones(model, unload_weights_only=True, force_unload=True):
 def free_memory(memory_required, device, keep_loaded=[]):
     unloaded_model = []
     can_unload = []
+    unloaded_models = []
 
     for i in range(len(current_loaded_models) -1, -1, -1):
         shift_model = current_loaded_models[i]
@@ -369,7 +370,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
         unloaded_model.append(i)
 
     for i in sorted(unloaded_model, reverse=True):
-        current_loaded_models.pop(i)
+        unloaded_models.append(current_loaded_models.pop(i))
 
     if len(unloaded_model) > 0:
         soft_empty_cache()
@@ -378,6 +379,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
             mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
             if mem_free_torch > mem_free_total * 0.25:
                 soft_empty_cache()
+    return unloaded_models
 
 def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None):
     global vram_state
@@ -421,7 +423,13 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
         for d in devs:
             if d != torch.device("cpu"):
                 free_memory(extra_mem, d, models_already_loaded)
-        return
+                free_mem = get_free_memory(d)
+                if free_mem < minimum_memory_required:
+                    logging.info("Unloading models for lowram load.") #TODO: partial model unloading when this case happens, also handle the opposite case where models can be unlowvramed.
+                    models_to_load = free_memory(minimum_memory_required, d)
+                    logging.info("{} models unloaded.".format(len(models_to_load)))
+        if len(models_to_load) == 0:
+            return
 
     logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
 

From de17a9755ecb8419cb167fe8504791df5b07246f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 6 Aug 2024 03:30:28 -0400
Subject: [PATCH 312/315] Unload all models if there's an OOM error.

---
 execution.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/execution.py b/execution.py
index 0a2e62e7e..d207e1b9e 100644
--- a/execution.py
+++ b/execution.py
@@ -188,6 +188,11 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
             "current_inputs": input_data_formatted,
             "current_outputs": output_data_formatted
         }
+
+        if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
+            logging.error("Got an OOM, unloading all loaded models.")
+            comfy.model_management.unload_all_models()
+
         return (False, error_details, ex)
 
     executed.add(unique_id)

From b334605a6631c12bbe7b3aff6d77526f47acdf42 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 6 Aug 2024 13:27:48 -0400
Subject: [PATCH 313/315] Fix OOMs happening in some cases.

A cloned model patcher sometimes reported a model was loaded on a device
when it wasn't.
---
 comfy/model_base.py       |  1 +
 comfy/model_management.py |  2 +-
 comfy/model_patcher.py    | 23 ++++++++++++++---------
 comfy/sd.py               |  2 +-
 4 files changed, 17 insertions(+), 11 deletions(-)

diff --git a/comfy/model_base.py b/comfy/model_base.py
index d19f5697a..cb6949649 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -74,6 +74,7 @@ class BaseModel(torch.nn.Module):
         self.latent_format = model_config.latent_format
         self.model_config = model_config
         self.manual_cast_dtype = model_config.manual_cast_dtype
+        self.device = device
 
         if not unet_config.get("disable_unet_model_creation", False):
             if self.manual_cast_dtype is not None:
diff --git a/comfy/model_management.py b/comfy/model_management.py
index cdbcd0be5..994fcd83b 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -274,7 +274,7 @@ class LoadedModel:
         return self.model.model_size()
 
     def model_memory_required(self, device):
-        if device == self.model.current_device:
+        if device == self.model.current_loaded_device():
             return 0
         else:
             return self.model_memory()
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index efac251ca..430b59879 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -64,9 +64,15 @@ def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_
     return model_options
 
 class ModelPatcher:
-    def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
+    def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
         self.size = size
         self.model = model
+        if not hasattr(self.model, 'device'):
+            logging.info("Model doesn't have a device attribute.")
+            self.model.device = offload_device
+        elif self.model.device is None:
+            self.model.device = offload_device
+
         self.patches = {}
         self.backup = {}
         self.object_patches = {}
@@ -75,11 +81,6 @@ class ModelPatcher:
         self.model_size()
         self.load_device = load_device
         self.offload_device = offload_device
-        if current_device is None:
-            self.current_device = self.offload_device
-        else:
-            self.current_device = current_device
-
         self.weight_inplace_update = weight_inplace_update
         self.model_lowvram = False
         self.lowvram_patch_counter = 0
@@ -92,7 +93,7 @@ class ModelPatcher:
         return self.size
 
     def clone(self):
-        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
+        n = ModelPatcher(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update)
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
@@ -302,7 +303,7 @@ class ModelPatcher:
 
             if device_to is not None:
                 self.model.to(device_to)
-                self.current_device = device_to
+                self.model.device = device_to
 
         return self.model
 
@@ -355,6 +356,7 @@ class ModelPatcher:
 
         self.model_lowvram = True
         self.lowvram_patch_counter = patch_counter
+        self.model.device = device_to
         return self.model
 
     def calculate_weight(self, patches, weight, key):
@@ -551,10 +553,13 @@ class ModelPatcher:
 
             if device_to is not None:
                 self.model.to(device_to)
-                self.current_device = device_to
+                self.model.device = device_to
 
         keys = list(self.object_patches_backup.keys())
         for k in keys:
             comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])
 
         self.object_patches_backup.clear()
+
+    def current_loaded_device(self):
+        return self.model.device
diff --git a/comfy/sd.py b/comfy/sd.py
index fac1a487f..94fc4e590 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -564,7 +564,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         logging.debug("left over keys: {}".format(left_over))
 
     if output_model:
-        model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
+        model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device())
         if inital_load_device != torch.device("cpu"):
             logging.info("loaded straight to GPU")
             model_management.load_model_gpu(model_patcher)

