Merge branch 'master' into master

.ci/windows_nightly_base_files/run_nvidia_gpu_fast.bat

@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --fast
+pause

.github/workflows/windows_release_nightly_pytorch.yml

@@ -67,6 +67,7 @@ jobs:
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
             cp -r ComfyUI/.ci/windows_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nightly_base_files/* ./
 
             echo "call update_comfyui.bat nopause
             ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2

README.md

@@ -230,7 +230,7 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the
 
 Use ```--preview-method auto``` to enable previews.
 
-The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
+The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth, taesdxl_decoder.pth, taesd3_decoder.pth and taef1_decoder.pth](https://github.com/madebyollin/taesd/) and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI and launch it with `--preview-method taesd` to enable high-quality previews.
 
 ## How to use TLS/SSL?
 Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`

api_server/routes/internal/README.md

@@ -0,0 +1,3 @@
+# ComfyUI Internal Routes
+
+All routes under the `/internal` path are designated for **internal use by ComfyUI only**. These routes are not intended for use by external applications may change at any time without notice.

api_server/routes/internal/internal_routes.py

@@ -0,0 +1,40 @@
+from aiohttp import web
+from typing import Optional
+from folder_paths import models_dir, user_directory, output_directory
+from api_server.services.file_service import FileService
+
+class InternalRoutes:
+    '''
+    The top level web router for internal routes: /internal/*
+    The endpoints here should NOT be depended upon. It is for ComfyUI frontend use only.
+    Check README.md for more information.
+    
+    '''
+    def __init__(self):
+        self.routes: web.RouteTableDef = web.RouteTableDef()
+        self._app: Optional[web.Application] = None
+        self.file_service = FileService({
+            "models": models_dir,
+            "user": user_directory,
+            "output": output_directory
+        })
+
+    def setup_routes(self):
+        @self.routes.get('/files')
+        async def list_files(request):
+            directory_key = request.query.get('directory', '')
+            try:
+                file_list = self.file_service.list_files(directory_key)
+                return web.json_response({"files": file_list})
+            except ValueError as e:
+                return web.json_response({"error": str(e)}, status=400)
+            except Exception as e:
+                return web.json_response({"error": str(e)}, status=500)
+
+
+    def get_app(self):
+        if self._app is None:
+            self._app = web.Application()
+            self.setup_routes()
+            self._app.add_routes(self.routes)
+        return self._app

api_server/services/file_service.py

@@ -0,0 +1,13 @@
+from typing import Dict, List, Optional
+from api_server.utils.file_operations import FileSystemOperations, FileSystemItem
+
+class FileService:
+    def __init__(self, allowed_directories: Dict[str, str], file_system_ops: Optional[FileSystemOperations] = None):
+        self.allowed_directories: Dict[str, str] = allowed_directories
+        self.file_system_ops: FileSystemOperations = file_system_ops or FileSystemOperations()
+
+    def list_files(self, directory_key: str) -> List[FileSystemItem]:
+        if directory_key not in self.allowed_directories:
+            raise ValueError("Invalid directory key")
+        directory_path: str = self.allowed_directories[directory_key]
+        return self.file_system_ops.walk_directory(directory_path)

api_server/utils/file_operations.py

@@ -0,0 +1,42 @@
+import os
+from typing import List, Union, TypedDict, Literal
+from typing_extensions import TypeGuard
+class FileInfo(TypedDict):
+    name: str
+    path: str
+    type: Literal["file"]
+    size: int
+
+class DirectoryInfo(TypedDict):
+    name: str
+    path: str
+    type: Literal["directory"]
+
+FileSystemItem = Union[FileInfo, DirectoryInfo]
+
+def is_file_info(item: FileSystemItem) -> TypeGuard[FileInfo]:
+    return item["type"] == "file"
+
+class FileSystemOperations:
+    @staticmethod
+    def walk_directory(directory: str) -> List[FileSystemItem]:
+        file_list: List[FileSystemItem] = []
+        for root, dirs, files in os.walk(directory):
+            for name in files:
+                file_path = os.path.join(root, name)
+                relative_path = os.path.relpath(file_path, directory)
+                file_list.append({
+                    "name": name,
+                    "path": relative_path,
+                    "type": "file",
+                    "size": os.path.getsize(file_path)
+                })
+            for name in dirs:
+                dir_path = os.path.join(root, name)
+                relative_path = os.path.relpath(dir_path, directory)
+                file_list.append({
+                    "name": name,
+                    "path": relative_path,
+                    "type": "directory"
+                })
+        return file_list

comfy/cli_args.py

@@ -123,6 +123,7 @@ parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha
 
 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.")
+parser.add_argument("--fast", action="store_true", help="Enable some untested and potentially quality deteriorating optimizations.")
 
 parser.add_argument("--dont-print-server", action="store_true", help="Don't print server output.")
 parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test for CI.")

comfy/clip_model.py

@@ -88,10 +88,11 @@ class CLIPTextModel_(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
+        num_positions = config_dict["max_position_embeddings"]
         self.eos_token_id = config_dict["eos_token_id"]
 
         super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, dtype=dtype, device=device, operations=operations)
+        self.embeddings = CLIPEmbeddings(embed_dim, num_positions=num_positions, 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)
 
@@ -123,7 +124,6 @@ class CLIPTextModel(torch.nn.Module):
         self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)
         embed_dim = config_dict["hidden_size"]
         self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
-        self.text_projection.weight.copy_(torch.eye(embed_dim))
         self.dtype = dtype
 
     def get_input_embeddings(self):

comfy/controlnet.py

@@ -391,7 +391,8 @@ def controlnet_config(sd):
     else:
         operations = comfy.ops.disable_weight_init
 
-    return model_config, operations, load_device, unet_dtype, manual_cast_dtype
+    offload_device = comfy.model_management.unet_offload_device()
+    return model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device
 
 def controlnet_load_state_dict(control_model, sd):
     missing, unexpected = control_model.load_state_dict(sd, strict=False)
@@ -405,12 +406,12 @@ def controlnet_load_state_dict(control_model, sd):
 
 def load_controlnet_mmdit(sd):
     new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(new_sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(new_sd)
     num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
     for k in sd:
         new_sd[k] = sd[k]
 
-    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=load_device, dtype=unet_dtype, **model_config.unet_config)
+    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, new_sd)
 
     latent_format = comfy.latent_formats.SD3()
@@ -420,9 +421,9 @@ def load_controlnet_mmdit(sd):
 
 
 def load_controlnet_hunyuandit(controlnet_data):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(controlnet_data)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(controlnet_data)
 
-    control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=load_device, dtype=unet_dtype)
+    control_model = comfy.ldm.hydit.controlnet.HunYuanControlNet(operations=operations, device=offload_device, dtype=unet_dtype)
     control_model = controlnet_load_state_dict(control_model, controlnet_data)
 
     latent_format = comfy.latent_formats.SDXL()
@@ -431,8 +432,8 @@ def load_controlnet_hunyuandit(controlnet_data):
     return control
 
 def load_controlnet_flux_xlabs(sd):
-    model_config, operations, load_device, unet_dtype, manual_cast_dtype = controlnet_config(sd)
+    model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd)
-    control_model = comfy.ldm.flux.controlnet_xlabs.ControlNetFlux(operations=operations, device=load_device, dtype=unet_dtype, **model_config.unet_config)
+    control_model = comfy.ldm.flux.controlnet_xlabs.ControlNetFlux(operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, sd)
     extra_conds = ['y', 'guidance']
     control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
@@ -536,6 +537,7 @@ def load_controlnet(ckpt_path, model=None):
     if manual_cast_dtype is not None:
         controlnet_config["operations"] = comfy.ops.manual_cast
     controlnet_config["dtype"] = unet_dtype
+    controlnet_config["device"] = comfy.model_management.unet_offload_device()
     controlnet_config.pop("out_channels")
     controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
     control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)

comfy/ldm/flux/layers.py

@@ -178,7 +178,7 @@ class DoubleStreamBlock(nn.Module):
         txt += txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
 
         if txt.dtype == torch.float16:
-            txt = txt.clip(-65504, 65504)
+            txt = torch.nan_to_num(txt, nan=0.0, posinf=65504, neginf=-65504)
 
         return img, txt
 
@@ -233,7 +233,7 @@ class SingleStreamBlock(nn.Module):
         output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
         x += mod.gate * output
         if x.dtype == torch.float16:
-            x = x.clip(-65504, 65504)
+            x = torch.nan_to_num(x, nan=0.0, posinf=65504, neginf=-65504)
         return x
 
 

comfy/lora.py

@@ -17,7 +17,10 @@
 """
 
 import comfy.utils
+import comfy.model_management
+import comfy.model_base
 import logging
+import torch
 
 LORA_CLIP_MAP = {
     "mlp.fc1": "mlp_fc1",
@@ -318,7 +321,196 @@ def model_lora_keys_unet(model, key_map={}):
         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["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
-                key_map[key_lora] = to
+                key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
 
