From b4c2d03d476f97d9d91c0d7d0af7fde382084385 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 5 Jul 2024 12:10:22 -0400
Subject: [PATCH 001/112] Remove duplicate import.

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

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

From ce649d61c05ab0e905155b4a010d4f0459a0db8d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 5 Jul 2024 23:48:17 -0400
Subject: [PATCH 002/112] Allow zeroing out of embeds with unused attention
 mask.

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

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

From 80c45909985aa37e6803ed59b6753fc289ee365f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 00:06:49 -0400
Subject: [PATCH 003/112] Allow specifying the padding token for the tokenizer.

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

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

From b8e58a939463be85877c1244ee00763368723c07 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 00:33:25 -0400
Subject: [PATCH 004/112] Cleanup T5 code a bit.

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

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

From 404049114913f1a134656769a538779d7c927249 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 00:53:33 -0400
Subject: [PATCH 005/112] Better T5xxl detection.

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

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

From ff63893d103d9c3168eaf9101fb3cebbc2abd8fa Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 02:42:53 -0400
Subject: [PATCH 006/112] Support other types of T5 models.

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

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

From 2dc84d14447782683862616eaf8c19c0c1feacf3 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 6 Jul 2024 04:06:03 -0400
Subject: [PATCH 007/112] Add a way to set the timestep multiplier in the flow
 sampling.

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

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

From 628f0b8ebc2c9a51205e5e5a9973f8db348a310f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 7 Jul 2024 09:22:32 -0400
Subject: [PATCH 008/112] Move audio nodes out of _for_testing.

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

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

From bb663bcd6c81a4395100c5f8527a221037d366f8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 8 Jul 2024 08:48:38 -0400
Subject: [PATCH 009/112] Rename clip_t5base to t5base for stable audio text
 encoder.

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

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

From faa57430b0ff882275b1afcf6610e8e9f8a5929b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 8 Jul 2024 23:26:34 -0400
Subject: [PATCH 010/112] Controlnet union model basic implementation.

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

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

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

From ade7aa1b0ce7944f50f66af5d8742299ac49d07f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 9 Jul 2024 11:05:05 -0400
Subject: [PATCH 011/112] Remove useless import.

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

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

From d160073829b836fce97046ebb5f145fa1c677ae3 Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Tue, 9 Jul 2024 08:23:26 -0700
Subject: [PATCH 012/112] Fix loadGraphData call during restore (#3976)

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

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

From c3db344746163f9ab942591020043d47ca34e4f0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 9 Jul 2024 11:52:31 -0400
Subject: [PATCH 013/112] Fix ConditioningZeroOut when there is no pooled
 output.

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

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

From f1a01c2c7e2678f34c72212d2a8640237f31fa43 Mon Sep 17 00:00:00 2001
From: Extraltodeus <extraltodeus@gmail.com>
Date: Tue, 9 Jul 2024 22:20:49 +0200
Subject: [PATCH 014/112] Add sampler_pre_cfg_function (#3979)

* Update samplers.py

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

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

From 83f70a88fbc4866050dae90d78ec66aebe8e341a Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 9 Jul 2024 17:07:15 -0400
Subject: [PATCH 015/112] Add __module__ to node info (#3936)

Use more explicit name 'python_module'

Parse abs ath

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

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

From 8d3f979b633dab9824c59bf8a06c748a533eec0b Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 9 Jul 2024 17:12:57 -0400
Subject: [PATCH 016/112] Check unhandled exception in test log in test action
 (#3987)

* Upload console logs

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

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

From 90389b3b8a69c08c3ed0bcc9d87a92246578a8e3 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Wed, 10 Jul 2024 11:28:15 -0400
Subject: [PATCH 017/112] Update bug issue template (#3996)

* Update issue template

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

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

From e44fa5667fd0a5469b1b5efa08187282779f4d44 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 10 Jul 2024 19:31:22 -0400
Subject: [PATCH 018/112] Support returning text encoder attention masks.

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

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

From 391c1046cff8a3877a2ba343057579ab4278c5b1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 10 Jul 2024 20:06:50 -0400
Subject: [PATCH 019/112] More flexibility with text encoder return values.