From 2a02546e2085487d34920e5b5c9b367918531f32 Mon Sep 17 00:00:00 2001
From: PhilWun <philipp.wundrack@live.de>
Date: Wed, 7 Aug 2024 03:59:34 +0200
Subject: [PATCH 314/315] Add type hints to folder_paths.py (#4191)

* add type hints to folder_paths.py

* replace deprecated standard collections type hints

* fix type error when using Python 3.8
---
 folder_paths.py | 64 ++++++++++++++++++++++++++-----------------------
 1 file changed, 34 insertions(+), 30 deletions(-)

diff --git a/folder_paths.py b/folder_paths.py
index 71faa2df4..3db1da61a 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -1,13 +1,13 @@
+from __future__ import annotations
+
 import os
 import time
 import logging
-from typing import Set, List, Dict, Tuple
+from collections.abc import Collection
 
-supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'])
+supported_pt_extensions: set[str] = {'.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'}
 
-SupportedFileExtensionsType = Set[str]
-ScanPathType = List[str]
-folder_names_and_paths: Dict[str, Tuple[ScanPathType, SupportedFileExtensionsType]] = {}
+folder_names_and_paths: dict[str, tuple[list[str], set[str]]] = {}
 
 base_path = os.path.dirname(os.path.realpath(__file__))
 models_dir = os.path.join(base_path, "models")
@@ -42,7 +42,7 @@ temp_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "temp
 input_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
 user_directory = os.path.join(os.path.dirname(os.path.realpath(__file__)), "user")
 
-filename_list_cache = {}
+filename_list_cache: dict[str, tuple[list[str], dict[str, float], float]] = {}
 
 if not os.path.exists(input_directory):
     try:
@@ -50,33 +50,33 @@ if not os.path.exists(input_directory):
     except:
         logging.error("Failed to create input directory")
 
-def set_output_directory(output_dir):
+def set_output_directory(output_dir: str) -> None:
     global output_directory
     output_directory = output_dir
 
-def set_temp_directory(temp_dir):
+def set_temp_directory(temp_dir: str) -> None:
     global temp_directory
     temp_directory = temp_dir
 
-def set_input_directory(input_dir):
+def set_input_directory(input_dir: str) -> None:
     global input_directory
     input_directory = input_dir
 
-def get_output_directory():
+def get_output_directory() -> str:
     global output_directory
     return output_directory
 
-def get_temp_directory():
+def get_temp_directory() -> str:
     global temp_directory
     return temp_directory
 
-def get_input_directory():
+def get_input_directory() -> str:
     global input_directory
     return input_directory
 
 
 #NOTE: used in http server so don't put folders that should not be accessed remotely
-def get_directory_by_type(type_name):
+def get_directory_by_type(type_name: str) -> str | None:
     if type_name == "output":
         return get_output_directory()
     if type_name == "temp":
@@ -88,7 +88,7 @@ def get_directory_by_type(type_name):
 
 # determine base_dir rely on annotation if name is 'filename.ext [annotation]' format
 # otherwise use default_path as base_dir
-def annotated_filepath(name):
+def annotated_filepath(name: str) -> tuple[str, str | None]:
     if name.endswith("[output]"):
         base_dir = get_output_directory()
         name = name[:-9]
@@ -104,7 +104,7 @@ def annotated_filepath(name):
     return name, base_dir
 
 
-def get_annotated_filepath(name, default_dir=None):
+def get_annotated_filepath(name: str, default_dir: str | None=None) -> str:
     name, base_dir = annotated_filepath(name)
 
     if base_dir is None:
@@ -116,7 +116,7 @@ def get_annotated_filepath(name, default_dir=None):
     return os.path.join(base_dir, name)
 
 
-def exists_annotated_filepath(name):
+def exists_annotated_filepath(name) -> bool:
     name, base_dir = annotated_filepath(name)
 
     if base_dir is None:
@@ -126,17 +126,17 @@ def exists_annotated_filepath(name):
     return os.path.exists(filepath)
 
 
-def add_model_folder_path(folder_name, full_folder_path):
+def add_model_folder_path(folder_name: str, full_folder_path: str) -> None:
     global folder_names_and_paths
     if folder_name in folder_names_and_paths:
         folder_names_and_paths[folder_name][0].append(full_folder_path)
     else:
         folder_names_and_paths[folder_name] = ([full_folder_path], set())
 
-def get_folder_paths(folder_name):
+def get_folder_paths(folder_name: str) -> list[str]:
     return folder_names_and_paths[folder_name][0][:]
 