     return key_map
+
+
+def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype):
+    dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype)
+    lora_diff *= alpha
+    weight_calc = weight + lora_diff.type(weight.dtype)
+    weight_norm = (
+        weight_calc.transpose(0, 1)
+        .reshape(weight_calc.shape[1], -1)
+        .norm(dim=1, keepdim=True)
+        .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
+        .transpose(0, 1)
+    )
+
+    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 calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
+    for p in patches:
+        strength = p[0]
+        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:
+            old_weight = weight
+            weight = weight.narrow(offset[0], offset[1], offset[2])
+
+        if strength_model != 1.0:
+            weight *= strength_model
+
+        if isinstance(v, list):
+            v = (calculate_weight(v[1:], v[0].clone(), key, intermediate_dtype=intermediate_dtype), )
+
+        if len(v) == 1:
+            patch_type = "diff"
+        elif len(v) == 2:
+            patch_type = v[0]
+            v = v[1]
+
+        if patch_type == "diff":
+            w1 = v[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 += 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, intermediate_dtype)
+            mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype)
+            dora_scale = v[4]
+            if v[2] is not None:
+                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, intermediate_dtype)
+                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:
+                lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
+                if dora_scale is not None:
+                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                else:
+                    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":
+            w1 = v[0]
+            w2 = v[1]
+            w1_a = v[3]
+            w1_b = v[4]
+            w2_a = v[5]
+            w2_b = v[6]
+            t2 = v[7]
+            dora_scale = v[8]
+            dim = None
+
+            if w1 is None:
+                dim = w1_b.shape[0]
+                w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype),
+                                comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype))
+            else:
+                w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype)
+
+            if w2 is None:
+                dim = w2_b.shape[0]
+                if t2 is None:
+                    w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype),
+                                    comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype))
+                else:
+                    w2 = torch.einsum('i j k l, j r, i p -> p r k l',
+                                        comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
+                                        comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype),
+                                        comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype))
+            else:
+                w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype)
+
+            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
+            else:
+                alpha = 1.0
+
+            try:
+                lora_diff = torch.kron(w1, w2).reshape(weight.shape)
+                if dora_scale is not None:
+                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                else:
+                    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":
+            w1a = v[0]
+            w1b = v[1]
+            if v[2] is not None:
+                alpha = v[2] / w1b.shape[0]
+            else:
+                alpha = 1.0
+
+            w2a = v[3]
+            w2b = v[4]
+            dora_scale = v[7]
+            if v[5] is not None: #cp decomposition
+                t1 = v[5]
+                t2 = v[6]
+                m1 = torch.einsum('i j k l, j r, i p -> p r k l',
+                                    comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype),
+                                    comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype),
+                                    comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype))
+
+                m2 = torch.einsum('i j k l, j r, i p -> p r k l',
+                                    comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
+                                    comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype),
+                                    comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype))
+            else:
+                m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype),
+                                comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype))
+                m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype),
+                                comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype))
+
+            try:
+                lora_diff = (m1 * m2).reshape(weight.shape)
+                if dora_scale is not None:
+                    weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                else:
+                    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":
+            if v[4] is not None:
+                alpha = v[4] / v[0].shape[0]
+            else:
+                alpha = 1.0
+
+            dora_scale = v[5]
+
+            a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
+            a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
+            b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
+            b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
+
+            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 = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
+                else:
+                    weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+            except Exception as e:
+                logging.error("ERROR {} {} {}".format(patch_type, key, e))
+        else:
+            logging.warning("patch type not recognized {} {}".format(patch_type, key))
+
+        if old_weight is not None:
+            weight = old_weight
+
+    return weight

comfy/model_base.py

@@ -96,10 +96,7 @@ class BaseModel(torch.nn.Module):
 
         if not unet_config.get("disable_unet_model_creation", False):
             if model_config.custom_operations is None:
-                if self.manual_cast_dtype is not None:
+                operations = comfy.ops.pick_operations(unet_config.get("dtype", None), self.manual_cast_dtype)
-                    operations = comfy.ops.manual_cast
-                else:
-                    operations = comfy.ops.disable_weight_init
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)

comfy/model_detection.py

@@ -473,8 +473,14 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None):
             'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
             'use_temporal_attention': False, 'use_temporal_resblock': False}
     
+    SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
+            'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
+            'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
+            'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+            'use_temporal_attention': False, 'use_temporal_resblock': False}  
 
-    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p]
+
+    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint]
 
     for unet_config in supported_models:
         matches = True

comfy/model_management.py

@@ -45,9 +45,14 @@ cpu_state = CPUState.GPU
 
 total_vram = 0
 
-lowvram_available = True
 xpu_available = False
+try:
+    torch_version = torch.version.__version__
+    xpu_available = (int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)) and torch.xpu.is_available()
+except:
+    pass
 
+lowvram_available = True
 if args.deterministic:
     logging.info("Using deterministic algorithms for pytorch")
     torch.use_deterministic_algorithms(True, warn_only=True)
@@ -67,10 +72,10 @@ if args.directml is not None:
 
 try:
     import intel_extension_for_pytorch as ipex
-    if torch.xpu.is_available():
+    _ = torch.xpu.device_count()
-        xpu_available = True
+    xpu_available = torch.xpu.is_available()
 except:
-    pass
+    xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available())
 
 try:
     if torch.backends.mps.is_available():
@@ -190,7 +195,6 @@ VAE_DTYPES = [torch.float32]
 
 try:
     if is_nvidia():
-        torch_version = torch.version.__version__
         if int(torch_version[0]) >= 2:
             if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                 ENABLE_PYTORCH_ATTENTION = True
@@ -322,8 +326,9 @@ class LoadedModel:
                 self.model_unload()
                 raise e
 
-        if is_intel_xpu() and not args.disable_ipex_optimize:
+        if is_intel_xpu() and not args.disable_ipex_optimize and self.real_model is not None:
-            self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True)
+            with torch.no_grad():
+                self.real_model = ipex.optimize(self.real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)
 
         self.weights_loaded = True
         return self.real_model
@@ -370,7 +375,7 @@ def minimum_inference_memory():
 
 EXTRA_RESERVED_VRAM = 200 * 1024 * 1024
 if any(platform.win32_ver()):
-    EXTRA_RESERVED_VRAM = 400 * 1024 * 1024 #Windows is higher because of the shared vram issue
+    EXTRA_RESERVED_VRAM = 500 * 1024 * 1024 #Windows is higher because of the shared vram issue
 
 if args.reserve_vram is not None:
     EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024
@@ -578,7 +583,9 @@ def cleanup_models(keep_clone_weights_loaded=False):
     to_delete = []
 
     for i in range(len(current_loaded_models)):
-        if sys.getrefcount(current_loaded_models[i].model) <= 3:
+        #TODO: very fragile function needs improvement
+        num_refs = sys.getrefcount(current_loaded_models[i].model)
+        if num_refs <= 2:
             if not keep_clone_weights_loaded:
                 to_delete = [i] + to_delete
             # TODO: find a less fragile way to do this.
@@ -685,6 +692,7 @@ def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.flo
     if bf16_supported and weight_dtype == torch.bfloat16:
         return None
 
+    fp16_supported = should_use_fp16(inference_device, prioritize_performance=True)
     for dt in supported_dtypes:
         if dt == torch.float16 and fp16_supported:
             return torch.float16
@@ -900,7 +908,8 @@ def pytorch_attention_flash_attention():
 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
+        macos_version = tuple(int(n) for n in platform.mac_ver()[0].split("."))
+        if (14, 5) <= macos_version < (14, 7):  # black image bug on recent versions of MacOS
             upcast = True
     except:
         pass
@@ -996,23 +1005,20 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if torch.version.hip:
         return True
 
-    props = torch.cuda.get_device_properties("cuda")
+    props = torch.cuda.get_device_properties(device)
     if props.major >= 8:
         return True
 
     if props.major < 6:
         return False
 
-    fp16_works = False
+    #FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32
-    #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled
-    #when the model doesn't actually fit on the card
-    #TODO: actually test if GP106 and others have the same type of behavior
     nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"]
     for x in nvidia_10_series:
         if x in props.name.lower():
-            fp16_works = True
+            return True
 
-    if fp16_works or manual_cast:
+    if manual_cast:
         free_model_memory = maximum_vram_for_weights(device)
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True
@@ -1052,7 +1058,7 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     if is_intel_xpu():
         return True
 
-    props = torch.cuda.get_device_properties("cuda")
+    props = torch.cuda.get_device_properties(device)
     if props.major >= 8:
         return True
 
@@ -1065,6 +1071,16 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
 
     return False
 
+def supports_fp8_compute(device=None):
+    props = torch.cuda.get_device_properties(device)
+    if props.major >= 9:
+        return True
+    if props.major < 8:
+        return False
+    if props.minor < 9:
+        return False
+    return True
+
 def soft_empty_cache(force=False):
     global cpu_state
     if cpu_state == CPUState.MPS:

comfy/model_patcher.py

@@ -27,30 +27,10 @@ import math
 import comfy.utils
 import comfy.float
 import comfy.model_management
+import comfy.lora
 from comfy.types import UnetWrapperFunction
 
 
-def weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype):
-    dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, intermediate_dtype)
-    lora_diff *= alpha
-    weight_calc = weight + lora_diff.type(weight.dtype)
-    weight_norm = (
-        weight_calc.transpose(0, 1)
-        .reshape(weight_calc.shape[1], -1)
-        .norm(dim=1, keepdim=True)
-        .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
-        .transpose(0, 1)
-    )
-
-    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()
 
@@ -92,12 +72,11 @@ def wipe_lowvram_weight(m):
     m.bias_function = None
 
 class LowVramPatch:
-    def __init__(self, key, model_patcher):
+    def __init__(self, key, patches):
         self.key = key
-        self.model_patcher = model_patcher
+        self.patches = patches
     def __call__(self, weight):
-        return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
+        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=weight.dtype)
-
 
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
@@ -329,7 +308,7 @@ class ModelPatcher:
             temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
         else:
             temp_weight = weight.to(torch.float32, copy=True)
-        out_weight = self.calculate_weight(self.patches[key], temp_weight, key)
+        out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)
         out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype)
         if inplace_update:
             comfy.utils.copy_to_param(self.model, key, out_weight)
@@ -360,13 +339,13 @@ class ModelPatcher:
                     if force_patch_weights:
                         self.patch_weight_to_device(weight_key)
                     else:
-                        m.weight_function = LowVramPatch(weight_key, self)
+                        m.weight_function = LowVramPatch(weight_key, self.patches)
                         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)
+                        m.bias_function = LowVramPatch(bias_key, self.patches)
                         patch_counter += 1
 
                 m.prev_comfy_cast_weights = m.comfy_cast_weights
@@ -428,174 +407,6 @@ class ModelPatcher:
             self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load)
         return self.model
 