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

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

From ffe0bb0a33a8a8d9a999dafb540558646127d443 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 10 Jul 2024 20:33:12 -0400
Subject: [PATCH 020/112] Remove useless code.

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

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

From 5e1fced6392ca4fef404e01094fbcbec77bdd31f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 11:37:31 -0400
Subject: [PATCH 021/112] Cleaner support for loading different diffusion model
 types.

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

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

From f45157e3acbef1c0edf7d31fc73ee83e7913a994 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 11:46:51 -0400
Subject: [PATCH 022/112] Fix error message never being shown.

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

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

From 9f291d75b344d7f7e83b7bd18447f430c99fcb28 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 16:51:06 -0400
Subject: [PATCH 023/112] AuraFlow model implementation.

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

diff --git a/comfy/ldm/aura/mmdit.py b/comfy/ldm/aura/mmdit.py
new file mode 100644
index 000000000..c465619bd
--- /dev/null
+++ b/comfy/ldm/aura/mmdit.py
@@ -0,0 +1,479 @@
+#AuraFlow MMDiT
+#Originally written by the AuraFlow Authors
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from comfy.ldm.modules.attention import optimized_attention
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+def find_multiple(n: int, k: int) -> int:
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim=None, dtype=None, device=None, operations=None) -> None:
+        super().__init__()
+        if hidden_dim is None:
+            hidden_dim = 4 * dim
+
+        n_hidden = int(2 * hidden_dim / 3)
+        n_hidden = find_multiple(n_hidden, 256)
+
+        self.c_fc1 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+        self.c_fc2 = operations.Linear(dim, n_hidden, bias=False, dtype=dtype, device=device)
+        self.c_proj = operations.Linear(n_hidden, dim, bias=False, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.silu(self.c_fc1(x)) * self.c_fc2(x)
+        x = self.c_proj(x)
+        return x
+
+
+class MultiHeadLayerNorm(nn.Module):
+    def __init__(self, hidden_size=None, eps=1e-5, dtype=None, device=None):
+        # Copy pasta from https://github.com/huggingface/transformers/blob/e5f71ecaae50ea476d1e12351003790273c4b2ed/src/transformers/models/cohere/modeling_cohere.py#L78
+
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        mean = hidden_states.mean(-1, keepdim=True)
+        variance = (hidden_states - mean).pow(2).mean(-1, keepdim=True)
+        hidden_states = (hidden_states - mean) * torch.rsqrt(
+            variance + self.variance_epsilon
+        )
+        hidden_states = self.weight.to(torch.float32) * hidden_states
+        return hidden_states.to(input_dtype)
+
+class SingleAttention(nn.Module):
+    def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.n_heads = n_heads
+        self.head_dim = dim // n_heads
+
+        # this is for cond
+        self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        self.q_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+    #@torch.compile()
+    def forward(self, c):
+
+        bsz, seqlen1, _ = c.shape
+
+        q, k, v = self.w1q(c), self.w1k(c), self.w1v(c)
+        q = q.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        k = k.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        q, k = self.q_norm1(q), self.k_norm1(k)
+
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
+        c = self.w1o(output)
+        return c
+
+
+
+class DoubleAttention(nn.Module):
+    def __init__(self, dim, n_heads, mh_qknorm=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.n_heads = n_heads
+        self.head_dim = dim // n_heads
+
+        # this is for cond
+        self.w1q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w1o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        # this is for x
+        self.w2q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2k = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2v = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+        self.w2o = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        self.q_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm1 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+        self.q_norm2 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+        self.k_norm2 = (
+            MultiHeadLayerNorm((self.n_heads, self.head_dim), dtype=dtype, device=device)
+            if mh_qknorm
+            else operations.LayerNorm(self.head_dim, elementwise_affine=False, dtype=dtype, device=device)