-def recursive_search(directory, excluded_dir_names=None):
+def recursive_search(directory: str, excluded_dir_names: list[str] | None=None) -> tuple[list[str], dict[str, float]]:
     if not os.path.isdir(directory):
         return [], {}
 
@@ -153,6 +153,10 @@ def recursive_search(directory, excluded_dir_names=None):
         logging.warning(f"Warning: Unable to access {directory}. Skipping this path.")
 
     logging.debug("recursive file list on directory {}".format(directory))
+    dirpath: str
+    subdirs: list[str]
+    filenames: list[str]
+
     for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
         subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
         for file_name in filenames:
@@ -160,7 +164,7 @@ def recursive_search(directory, excluded_dir_names=None):
             result.append(relative_path)
 
         for d in subdirs:
-            path = os.path.join(dirpath, d)
+            path: str = os.path.join(dirpath, d)
             try:
                 dirs[path] = os.path.getmtime(path)
             except FileNotFoundError:
@@ -169,12 +173,12 @@ def recursive_search(directory, excluded_dir_names=None):
     logging.debug("found {} files".format(len(result)))
     return result, dirs
 
-def filter_files_extensions(files, extensions):
+def filter_files_extensions(files: Collection[str], extensions: Collection[str]) -> list[str]:
     return sorted(list(filter(lambda a: os.path.splitext(a)[-1].lower() in extensions or len(extensions) == 0, files)))
 
 
 
-def get_full_path(folder_name, filename):
+def get_full_path(folder_name: str, filename: str) -> str | None:
     global folder_names_and_paths
     if folder_name not in folder_names_and_paths:
         return None
@@ -189,7 +193,7 @@ def get_full_path(folder_name, filename):
 
     return None
 
-def get_filename_list_(folder_name):
+def get_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float]:
     global folder_names_and_paths
     output_list = set()
     folders = folder_names_and_paths[folder_name]
@@ -199,9 +203,9 @@ def get_filename_list_(folder_name):
         output_list.update(filter_files_extensions(files, folders[1]))
         output_folders = {**output_folders, **folders_all}
 
-    return (sorted(list(output_list)), output_folders, time.perf_counter())
+    return sorted(list(output_list)), output_folders, time.perf_counter()
 
-def cached_filename_list_(folder_name):
+def cached_filename_list_(folder_name: str) -> tuple[list[str], dict[str, float], float] | None:
     global filename_list_cache
     global folder_names_and_paths
     if folder_name not in filename_list_cache:
@@ -222,7 +226,7 @@ def cached_filename_list_(folder_name):
 
     return out
 
-def get_filename_list(folder_name):
+def get_filename_list(folder_name: str) -> list[str]:
     out = cached_filename_list_(folder_name)
     if out is None:
         out = get_filename_list_(folder_name)
@@ -230,17 +234,17 @@ def get_filename_list(folder_name):
         filename_list_cache[folder_name] = out
     return list(out[0])
 
-def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height=0):
-    def map_filename(filename):
+def get_save_image_path(filename_prefix: str, output_dir: str, image_width=0, image_height=0) -> tuple[str, str, int, str, str]:
+    def map_filename(filename: str) -> tuple[int, str]:
         prefix_len = len(os.path.basename(filename_prefix))
         prefix = filename[:prefix_len + 1]
         try:
             digits = int(filename[prefix_len + 1:].split('_')[0])
         except:
             digits = 0
-        return (digits, prefix)
+        return digits, prefix
 
-    def compute_vars(input, image_width, image_height):
+    def compute_vars(input: str, image_width: int, image_height: int) -> str:
         input = input.replace("%width%", str(image_width))
         input = input.replace("%height%", str(image_height))
         return input

From 1c08bf35b49879115dedd8ec6bc92d9e8d8fd871 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 7 Aug 2024 03:45:25 -0400
Subject: [PATCH 315/315] Support format for embeddings bundled in loras.

---
 comfy/sd1_clip.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index d32121d1b..6f3a7fd9a 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -313,6 +313,20 @@ def expand_directory_list(directories):
             dirs.add(root)
     return list(dirs)
 
+def bundled_embed(embed, key): #bundled embedding in lora format
+    i = 0
+    out_list = []
+    while True:
+        i += 1
+        k = key.format(i)
+        w = embed.get(k, None)
+        if w is None:
+            break
+        else:
+            out_list.append(w)
+
+    return torch.cat(out_list, dim=0)
+
 def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
     if isinstance(embedding_directory, str):
         embedding_directory = [embedding_directory]
@@ -378,6 +392,10 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
             embed_out = torch.cat(out_list, dim=0)
         elif embed_key is not None and embed_key in embed:
             embed_out = embed[embed_key]
+        elif 'bundle_emb.place1.string_to_param.*' in embed:
+            embed_out = bundled_embed(embed, 'bundle_emb.place{}.string_to_param.*')
+        elif 'bundle_emb.place1.{}'.format(embed_key) in embed:
+            embed_out = bundled_embed(embed, 'bundle_emb.place{}.{}'.format('{}', embed_key))
         else:
             values = embed.values()
             embed_out = next(iter(values))