-    def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
-        for p in patches:
-            strength = p[0]
-            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:
-                old_weight = weight
-                weight = weight.narrow(offset[0], offset[1], offset[2])
-
-            if strength_model != 1.0:
-                weight *= strength_model
-
-            if isinstance(v, list):
-                v = (self.calculate_weight(v[1:], v[0].clone(), key, intermediate_dtype=intermediate_dtype), )
-
-            if len(v) == 1:
-                patch_type = "diff"
-            elif len(v) == 2:
-                patch_type = v[0]
-                v = v[1]
-
-            if patch_type == "diff":
-                w1 = v[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 += 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, intermediate_dtype)
-                mat2 = comfy.model_management.cast_to_device(v[1], weight.device, intermediate_dtype)
-                dora_scale = v[4]
-                if v[2] is not None:
-                    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, intermediate_dtype)
-                    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:
-                    lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
-                    if dora_scale is not None:
-                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
-                    else:
-                        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":
-                w1 = v[0]
-                w2 = v[1]
-                w1_a = v[3]
-                w1_b = v[4]
-                w2_a = v[5]
-                w2_b = v[6]
-                t2 = v[7]
-                dora_scale = v[8]
-                dim = None
-
-                if w1 is None:
-                    dim = w1_b.shape[0]
-                    w1 = torch.mm(comfy.model_management.cast_to_device(w1_a, weight.device, intermediate_dtype),
-                                  comfy.model_management.cast_to_device(w1_b, weight.device, intermediate_dtype))
-                else:
-                    w1 = comfy.model_management.cast_to_device(w1, weight.device, intermediate_dtype)
-
-                if w2 is None:
-                    dim = w2_b.shape[0]
-                    if t2 is None:
-                        w2 = torch.mm(comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype),
-                                      comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype))
-                    else:
-                        w2 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                          comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
-                                          comfy.model_management.cast_to_device(w2_b, weight.device, intermediate_dtype),
-                                          comfy.model_management.cast_to_device(w2_a, weight.device, intermediate_dtype))
-                else:
-                    w2 = comfy.model_management.cast_to_device(w2, weight.device, intermediate_dtype)
-
-                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
-                else:
-                    alpha = 1.0
-
-                try:
-                    lora_diff = torch.kron(w1, w2).reshape(weight.shape)
-                    if dora_scale is not None:
-                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
-                    else:
-                        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":
-                w1a = v[0]
-                w1b = v[1]
-                if v[2] is not None:
-                    alpha = v[2] / w1b.shape[0]
-                else:
-                    alpha = 1.0
-
-                w2a = v[3]
-                w2b = v[4]
-                dora_scale = v[7]
-                if v[5] is not None: #cp decomposition
-                    t1 = v[5]
-                    t2 = v[6]
-                    m1 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                      comfy.model_management.cast_to_device(t1, weight.device, intermediate_dtype),
-                                      comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype),
-                                      comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype))
-
-                    m2 = torch.einsum('i j k l, j r, i p -> p r k l',
-                                      comfy.model_management.cast_to_device(t2, weight.device, intermediate_dtype),
-                                      comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype),
-                                      comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype))
-                else:
-                    m1 = torch.mm(comfy.model_management.cast_to_device(w1a, weight.device, intermediate_dtype),
-                                  comfy.model_management.cast_to_device(w1b, weight.device, intermediate_dtype))
-                    m2 = torch.mm(comfy.model_management.cast_to_device(w2a, weight.device, intermediate_dtype),
-                                  comfy.model_management.cast_to_device(w2b, weight.device, intermediate_dtype))
-
-                try:
-                    lora_diff = (m1 * m2).reshape(weight.shape)
-                    if dora_scale is not None:
-                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
-                    else:
-                        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":
-                if v[4] is not None:
-                    alpha = v[4] / v[0].shape[0]
-                else:
-                    alpha = 1.0
-
-                dora_scale = v[5]
-
-                a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
-                a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
-                b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
-                b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
-
-                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 = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
-                    else:
-                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
-                except Exception as e:
-                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
-            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):
         if unpatch_weights:
             if self.model.model_lowvram:
@@ -664,10 +475,10 @@ class ModelPatcher:
 
                 m.to(device_to)
                 if weight_key in self.patches:
-                    m.weight_function = LowVramPatch(weight_key, self)
+                    m.weight_function = LowVramPatch(weight_key, self.patches)
                     patch_counter += 1
                 if bias_key in self.patches:
-                    m.bias_function = LowVramPatch(bias_key, self)
+                    m.bias_function = LowVramPatch(bias_key, self.patches)
                     patch_counter += 1
 
                 m.prev_comfy_cast_weights = m.comfy_cast_weights
@@ -695,3 +506,7 @@ class ModelPatcher:
 
     def current_loaded_device(self):
         return self.model.device
+
+    def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
+        print("WARNING the ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead")
+        return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)

comfy/ops.py

@@ -18,31 +18,42 @@
 
 import torch
 import comfy.model_management
+from comfy.cli_args import args
 
+def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False):
+    if device is None or weight.device == device:
+        if not copy:
+            if dtype is None or weight.dtype == dtype:
+                return weight
+        return weight.to(dtype=dtype, copy=copy)
 
-def cast_to(weight, dtype=None, device=None, non_blocking=False):
     r = torch.empty_like(weight, dtype=dtype, device=device)
     r.copy_(weight, non_blocking=non_blocking)
     return r
 
-def cast_to_input(weight, input, non_blocking=False):
+def cast_to_input(weight, input, non_blocking=False, copy=True):
-    return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking)
+    return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
 
-def cast_bias_weight(s, input=None, dtype=None, device=None):
+def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
     if input is not None:
         if dtype is None:
             dtype = input.dtype
+        if bias_dtype is None:
+            bias_dtype = dtype
         if device is None:
             device = input.device
 
     bias = None
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
     if s.bias is not None:
-        bias = cast_to(s.bias, dtype, device, non_blocking=non_blocking)
+        has_function = s.bias_function is not None
-        if s.bias_function is not None:
+        bias = cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function)
+        if has_function:
             bias = s.bias_function(bias)
-    weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking)
+
-    if s.weight_function is not None:
+    has_function = s.weight_function is not None
+    weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function)
+    if has_function:
         weight = s.weight_function(weight)
     return weight, bias
 
@@ -240,3 +251,59 @@ class manual_cast(disable_weight_init):
 
     class Embedding(disable_weight_init.Embedding):
         comfy_cast_weights = True
+
+
+def fp8_linear(self, input):
+    dtype = self.weight.dtype
+    if dtype not in [torch.float8_e4m3fn]:
+        return None
+
+    if len(input.shape) == 3:
+        inn = input.reshape(-1, input.shape[2]).to(dtype)
+        non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
+        w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input.dtype)
+        w = w.t()
+
+        scale_weight = self.scale_weight
+        scale_input = self.scale_input
+        if scale_weight is None:
+            scale_weight = torch.ones((1), device=input.device, dtype=torch.float32)
+            if scale_input is None:
+                scale_input = scale_weight
+        if scale_input is None:
+            scale_input = torch.ones((1), device=input.device, dtype=torch.float32)
+
+        if bias is not None:
+            o = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
+        else:
+            o = torch._scaled_mm(inn, w, out_dtype=input.dtype, scale_a=scale_input, scale_b=scale_weight)
+
+        if isinstance(o, tuple):
+            o = o[0]
+
+        return o.reshape((-1, input.shape[1], self.weight.shape[0]))
+    return None
+
+class fp8_ops(manual_cast):
+    class Linear(manual_cast.Linear):
+        def reset_parameters(self):
+            self.scale_weight = None
+            self.scale_input = None
+            return None
+
+        def forward_comfy_cast_weights(self, input):
+            out = fp8_linear(self, input)
+            if out is not None:
+                return out
+
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.linear(input, weight, bias)
+
+
+def pick_operations(weight_dtype, compute_dtype, load_device=None):
+    if compute_dtype is None or weight_dtype == compute_dtype:
+        return disable_weight_init
+    if args.fast:
+        if comfy.model_management.supports_fp8_compute(load_device):
+            return fp8_ops
+    return manual_cast

comfy/sd.py

@@ -25,6 +25,7 @@ import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
 import comfy.text_encoders.flux
+import comfy.text_encoders.long_clipl
 
 
 import comfy.model_patcher
@@ -445,8 +446,13 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             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
+            w = clip_data[0].get("text_model.embeddings.position_embedding.weight", None)
-            clip_target.tokenizer = sd1_clip.SD1Tokenizer
+            if w is not None and w.shape[0] == 248:
+                clip_target.clip = comfy.text_encoders.long_clipl.LongClipModel
+                clip_target.tokenizer = comfy.text_encoders.long_clipl.LongClipTokenizer
+            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 = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)

comfy/sd1_clip.py

@@ -75,7 +75,6 @@ class ClipTokenWeightEncoder:
         return r
 
 class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
-    """Uses the CLIP transformer encoder for text (from huggingface)"""
     LAYERS = [
         "last",
         "pooled",
@@ -556,8 +555,12 @@ class SD1Tokenizer:
     def state_dict(self):
         return {}
 
+class SD1CheckpointClipModel(SDClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options)
+
 class SD1ClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SDClipModel, name=None, **kwargs):
+    def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs):
         super().__init__()
 
         if name is not None:

comfy/sdxl_clip.py

@@ -10,7 +10,7 @@ class SDXLClipG(sd1_clip.SDClipModel):
 
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
         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}, layer_norm_hidden_state=False, model_options=model_options)
+                         special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False, return_projected_pooled=True, model_options=model_options)
 
     def load_sd(self, sd):
         return super().load_sd(sd)
@@ -82,7 +82,7 @@ class StableCascadeClipG(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None, model_options={}):
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
         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": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, model_options=model_options)
+                         special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True, return_projected_pooled=True, model_options=model_options)
 
     def load_sd(self, sd):
         return super().load_sd(sd)

comfy/supported_models.py

@@ -654,6 +654,7 @@ class Flux(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         pref = self.text_encoder_key_prefix[0]
         t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        dtype_t5 = None
         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))

comfy/text_encoders/long_clipl.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "openai/clip-vit-large-patch14",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 49407,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 248,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0",
+  "vocab_size": 49408
+}

comfy/text_encoders/long_clipl.py

@@ -0,0 +1,19 @@
+from comfy import sd1_clip
+import os
+
+class LongClipTokenizer_(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(max_length=248, embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+class LongClipModel_(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "long_clipl.json")
+        super().__init__(device=device, textmodel_json_config=textmodel_json_config, return_projected_pooled=False, dtype=dtype, model_options=model_options)
+
+class LongClipTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, tokenizer=LongClipTokenizer_)
+
+class LongClipModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+        super().__init__(device=device, dtype=dtype, model_options=model_options, clip_model=LongClipModel_, **kwargs)

comfy/text_encoders/sd2_clip.py

@@ -8,7 +8,7 @@ class SD2ClipHModel(sd1_clip.SDClipModel):
             layer_idx=-2
 
         textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json")
-        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}, model_options=model_options)
+        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}, return_projected_pooled=True, model_options=model_options)
 
 class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
     def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):

comfy/text_encoders/sd3_clip.py

@@ -15,7 +15,7 @@ class T5XXLModel(sd1_clip.SDClipModel):
 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=77)
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, 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 SD3Tokenizer:

comfy_execution/caching.py

@@ -56,6 +56,8 @@ class CacheKeySetID(CacheKeySet):
         for node_id in node_ids:
             if node_id in self.keys:
                 continue
+            if not self.dynprompt.has_node(node_id):
+                continue
             node = self.dynprompt.get_node(node_id)
             self.keys[node_id] = (node_id, node["class_type"])
             self.subcache_keys[node_id] = (node_id, node["class_type"])
@@ -74,6 +76,8 @@ class CacheKeySetInputSignature(CacheKeySet):
         for node_id in node_ids:
             if node_id in self.keys:
                 continue
+            if not self.dynprompt.has_node(node_id):
+                continue
             node = self.dynprompt.get_node(node_id)
             self.keys[node_id] = self.get_node_signature(self.dynprompt, node_id)
             self.subcache_keys[node_id] = (node_id, node["class_type"])
@@ -87,6 +91,9 @@ class CacheKeySetInputSignature(CacheKeySet):
         return to_hashable(signature)
 
     def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
+        if not dynprompt.has_node(node_id):
+            # This node doesn't exist -- we can't cache it.
+            return [float("NaN")]
         node = dynprompt.get_node(node_id)
         class_type = node["class_type"]
         class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
@@ -112,6 +119,8 @@ class CacheKeySetInputSignature(CacheKeySet):
         return ancestors, order_mapping
 
     def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping):
+        if not dynprompt.has_node(node_id):
+            return
         inputs = dynprompt.get_node(node_id)["inputs"]
         input_keys = sorted(inputs.keys())
         for key in input_keys:

custom_nodes/example_node.py.example

@@ -23,6 +23,12 @@ class Example:
         Assumed to be False if not present.
     CATEGORY (`str`):
         The category the node should appear in the UI.
+    DEPRECATED (`bool`):
+        Indicates whether the node is deprecated. Deprecated nodes are hidden by default in the UI, but remain
+        functional in existing workflows that use them.
+    EXPERIMENTAL (`bool`):
+        Indicates whether the node is experimental. Experimental nodes are marked as such in the UI and may be subject to
+        significant changes or removal in future versions. Use with caution in production workflows.
     execute(s) -> tuple || None:
         The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
         For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.

execution.py

@@ -47,7 +47,8 @@ class IsChangedCache:
             self.is_changed[node_id] = node["is_changed"]
             return self.is_changed[node_id]
 
-        input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, self.outputs_cache)
+        # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
+        input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None)
         try:
             is_changed = _map_node_over_list(class_def, input_data_all, "IS_CHANGED")
             node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed]
@@ -491,6 +492,7 @@ class PromptExecutor:
                     break
 
                 result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results)
+                self.success = result != ExecutionResult.FAILURE
                 if result == ExecutionResult.FAILURE:
                     self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
                     break

server.py

@@ -29,6 +29,8 @@ from app.frontend_management import FrontendManager
 from app.user_manager import UserManager
 from model_filemanager import download_model, DownloadModelStatus
 from typing import Optional
+from api_server.routes.internal.internal_routes import InternalRoutes
+
 
 class BinaryEventTypes:
     PREVIEW_IMAGE = 1
@@ -72,6 +74,7 @@ class PromptServer():
         mimetypes.types_map['.js'] = 'application/javascript; charset=utf-8'
 
         self.user_manager = UserManager()
+        self.internal_routes = InternalRoutes()
         self.supports = ["custom_nodes_from_web"]
         self.prompt_queue = None
         self.loop = loop
@@ -139,6 +142,14 @@ class PromptServer():
             embeddings = folder_paths.get_filename_list("embeddings")
             return web.json_response(list(map(lambda a: os.path.splitext(a)[0], embeddings)))
 
+        @routes.get("/models/{folder}")
+        async def get_models(request):
+            folder = request.match_info.get("folder", None)
+            if not folder in folder_paths.folder_names_and_paths:
+                return web.Response(status=404)
+            files = folder_paths.get_filename_list(folder)
+            return web.json_response(files)
+
         @routes.get("/extensions")
         async def get_extensions(request):
             files = glob.glob(os.path.join(
@@ -442,6 +453,11 @@ class PromptServer():
 
             if hasattr(obj_class, 'OUTPUT_TOOLTIPS'):
                 info['output_tooltips'] = obj_class.OUTPUT_TOOLTIPS
+
+            if getattr(obj_class, "DEPRECATED", False):
+                info['deprecated'] = True
+            if getattr(obj_class, "EXPERIMENTAL", False):
+                info['experimental'] = True
             return info
 
         @routes.get("/object_info")
@@ -597,6 +613,7 @@ class PromptServer():
 
     def add_routes(self):
         self.user_manager.add_routes(self.routes)
+        self.app.add_subapp('/internal', self.internal_routes.get_app())
 
         # Prefix every route with /api for easier matching for delegation.
         # This is very useful for frontend dev server, which need to forward

tests-unit/server/routes/internal_routes_test.py

@@ -0,0 +1,115 @@
+import pytest
+from aiohttp import web
+from unittest.mock import MagicMock, patch
+from api_server.routes.internal.internal_routes import InternalRoutes
+from api_server.services.file_service import FileService
+from folder_paths import models_dir, user_directory, output_directory
+
+
+@pytest.fixture
+def internal_routes():
+    return InternalRoutes()
+
+@pytest.fixture
+def aiohttp_client_factory(aiohttp_client, internal_routes):
+    async def _get_client():
+        app = internal_routes.get_app()
+        return await aiohttp_client(app)
+    return _get_client
+
+@pytest.mark.asyncio
+async def test_list_files_valid_directory(aiohttp_client_factory, internal_routes):
+    mock_file_list = [
+        {"name": "file1.txt", "path": "file1.txt", "type": "file", "size": 100},
+        {"name": "dir1", "path": "dir1", "type": "directory"}
+    ]
+    internal_routes.file_service.list_files = MagicMock(return_value=mock_file_list)
+    client = await aiohttp_client_factory()
+    resp = await client.get('/files?directory=models')
+    assert resp.status == 200
+    data = await resp.json()
+    assert 'files' in data
+    assert len(data['files']) == 2
+    assert data['files'] == mock_file_list
+
+    # Check other valid directories
+    resp = await client.get('/files?directory=user')
+    assert resp.status == 200
+    resp = await client.get('/files?directory=output')
+    assert resp.status == 200
+
+@pytest.mark.asyncio
+async def test_list_files_invalid_directory(aiohttp_client_factory, internal_routes):
+    internal_routes.file_service.list_files = MagicMock(side_effect=ValueError("Invalid directory key"))
+    client = await aiohttp_client_factory()
+    resp = await client.get('/files?directory=invalid')
+    assert resp.status == 400
+    data = await resp.json()
+    assert 'error' in data
+    assert data['error'] == "Invalid directory key"
+
+@pytest.mark.asyncio
+async def test_list_files_exception(aiohttp_client_factory, internal_routes):
+    internal_routes.file_service.list_files = MagicMock(side_effect=Exception("Unexpected error"))
+    client = await aiohttp_client_factory()
+    resp = await client.get('/files?directory=models')
+    assert resp.status == 500
+    data = await resp.json()
+    assert 'error' in data
+    assert data['error'] == "Unexpected error"
+
+@pytest.mark.asyncio
+async def test_list_files_no_directory_param(aiohttp_client_factory, internal_routes):
+    mock_file_list = []
+    internal_routes.file_service.list_files = MagicMock(return_value=mock_file_list)
+    client = await aiohttp_client_factory()
+    resp = await client.get('/files')
+    assert resp.status == 200
+    data = await resp.json()
+    assert 'files' in data
+    assert len(data['files']) == 0
+
+def test_setup_routes(internal_routes):
+    internal_routes.setup_routes()
+    routes = internal_routes.routes
+    assert any(route.method == 'GET' and str(route.path) == '/files' for route in routes)
+
+def test_get_app(internal_routes):
+    app = internal_routes.get_app()
+    assert isinstance(app, web.Application)
+    assert internal_routes._app is not None
+
+def test_get_app_reuse(internal_routes):
+    app1 = internal_routes.get_app()
+    app2 = internal_routes.get_app()
+    assert app1 is app2
+
+@pytest.mark.asyncio
+async def test_routes_added_to_app(aiohttp_client_factory, internal_routes):
+    client = await aiohttp_client_factory()
+    try:
+        resp = await client.get('/files')
+        print(f"Response received: status {resp.status}")
+    except Exception as e:
+        print(f"Exception occurred during GET request: {e}")
+        raise
+
+    assert resp.status != 404, "Route /files does not exist"
+
+@pytest.mark.asyncio
+async def test_file_service_initialization():
+    with patch('api_server.routes.internal.internal_routes.FileService') as MockFileService:
+        # Create a mock instance
+        mock_file_service_instance = MagicMock(spec=FileService)
+        MockFileService.return_value = mock_file_service_instance
+        internal_routes = InternalRoutes()
+
+        # Check if FileService was initialized with the correct parameters
+        MockFileService.assert_called_once_with({
+            "models": models_dir,
+            "user": user_directory,
+            "output": output_directory
+        })
+
+        # Verify that the file_service attribute of InternalRoutes is set
+        assert internal_routes.file_service == mock_file_service_instance

tests-unit/server/services/file_service_test.py

@@ -0,0 +1,54 @@
+import pytest
+from unittest.mock import MagicMock
+from api_server.services.file_service import FileService
+
+@pytest.fixture
+def mock_file_system_ops():
+    return MagicMock()
+
+@pytest.fixture
+def file_service(mock_file_system_ops):
+    allowed_directories = {
+        "models": "/path/to/models",
+        "user": "/path/to/user",
+        "output": "/path/to/output"
+    }
+    return FileService(allowed_directories, file_system_ops=mock_file_system_ops)
+
+def test_list_files_valid_directory(file_service, mock_file_system_ops):
+    mock_file_system_ops.walk_directory.return_value = [
+        {"name": "file1.txt", "path": "file1.txt", "type": "file", "size": 100},
+        {"name": "dir1", "path": "dir1", "type": "directory"}
+    ]
+    
+    result = file_service.list_files("models")
+    
+    assert len(result) == 2
+    assert result[0]["name"] == "file1.txt"
+    assert result[1]["name"] == "dir1"
+    mock_file_system_ops.walk_directory.assert_called_once_with("/path/to/models")
+
+def test_list_files_invalid_directory(file_service):
+    # Does not support walking directories outside of the allowed directories
+    with pytest.raises(ValueError, match="Invalid directory key"):
+        file_service.list_files("invalid_key")
+
+def test_list_files_empty_directory(file_service, mock_file_system_ops):
+    mock_file_system_ops.walk_directory.return_value = []
+    
+    result = file_service.list_files("models")
+    
+    assert len(result) == 0
+    mock_file_system_ops.walk_directory.assert_called_once_with("/path/to/models")
+
+@pytest.mark.parametrize("directory_key", ["models", "user", "output"])
+def test_list_files_all_allowed_directories(file_service, mock_file_system_ops, directory_key):
+    mock_file_system_ops.walk_directory.return_value = [
+        {"name": f"file_{directory_key}.txt", "path": f"file_{directory_key}.txt", "type": "file", "size": 100}
+    ]
+    
+    result = file_service.list_files(directory_key)
+    
+    assert len(result) == 1
+    assert result[0]["name"] == f"file_{directory_key}.txt"
+    mock_file_system_ops.walk_directory.assert_called_once_with(f"/path/to/{directory_key}")

tests-unit/server/utils/file_operations_test.py

@@ -0,0 +1,42 @@
+import pytest
+from typing import List
+from api_server.utils.file_operations import FileSystemOperations, FileSystemItem, is_file_info
+
+@pytest.fixture
+def temp_directory(tmp_path):
+    # Create a temporary directory structure
+    dir1 = tmp_path / "dir1"
+    dir2 = tmp_path / "dir2"
+    dir1.mkdir()
+    dir2.mkdir()
+    (dir1 / "file1.txt").write_text("content1")
+    (dir2 / "file2.txt").write_text("content2")
+    (tmp_path / "file3.txt").write_text("content3")
+    return tmp_path
+
+def test_walk_directory(temp_directory):
+    result: List[FileSystemItem] = FileSystemOperations.walk_directory(str(temp_directory))
+    
+    assert len(result) == 5  # 2 directories and 3 files
+    
+    files = [item for item in result if item['type'] == 'file']
+    dirs = [item for item in result if item['type'] == 'directory']
+    
+    assert len(files) == 3
+    assert len(dirs) == 2
+    
+    file_names = {file['name'] for file in files}
+    assert file_names == {'file1.txt', 'file2.txt', 'file3.txt'}
+    
+    dir_names = {dir['name'] for dir in dirs}
+    assert dir_names == {'dir1', 'dir2'}
+
+def test_walk_directory_empty(tmp_path):
+    result = FileSystemOperations.walk_directory(str(tmp_path))
+    assert len(result) == 0
+
+def test_walk_directory_file_size(temp_directory):
+    result: List[FileSystemItem] = FileSystemOperations.walk_directory(str(temp_directory))
+    files = [item for item in result if is_file_info(item)]
+    for file in files:
+        assert file['size'] > 0  # Assuming all files have some content

tests/inference/test_execution.py

@@ -357,6 +357,25 @@ class TestExecution:
             assert 'prompt_id' in e.args[0], f"Did not get back a proper error message: {e}"
             assert e.args[0]['node_id'] == generator.id, "Error should have been on the generator node"
 
+    def test_missing_node_error(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
+        input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
+        input3 = g.node("StubImage", content="WHITE", height=512, width=512, batch_size=1)
+        mask = g.node("StubMask", value=0.5, height=512, width=512, batch_size=1)
+        mix1 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input2.out(0), mask=mask.out(0))
+        mix2 = g.node("TestLazyMixImages", image1=input1.out(0), image2=input3.out(0), mask=mask.out(0))
+        # We have multiple outputs. The first is invalid, but the second is valid
+        g.node("SaveImage", images=mix1.out(0))
+        g.node("SaveImage", images=mix2.out(0))
+        g.remove_node("removeme")
+        
+        client.run(g)
+
+        # Add back in the missing node to make sure the error doesn't break the server
+        input2 = g.node("StubImage", id="removeme", content="WHITE", height=512, width=512, batch_size=1)
+        client.run(g)
+
     def test_custom_is_changed(self, client: ComfyClient, builder: GraphBuilder):
         g = builder
         # Creating the nodes in this specific order previously caused a bug
@@ -450,8 +469,8 @@ class TestExecution:
         g = builder
         input1 = g.node("StubImage", content="BLACK", height=512, width=512, batch_size=1)
 
-        output1 = g.node("PreviewImage", images=input1.out(0))
+        output1 = g.node("SaveImage", images=input1.out(0))
-        output2 = g.node("PreviewImage", images=input1.out(0))
+        output2 = g.node("SaveImage", images=input1.out(0))
 
         result = client.run(g)
         images1 = result.get_images(output1)
@@ -459,3 +478,22 @@ class TestExecution:
         assert len(images1) == 1, "Should have 1 image"
         assert len(images2) == 1, "Should have 1 image"
 
+
+    # This tests that only constant outputs are used in the call to `IS_CHANGED`
+    def test_is_changed_with_outputs(self, client: ComfyClient, builder: GraphBuilder):
+        g = builder
+        input1 = g.node("StubConstantImage", value=0.5, height=512, width=512, batch_size=1)
+        test_node = g.node("TestIsChangedWithConstants", image=input1.out(0), value=0.5)
+
+        output = g.node("PreviewImage", images=test_node.out(0))
+
+        result = client.run(g)
+        images = result.get_images(output)
+        assert len(images) == 1, "Should have 1 image"
+        assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
+
+        result = client.run(g)
+        images = result.get_images(output)
+        assert len(images) == 1, "Should have 1 image"
+        assert numpy.array(images[0]).min() == 63 and numpy.array(images[0]).max() == 63, "Image should have value 0.25"
+        assert not result.did_run(test_node), "The execution should have been cached"

tests/inference/testing_nodes/testing-pack/specific_tests.py

@@ -95,6 +95,31 @@ class TestCustomIsChanged:
         else:
             return False
 
+class TestIsChangedWithConstants:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "custom_is_changed"
+
+    CATEGORY = "Testing/Nodes"
+
+    def custom_is_changed(self, image, value):
+        return (image * value,)
+    
+    @classmethod
+    def IS_CHANGED(cls, image, value):
+        if image is None:
+            return value
+        else:
+            return image.mean().item() * value
+
 class TestCustomValidation1:
     @classmethod
     def INPUT_TYPES(cls):
@@ -312,6 +337,7 @@ TEST_NODE_CLASS_MAPPINGS = {
     "TestLazyMixImages": TestLazyMixImages,
     "TestVariadicAverage": TestVariadicAverage,
     "TestCustomIsChanged": TestCustomIsChanged,
+    "TestIsChangedWithConstants": TestIsChangedWithConstants,
     "TestCustomValidation1": TestCustomValidation1,
     "TestCustomValidation2": TestCustomValidation2,
     "TestCustomValidation3": TestCustomValidation3,
@@ -325,6 +351,7 @@ TEST_NODE_DISPLAY_NAME_MAPPINGS = {
     "TestLazyMixImages": "Lazy Mix Images",
     "TestVariadicAverage": "Variadic Average",
     "TestCustomIsChanged": "Custom IsChanged",
+    "TestIsChangedWithConstants": "IsChanged With Constants",
     "TestCustomValidation1": "Custom Validation 1",
     "TestCustomValidation2": "Custom Validation 2",
     "TestCustomValidation3": "Custom Validation 3",

tests/inference/testing_nodes/testing-pack/stubs.py

@@ -28,6 +28,28 @@ class StubImage:
         elif content == "NOISE":
             return (torch.rand(batch_size, height, width, 3),)
 
+class StubConstantImage:
+    def __init__(self):
+        pass
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "height": ("INT", {"default": 512, "min": 1, "max": 1024 ** 3, "step": 1}),
+                "width": ("INT", {"default": 512, "min": 1, "max": 4096 ** 3, "step": 1}),
+                "batch_size": ("INT", {"default": 1, "min": 1, "max": 1024 ** 3, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "stub_constant_image"
+
+    CATEGORY = "Testing/Stub Nodes"
+
+    def stub_constant_image(self, value, height, width, batch_size):
+        return (torch.ones(batch_size, height, width, 3) * value,)
+
 class StubMask:
     def __init__(self):
         pass
@@ -93,12 +115,14 @@ class StubFloat:
 
 TEST_STUB_NODE_CLASS_MAPPINGS = {
     "StubImage": StubImage,
+    "StubConstantImage": StubConstantImage,
     "StubMask": StubMask,
     "StubInt": StubInt,
     "StubFloat": StubFloat,
 }
 TEST_STUB_NODE_DISPLAY_NAME_MAPPINGS = {
     "StubImage": "Stub Image",
+    "StubConstantImage": "Stub Constant Image",
     "StubMask": "Stub Mask",
     "StubInt": "Stub Int",
     "StubFloat": "Stub Float",

web/assets/index-DAK31IJJ.css

@@ -1,8 +1,8 @@
 @font-face {
     font-family: 'primeicons';
     font-display: block;
-    src: url('/assets/primeicons-DMOk5skT.eot');
+    src: url('./primeicons-DMOk5skT.eot');
-    src: url('/assets/primeicons-DMOk5skT.eot?#iefix') format('embedded-opentype'), url('/assets/primeicons-C6QP2o4f.woff2') format('woff2'), url('/assets/primeicons-WjwUDZjB.woff') format('woff'), url('/assets/primeicons-MpK4pl85.ttf') format('truetype'), url('/assets/primeicons-Dr5RGzOO.svg?#primeicons') format('svg');
+    src: url('./primeicons-DMOk5skT.eot?#iefix') format('embedded-opentype'), url('./primeicons-C6QP2o4f.woff2') format('woff2'), url('./primeicons-WjwUDZjB.woff') format('woff'), url('./primeicons-MpK4pl85.ttf') format('truetype'), url('./primeicons-Dr5RGzOO.svg?#primeicons') format('svg');
     font-weight: normal;
     font-style: normal;
 }
@@ -1330,6 +1330,184 @@
 .comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-7a0b94a3]:hover {
   border-right: 4px solid var(--p-button-text-primary-color);
 }
+
+:root {
+  --red-600: #dc3545;
+}
+
+.comfy-missing-nodes[data-v-286402f2] {
+  font-family: monospace;
+  color: var(--red-600);
+  padding: 1.5rem;
+  background-color: var(--surface-ground);
+  border-radius: var(--border-radius);
+  box-shadow: var(--card-shadow);
+}
+.warning-title[data-v-286402f2] {
+  margin-top: 0;
+  margin-bottom: 1rem;
+}
+.warning-description[data-v-286402f2] {
+  margin-bottom: 1rem;
+}
+.missing-nodes-list[data-v-286402f2] {
+  max-height: 300px;
+  overflow-y: auto;
+}
+.missing-nodes-list.maximized[data-v-286402f2] {
+  max-height: unset;
+}
+.missing-node-item[data-v-286402f2] {
+  display: flex;
+  align-items: center;
+  padding: 0.5rem;
+}
+.node-type[data-v-286402f2] {
+  font-weight: 600;
+  color: var(--text-color);
+}
+.node-hint[data-v-286402f2] {
+  margin-left: 0.5rem;
+  font-style: italic;
+  color: var(--text-color-secondary);
+}
+[data-v-286402f2] .p-button {
+  margin-left: auto;
+}
+.added-nodes-warning[data-v-286402f2] {
+  margin-top: 1rem;
+  font-style: italic;
+}
+
+.input-slider[data-v-fbaf7a8c] {
+  display: flex;
+  align-items: center;
+  gap: 1rem;
+}
+.slider-part[data-v-fbaf7a8c] {
+  flex-grow: 1;
+}
+.input-part[data-v-fbaf7a8c] {
+  width: 5rem !important;
+}
+
+.info-chip[data-v-6361f2fb] {
+  background: transparent;
+}
+.setting-item[data-v-6361f2fb] {
+  display: flex;
+  justify-content: space-between;
+  align-items: center;
+  margin-bottom: 1rem;
+}
+.setting-label[data-v-6361f2fb] {
+  display: flex;
+  align-items: center;
+  flex: 1;
+}
+.setting-input[data-v-6361f2fb] {
+  flex: 1;
+  display: flex;
+  justify-content: flex-end;
+  margin-left: 1rem;
+}
+
+/* Ensure PrimeVue components take full width of their container */
+.setting-input[data-v-6361f2fb] .p-inputtext,
+.setting-input[data-v-6361f2fb] .input-slider,
+.setting-input[data-v-6361f2fb] .p-select,
+.setting-input[data-v-6361f2fb] .p-togglebutton {
+  width: 100%;
+  max-width: 200px;
+}
+.setting-input[data-v-6361f2fb] .p-inputtext {
+  max-width: unset;
+}
+
+/* Special case for ToggleSwitch to align it to the right */
+.setting-input[data-v-6361f2fb] .p-toggleswitch {
+  margin-left: auto;
+}
+
+.search-box-input[data-v-8160f15b] {
+  width: 100%;
+}
+
+.no-results-placeholder[data-v-5a7d148a] {
+  display: flex;
+  justify-content: center;
+  align-items: center;
+  height: 100%;
+  padding: 2rem;
+}
+.no-results-placeholder[data-v-5a7d148a] .p-card {
+  background-color: var(--surface-ground);
+  text-align: center;
+}
+.no-results-placeholder h3[data-v-5a7d148a] {
+  color: var(--text-color);
+  margin-bottom: 0.5rem;
+}
+.no-results-placeholder p[data-v-5a7d148a] {
+  color: var(--text-color-secondary);
+  margin-bottom: 1rem;
+}
+
+/* Remove after we have tailwind setup */
+.border-none {
+  border: none !important;
+}
+.settings-tab-panels {
+  padding-top: 0px !important;
+}
+
+.settings-container[data-v-29723d1f] {
+  display: flex;
+  height: 70vh;
+  width: 60vw;
+  max-width: 1000px;
+  overflow: hidden;
+  /* Prevents container from scrolling */
+}
+.settings-sidebar[data-v-29723d1f] {
+  width: 250px;
+  flex-shrink: 0;
+  /* Prevents sidebar from shrinking */
+  overflow-y: auto;
+  padding: 10px;
+}
+.settings-search-box[data-v-29723d1f] {
+  width: 100%;
+  margin-bottom: 10px;
+}
+.settings-content[data-v-29723d1f] {
+  flex-grow: 1;
+  overflow-y: auto;
+  /* Allows vertical scrolling */
+}
+
+/* Ensure the Listbox takes full width of the sidebar */
+.settings-sidebar[data-v-29723d1f] .p-listbox {
+  width: 100%;
+}
+
+/* Optional: Style scrollbars for webkit browsers */
+.settings-sidebar[data-v-29723d1f]::-webkit-scrollbar,
+.settings-content[data-v-29723d1f]::-webkit-scrollbar {
+  width: 1px;
+}
+.settings-sidebar[data-v-29723d1f]::-webkit-scrollbar-thumb,
+.settings-content[data-v-29723d1f]::-webkit-scrollbar-thumb {
+  background-color: transparent;
+}
+
+.pi-cog[data-v-969a1066] {
+  font-size: 1.25rem;
+  margin-right: 0.5rem;
+}
+.version-tag[data-v-969a1066] {
+  margin-left: 0.5rem;
+}
 .lds-ring {
   display: inline-block;
   position: relative;
@@ -2881,6 +3059,7 @@ body {
 #graph-canvas {
   width: 100%;
   height: 100%;
+  touch-action: none;
 }
 
 .comfyui-body-right {
@@ -3482,184 +3661,6 @@ audio.comfy-audio.empty-audio-widget {
   max-width: 25vw;
 }
 
-:root {
-  --red-600: #dc3545;
-}
-
-.comfy-missing-nodes[data-v-286402f2] {
-  font-family: monospace;
-  color: var(--red-600);
-  padding: 1.5rem;
-  background-color: var(--surface-ground);
-  border-radius: var(--border-radius);
-  box-shadow: var(--card-shadow);
-}
-.warning-title[data-v-286402f2] {
-  margin-top: 0;
-  margin-bottom: 1rem;
-}
-.warning-description[data-v-286402f2] {
-  margin-bottom: 1rem;
-}
-.missing-nodes-list[data-v-286402f2] {
-  max-height: 300px;
-  overflow-y: auto;
-}
-.missing-nodes-list.maximized[data-v-286402f2] {
-  max-height: unset;
-}
-.missing-node-item[data-v-286402f2] {
-  display: flex;
-  align-items: center;
-  padding: 0.5rem;
-}
-.node-type[data-v-286402f2] {
-  font-weight: 600;
-  color: var(--text-color);
-}
-.node-hint[data-v-286402f2] {
-  margin-left: 0.5rem;
-  font-style: italic;
-  color: var(--text-color-secondary);
-}
-[data-v-286402f2] .p-button {
-  margin-left: auto;
-}
-.added-nodes-warning[data-v-286402f2] {
-  margin-top: 1rem;
-  font-style: italic;
-}
-
-.input-slider[data-v-fbaf7a8c] {
-  display: flex;
-  align-items: center;
-  gap: 1rem;
-}
-.slider-part[data-v-fbaf7a8c] {
-  flex-grow: 1;
-}
-.input-part[data-v-fbaf7a8c] {
-  width: 5rem !important;
-}
-
-.info-chip[data-v-4feeb3d2] {
-  background: transparent;
-}
-.setting-item[data-v-4feeb3d2] {
-  display: flex;
-  justify-content: space-between;
-  align-items: center;
-  margin-bottom: 1rem;
-}
-.setting-label[data-v-4feeb3d2] {
-  display: flex;
-  align-items: center;
-  flex: 1;
-}
-.setting-input[data-v-4feeb3d2] {
-  flex: 1;
-  display: flex;
-  justify-content: flex-end;
-  margin-left: 1rem;
-}
-
-/* Ensure PrimeVue components take full width of their container */
-.setting-input[data-v-4feeb3d2] .p-inputtext,
-.setting-input[data-v-4feeb3d2] .input-slider,
-.setting-input[data-v-4feeb3d2] .p-select,
-.setting-input[data-v-4feeb3d2] .p-togglebutton {
-  width: 100%;
-  max-width: 200px;
-}
-.setting-input[data-v-4feeb3d2] .p-inputtext {
-  max-width: unset;
-}
-
-/* Special case for ToggleSwitch to align it to the right */
-.setting-input[data-v-4feeb3d2] .p-toggleswitch {
-  margin-left: auto;
-}
-
-.search-box-input[data-v-3bbe5335] {
-  width: 100%;
-}
-
-.no-results-placeholder[data-v-5a7d148a] {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-  height: 100%;
-  padding: 2rem;
-}
-.no-results-placeholder[data-v-5a7d148a] .p-card {
-  background-color: var(--surface-ground);
-  text-align: center;
-}
-.no-results-placeholder h3[data-v-5a7d148a] {
-  color: var(--text-color);
-  margin-bottom: 0.5rem;
-}
-.no-results-placeholder p[data-v-5a7d148a] {
-  color: var(--text-color-secondary);
-  margin-bottom: 1rem;
-}
-
-/* Remove after we have tailwind setup */
-.border-none {
-  border: none !important;
-}
-.settings-tab-panels {
-  padding-top: 0px !important;
-}
-
-.settings-container[data-v-833dbfbb] {
-  display: flex;
-  height: 70vh;
-  width: 60vw;
-  max-width: 1000px;
-  overflow: hidden;
-  /* Prevents container from scrolling */
-}
-.settings-sidebar[data-v-833dbfbb] {
-  width: 250px;
-  flex-shrink: 0;
-  /* Prevents sidebar from shrinking */
-  overflow-y: auto;
-  padding: 10px;
-}
-.settings-search-box[data-v-833dbfbb] {
-  width: 100%;
-  margin-bottom: 10px;
-}
-.settings-content[data-v-833dbfbb] {
-  flex-grow: 1;
-  overflow-y: auto;
-  /* Allows vertical scrolling */
-}
-
-/* Ensure the Listbox takes full width of the sidebar */
-.settings-sidebar[data-v-833dbfbb] .p-listbox {
-  width: 100%;
-}
-
-/* Optional: Style scrollbars for webkit browsers */
-.settings-sidebar[data-v-833dbfbb]::-webkit-scrollbar,
-.settings-content[data-v-833dbfbb]::-webkit-scrollbar {
-  width: 1px;
-}
-.settings-sidebar[data-v-833dbfbb]::-webkit-scrollbar-thumb,
-.settings-content[data-v-833dbfbb]::-webkit-scrollbar-thumb {
-  background-color: transparent;
-}
-
-.pi-cog[data-v-969a1066] {
-  font-size: 1.25rem;
-  margin-right: 0.5rem;
-}
-.version-tag[data-v-969a1066] {
-  margin-left: 0.5rem;
-}
-
 :root {
   --sidebar-width: 64px;
   --sidebar-icon-size: 1.5rem;
@@ -3869,77 +3870,81 @@ audio.comfy-audio.empty-audio-widget {
   color: var(--error-text);
 }
 
-.comfy-vue-node-search-container[data-v-b8a4ffdc] {
+.comfy-vue-node-search-container[data-v-ba2c5897] {
   display: flex;
   width: 100%;
   min-width: 24rem;
   align-items: center;
   justify-content: center;
 }
-.comfy-vue-node-search-container[data-v-b8a4ffdc] * {
+.comfy-vue-node-search-container[data-v-ba2c5897] * {
   pointer-events: auto;
 }
-.comfy-vue-node-preview-container[data-v-b8a4ffdc] {
+.comfy-vue-node-preview-container[data-v-ba2c5897] {
   position: absolute;
   left: -350px;
   top: 50px;
 }
-.comfy-vue-node-search-box[data-v-b8a4ffdc] {
+.comfy-vue-node-search-box[data-v-ba2c5897] {
   z-index: 10;
   flex-grow: 1;
 }
-.option-container[data-v-b8a4ffdc] {
+.option-container[data-v-ba2c5897] {
   display: flex;
   width: 100%;
   cursor: pointer;
-  flex-direction: column;
+  align-items: center;
+  justify-content: space-between;
   overflow: hidden;
-  padding-left: 1rem;
+  padding-left: 0.5rem;
-  padding-right: 1rem;
+  padding-right: 0.5rem;
-  padding-top: 0.5rem;
+  padding-top: 0px;
-  padding-bottom: 0.5rem;
+  padding-bottom: 0px;
 }
-.option-display-name[data-v-b8a4ffdc] {
+.option-display-name[data-v-ba2c5897] {
+  display: flex;
+  flex-direction: column;
   font-weight: 600;
 }
-.option-category[data-v-b8a4ffdc] {
+.option-category[data-v-ba2c5897] {
   overflow: hidden;
   text-overflow: ellipsis;
   font-size: 0.875rem;
   line-height: 1.25rem;
+  font-weight: 300;
   --tw-text-opacity: 1;
   color: rgb(156 163 175 / var(--tw-text-opacity));
   /* Keeps the text on a single line by default */
   white-space: nowrap;
 }
-.i-badge[data-v-b8a4ffdc] {
+.i-badge[data-v-ba2c5897] {
   --tw-bg-opacity: 1;
   background-color: rgb(34 197 94 / var(--tw-bg-opacity));
   --tw-text-opacity: 1;
   color: rgb(255 255 255 / var(--tw-text-opacity));
 }
-.o-badge[data-v-b8a4ffdc] {
+.o-badge[data-v-ba2c5897] {
   --tw-bg-opacity: 1;
   background-color: rgb(239 68 68 / var(--tw-bg-opacity));
   --tw-text-opacity: 1;
   color: rgb(255 255 255 / var(--tw-text-opacity));
 }
-.c-badge[data-v-b8a4ffdc] {
+.c-badge[data-v-ba2c5897] {
   --tw-bg-opacity: 1;
   background-color: rgb(59 130 246 / var(--tw-bg-opacity));
   --tw-text-opacity: 1;
   color: rgb(255 255 255 / var(--tw-text-opacity));
 }
-.s-badge[data-v-b8a4ffdc] {
+.s-badge[data-v-ba2c5897] {
   --tw-bg-opacity: 1;
   background-color: rgb(234 179 8 / var(--tw-bg-opacity));
 }
-[data-v-b8a4ffdc] .highlight {
+[data-v-ba2c5897] .highlight {
   background-color: var(--p-primary-color);
   color: var(--p-primary-contrast-color);
   font-weight: bold;
   border-radius: 0.25rem;
-  padding: 0.125rem 0.25rem;
+  padding: 0rem 0.125rem;
   margin: -0.125rem 0.125rem;
 }
 
@@ -3971,46 +3976,57 @@ audio.comfy-audio.empty-audio-widget {
   z-index: 99999;
 }
 
-.result-container[data-v-0fac61d9] {
+.broken-image[data-v-1a883642] {
+  display: none;
+}
+.broken-image-placeholder[data-v-1a883642] {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
+  justify-content: center;
+  width: 100%;
+  height: 100%;
+  margin: 2rem;
+}
+.broken-image-placeholder i[data-v-1a883642] {
+  font-size: 3rem;
+  margin-bottom: 0.5rem;
+}
+
+.result-container[data-v-7ceacc88] {
+  width: 100%;
+  height: 100%;
   aspect-ratio: 1 / 1;
   overflow: hidden;
+  position: relative;
+  display: flex;
+  justify-content: center;
+  align-items: center;
 }
-[data-v-0fac61d9] img {
+[data-v-7ceacc88] .task-output-image {
-  position: absolute;
-  top: 50%;
-  left: 50%;
-  transform: translate(-50%, -50%);
   width: 100%;
   height: 100%;
   -o-object-fit: cover;
      object-fit: cover;
+  -o-object-position: center;
+     object-position: center;
 }
-.p-image-preview[data-v-0fac61d9] {
+.image-preview-mask[data-v-7ceacc88] {
-  position: static;
-  display: contents;
-}
-[data-v-0fac61d9] .image-preview-mask {
   position: absolute;
   left: 50%;
   top: 50%;
   transform: translate(-50%, -50%);
-  width: auto;
-  height: auto;
   display: flex;
   align-items: center;
   justify-content: center;
   opacity: 0;
-  padding: 10px;
+  transition: opacity 0.3s ease;
-  cursor: pointer;
+}
-  background: rgba(0, 0, 0, 0.5);
+.result-container:hover .image-preview-mask[data-v-7ceacc88] {
-  color: var(--p-image-preview-mask-color);
+  opacity: 1;
-  transition:
-    opacity var(--p-image-transition-duration),
-    background var(--p-image-transition-duration);
-  border-radius: 50%;
 }
 
-.task-result-preview[data-v-6cf8179c] {
+.task-result-preview[data-v-7c099cb7] {
   aspect-ratio: 1 / 1;
   overflow: hidden;
   display: flex;
@@ -4019,18 +4035,18 @@ audio.comfy-audio.empty-audio-widget {
   width: 100%;
   height: 100%;
 }
-.task-result-preview i[data-v-6cf8179c],
+.task-result-preview i[data-v-7c099cb7],
-.task-result-preview span[data-v-6cf8179c] {
+.task-result-preview span[data-v-7c099cb7] {
   font-size: 2rem;
 }
-.task-item[data-v-6cf8179c] {
+.task-item[data-v-7c099cb7] {
   display: flex;
   flex-direction: column;
   border-radius: 4px;
   overflow: hidden;
   position: relative;
 }
-.task-item-details[data-v-6cf8179c] {
+.task-item-details[data-v-7c099cb7] {
   position: absolute;
   bottom: 0;
   padding: 0.6rem;
@@ -4041,12 +4057,23 @@ audio.comfy-audio.empty-audio-widget {
 
 /* In dark mode, transparent background color for tags is not ideal for tags that
 are floating on top of images. */
-.tag-wrapper[data-v-6cf8179c] {
+.tag-wrapper[data-v-7c099cb7] {
   background-color: var(--p-primary-contrast-color);
   border-radius: 6px;
   display: inline-flex;
 }
 
+/* PrimeVue's galleria teleports the fullscreen gallery out of subtree so we
+cannot use scoped style here. */
+img.galleria-image {
+  max-width: 100vw;
+  max-height: 100vh;
+  -o-object-fit: contain;
+     object-fit: contain;
+  /* Set z-index so the close button doesn't get hidden behind the image when image is large */
+  z-index: -1;
+}
+
 .comfy-vue-side-bar-container[data-v-bde767d2] {
   display: flex;
   flex-direction: column;
@@ -4078,14 +4105,33 @@ are floating on top of images. */
   background-color: transparent;
 }
 
-.queue-grid[data-v-7f831ee9] {
+.scroll-container[data-v-bd027c46] {
+  height: 100%;
+  overflow-y: auto;
+}
+.queue-grid[data-v-bd027c46] {
   display: grid;
   grid-template-columns: repeat(auto-fill, minmax(200px, 1fr));
   padding: 0.5rem;
   gap: 0.5rem;
 }
 
-.spinner[data-v-40c18658] {
+.node-lib-tree-node-label {
+  display: flex;
+  align-items: center;
+  margin-left: var(--p-tree-node-gap);
+}
+
+[data-v-11e12183] .node-lib-search-box {
+  margin-left: 1rem;
+  margin-right: 1rem;
+  margin-top: 1rem;
+}
+[data-v-11e12183] .comfy-vue-side-bar-body {
+  background: var(--p-tree-background);
+}
+
+.spinner[data-v-afce9bd6] {
     position: absolute;
     inset: 0px;
     display: flex;

web/assets/index-DkvOTKox.js

@@ -48,7 +48,7 @@ var __async = (__this, __arguments, generator) => {
   });
 };
 var _PrimitiveNode_instances, onFirstConnection_fn, createWidget_fn, mergeWidgetConfig_fn, isValidConnection_fn, removeWidgets_fn, _convertedToProcess;
-import { C as ComfyDialog, $ as $el, a as ComfyApp, b as app, L as LGraphCanvas, c as LiteGraph, d as applyTextReplacements, e as ComfyWidgets, f as addValueControlWidgets, D as DraggableList, g as api, h as LGraphGroup, i as LGraphNode } from "./index-D8Zp4vRl.js";
+import { C as ComfyDialog, $ as $el, a as ComfyApp, b as app, L as LGraphCanvas, c as LiteGraph, d as applyTextReplacements, e as ComfyWidgets, f as addValueControlWidgets, D as DraggableList, g as api, u as useToastStore, h as LGraphGroup, i as LGraphNode } from "./index-CaD4RONs.js";
 const _ClipspaceDialog = class _ClipspaceDialog extends ComfyDialog {
   static registerButton(name, contextPredicate, callback) {
     const item = $el("button", {
@@ -891,6 +891,7 @@ app.registerExtension({
     });
     app.ui.settings.addSetting({
       id: id$4,
+      category: ["Comfy", "ColorPalette"],
       name: "Color Palette",
       type: /* @__PURE__ */ __name((name, setter, value) => {
         const options = [
@@ -923,12 +924,6 @@ app.registerExtension({
           options
         );
         return $el("tr", [
-          $el("td", [
-            $el("label", {
-              for: id$4.replaceAll(".", "-"),
-              textContent: "Color palette"
-            })
-          ]),
           $el("td", [
             els.select,
             $el(
@@ -1221,7 +1216,6 @@ app.registerExtension({
       const floatWeight = parseFloat(weight);
       if (isNaN(floatWeight)) return weight;
       const newWeight = floatWeight + delta;
-      if (newWeight < 0) return "0";
       return String(Number(newWeight.toFixed(10)));
     }
     __name(incrementWeight, "incrementWeight");
@@ -1302,7 +1296,7 @@ app.registerExtension({
       selectedText = addWeightToParentheses(selectedText);
       const weightDelta = event.key === "ArrowUp" ? delta : -delta;
       const updatedText = selectedText.replace(
-        /\((.*):(\d+(?:\.\d+)?)\)/,
+        /\((.*):([+-]?\d+(?:\.\d+)?)\)/,
         (match, text, weight) => {
           weight = incrementWeight(weight, weightDelta);
           if (weight == 1) {
@@ -1620,9 +1614,9 @@ function getWidgetConfig(slot) {
 }
 __name(getWidgetConfig, "getWidgetConfig");
 function getConfig(widgetName) {
-  var _a, _b, _c, _d;
+  var _a, _b, _c, _d, _e;
   const { nodeData } = this.constructor;
-  return (_d = (_a = nodeData == null ? void 0 : nodeData.input) == null ? void 0 : _a.required[widgetName]) != null ? _d : (_c = (_b = nodeData == null ? void 0 : nodeData.input) == null ? void 0 : _b.optional) == null ? void 0 : _c[widgetName];
+  return (_e = (_b = (_a = nodeData == null ? void 0 : nodeData.input) == null ? void 0 : _a.required) == null ? void 0 : _b[widgetName]) != null ? _e : (_d = (_c = nodeData == null ? void 0 : nodeData.input) == null ? void 0 : _c.optional) == null ? void 0 : _d[widgetName];
 }
 __name(getConfig, "getConfig");
 function isConvertibleWidget(widget, config) {
@@ -1854,8 +1848,7 @@ app.registerExtension({
   init() {
     useConversionSubmenusSetting = app.ui.settings.addSetting({
       id: "Comfy.NodeInputConversionSubmenus",
-      name: "Node widget/input conversion sub-menus",
+      name: "In the node context menu, place the entries that convert between input/widget in sub-menus.",
-      tooltip: "In the node context menu, place the entries that convert between input/widget in sub-menus.",
       type: "boolean",
       defaultValue: true
     });
@@ -3957,7 +3950,8 @@ app.registerExtension({
     }, "replace");
     app.ui.settings.addSetting({
       id: id$2,
-      name: "Invert Menu Scrolling",
+      category: ["Comfy", "Graph", "InvertMenuScrolling"],
+      name: "Invert Context Menu Scrolling",
       type: "boolean",
       defaultValue: false,
       onChange(value) {
@@ -3974,58 +3968,66 @@ app.registerExtension({
   name: "Comfy.Keybinds",
   init() {
     const keybindListener = /* @__PURE__ */ __name(function(event) {
-      const modifierPressed = event.ctrlKey || event.metaKey;
+      return __async(this, null, function* () {
-      if (modifierPressed && event.key === "Enter") {
+        const modifierPressed = event.ctrlKey || event.metaKey;
-        if (event.altKey) {
+        if (modifierPressed && event.key === "Enter") {
-          api.interrupt();
+          if (event.altKey) {
+            yield api.interrupt();
+            useToastStore().add({
+              severity: "info",
+              summary: "Interrupted",
+              detail: "Execution has been interrupted",
+              life: 1e3
+            });
+            return;
+          }
+          app.queuePrompt(event.shiftKey ? -1 : 0).then();
           return;
         }
-        app.queuePrompt(event.shiftKey ? -1 : 0).then();
+        const target = event.composedPath()[0];
-        return;
+        if (["INPUT", "TEXTAREA"].includes(target.tagName)) {
-      }
+          return;
-      const target = event.composedPath()[0];
-      if (["INPUT", "TEXTAREA"].includes(target.tagName)) {
-        return;
-      }
-      const modifierKeyIdMap = {
-        s: "#comfy-save-button",
-        o: "#comfy-file-input",
-        Backspace: "#comfy-clear-button",
-        d: "#comfy-load-default-button"
-      };
-      const modifierKeybindId = modifierKeyIdMap[event.key];
-      if (modifierPressed && modifierKeybindId) {
-        event.preventDefault();
-        const elem = document.querySelector(modifierKeybindId);
-        elem.click();
-        return;
-      }
-      if (event.ctrlKey || event.altKey || event.metaKey) {
-        return;
-      }
-      if (event.key === "Escape") {
-        const modals = document.querySelectorAll(".comfy-modal");
-        const modal = Array.from(modals).find(
-          (modal2) => window.getComputedStyle(modal2).getPropertyValue("display") !== "none"
-        );
-        if (modal) {
-          modal.style.display = "none";
         }
-        ;
+        const modifierKeyIdMap = {
-        [...document.querySelectorAll("dialog")].forEach((d) => {
+          s: "#comfy-save-button",
-          d.close();
+          o: "#comfy-file-input",
-        });
+          Backspace: "#comfy-clear-button",
-      }
+          d: "#comfy-load-default-button"
-      const keyIdMap = {
+        };
-        q: ".queue-tab-button.side-bar-button",
+        const modifierKeybindId = modifierKeyIdMap[event.key];
-        h: ".queue-tab-button.side-bar-button",
+        if (modifierPressed && modifierKeybindId) {
-        r: "#comfy-refresh-button"
+          event.preventDefault();
-      };
+          const elem = document.querySelector(modifierKeybindId);
-      const buttonId = keyIdMap[event.key];
+          elem.click();
-      if (buttonId) {
+          return;
-        const button = document.querySelector(buttonId);
+        }
-        button.click();
+        if (event.ctrlKey || event.altKey || event.metaKey) {
-      }
+          return;
+        }
+        if (event.key === "Escape") {
+          const modals = document.querySelectorAll(".comfy-modal");
+          const modal = Array.from(modals).find(
+            (modal2) => window.getComputedStyle(modal2).getPropertyValue("display") !== "none"
+          );
+          if (modal) {
+            modal.style.display = "none";
+          }
+          ;
+          [...document.querySelectorAll("dialog")].forEach((d) => {
+            d.close();
+          });
+        }
+        const keyIdMap = {
+          q: ".queue-tab-button.side-bar-button",
+          h: ".queue-tab-button.side-bar-button",
+          r: "#comfy-refresh-button"
+        };
+        const buttonId = keyIdMap[event.key];
+        if (buttonId) {
+          const button = document.querySelector(buttonId);
+          button.click();
+        }
+      });
     }, "keybindListener");
     window.addEventListener("keydown", keybindListener, true);
   }
@@ -4037,6 +4039,7 @@ const ext = {
     return __async(this, null, function* () {
       app2.ui.settings.addSetting({
         id: id$1,
+        category: ["Comfy", "Graph", "LinkRenderMode"],
         name: "Link Render Mode",
         defaultValue: 2,
         type: "combo",
@@ -5684,7 +5687,9 @@ app.registerExtension({
     LiteGraph.middle_click_slot_add_default_node = true;
     this.suggestionsNumber = app.ui.settings.addSetting({
       id: "Comfy.NodeSuggestions.number",
+      category: ["Comfy", "Node Search Box", "NodeSuggestions"],
       name: "Number of nodes suggestions",
+      tooltip: "Only for litegraph searchbox/context menu",
       type: "slider",
       attrs: {
         min: 1,
@@ -5766,7 +5771,8 @@ app.registerExtension({
   init() {
     app.ui.settings.addSetting({
       id: "Comfy.SnapToGrid.GridSize",
-      name: "Grid Size",
+      category: ["Comfy", "Graph", "GridSize"],
+      name: "Snap to gird size",
       type: "slider",
       attrs: {
         min: 1,
@@ -6176,4 +6182,4 @@ app.registerExtension({
     };
   }
 });
-//# sourceMappingURL=index--0nRVkuV.js.map
+//# sourceMappingURL=index-DkvOTKox.js.map

web/assets/userSelection-GRU1gtOt.js

@@ -20,7 +20,7 @@ var __async = (__this, __arguments, generator) => {
     step((generator = generator.apply(__this, __arguments)).next());
   });
 };
-import { j as createSpinner, g as api, $ as $el } from "./index-D8Zp4vRl.js";
+import { j as createSpinner, g as api, $ as $el } from "./index-CaD4RONs.js";
 const _UserSelectionScreen = class _UserSelectionScreen {
   show(users, user) {
     return __async(this, null, function* () {
@@ -139,4 +139,4 @@ window.comfyAPI.userSelection.UserSelectionScreen = UserSelectionScreen;
 export {
   UserSelectionScreen
 };
-//# sourceMappingURL=userSelection-CH4RQEqW.js.map
+//# sourceMappingURL=userSelection-GRU1gtOt.js.map

web/index.html

@@ -14,8 +14,8 @@
 		</style> -->
 		<link rel="stylesheet" type="text/css" href="user.css" />
 		<link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
-		<script type="module" crossorigin src="/assets/index-D8Zp4vRl.js"></script>
+		<script type="module" crossorigin src="./assets/index-CaD4RONs.js"></script>
-		<link rel="stylesheet" crossorigin href="/assets/index-BHzRuMlR.css">
+		<link rel="stylesheet" crossorigin href="./assets/index-DAK31IJJ.css">
 	</head>
 	<body class="litegraph">
 		<div id="vue-app"></div>