+        )
+
+
+    #@torch.compile()
+    def forward(self, c, x):
+
+        bsz, seqlen1, _ = c.shape
+        bsz, seqlen2, _ = x.shape
+        seqlen = seqlen1 + seqlen2
+
+        cq, ck, cv = self.w1q(c), self.w1k(c), self.w1v(c)
+        cq = cq.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        ck = ck.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        cv = cv.view(bsz, seqlen1, self.n_heads, self.head_dim)
+        cq, ck = self.q_norm1(cq), self.k_norm1(ck)
+
+        xq, xk, xv = self.w2q(x), self.w2k(x), self.w2v(x)
+        xq = xq.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen2, self.n_heads, self.head_dim)
+        xq, xk = self.q_norm2(xq), self.k_norm2(xk)
+
+        # concat all
+        q, k, v = (
+            torch.cat([cq, xq], dim=1),
+            torch.cat([ck, xk], dim=1),
+            torch.cat([cv, xv], dim=1),
+        )
+
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
+
+        c, x = output.split([seqlen1, seqlen2], dim=1)
+        c = self.w1o(c)
+        x = self.w2o(x)
+
+        return c, x
+
+
+class MMDiTBlock(nn.Module):
+    def __init__(self, dim, heads=8, global_conddim=1024, is_last=False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.normC1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.normC2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        if not is_last:
+            self.mlpC = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+            self.modC = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+            )
+        else:
+            self.modC = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+            )
+
+        self.normX1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.normX2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.mlpX = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+        self.modX = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.attn = DoubleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+        self.is_last = is_last
+
+    #@torch.compile()
+    def forward(self, c, x, global_cond, **kwargs):
+
+        cres, xres = c, x
+
+        cshift_msa, cscale_msa, cgate_msa, cshift_mlp, cscale_mlp, cgate_mlp = (
+            self.modC(global_cond).chunk(6, dim=1)
+        )
+
+        c = modulate(self.normC1(c), cshift_msa, cscale_msa)
+
+        # xpath
+        xshift_msa, xscale_msa, xgate_msa, xshift_mlp, xscale_mlp, xgate_mlp = (
+            self.modX(global_cond).chunk(6, dim=1)
+        )
+
+        x = modulate(self.normX1(x), xshift_msa, xscale_msa)
+
+        # attention
+        c, x = self.attn(c, x)
+
+
+        c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
+        c = cgate_mlp.unsqueeze(1) * self.mlpC(modulate(c, cshift_mlp, cscale_mlp))
+        c = cres + c
+
+        x = self.normX2(xres + xgate_msa.unsqueeze(1) * x)
+        x = xgate_mlp.unsqueeze(1) * self.mlpX(modulate(x, xshift_mlp, xscale_mlp))
+        x = xres + x
+
+        return c, x
+
+class DiTBlock(nn.Module):
+    # like MMDiTBlock, but it only has X
+    def __init__(self, dim, heads=8, global_conddim=1024, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.norm1 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+        self.norm2 = operations.LayerNorm(dim, elementwise_affine=False, dtype=dtype, device=device)
+
+        self.modCX = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 6 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.attn = SingleAttention(dim, heads, dtype=dtype, device=device, operations=operations)
+        self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
+
+    #@torch.compile()
+    def forward(self, cx, global_cond, **kwargs):
+        cxres = cx
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
+            global_cond
+        ).chunk(6, dim=1)
+        cx = modulate(self.norm1(cx), shift_msa, scale_msa)
+        cx = self.attn(cx)
+        cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
+        mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
+        cx = gate_mlp.unsqueeze(1) * mlpout
+
+        cx = cxres + cx
+
+        return cx
+
+
+
+class TimestepEmbedder(nn.Module):
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        half = dim // 2
+        freqs = 1000 * torch.exp(
+            -math.log(max_period) * torch.arange(start=0, end=half) / half
+        ).to(t.device)
+        args = t[:, None] * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        return embedding
+
+    #@torch.compile()
+    def forward(self, t, dtype):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class MMDiT(nn.Module):
+    def __init__(
+        self,
+        in_channels=4,
+        out_channels=4,
+        patch_size=2,
+        dim=3072,
+        n_layers=36,
+        n_double_layers=4,
+        n_heads=12,
+        global_conddim=3072,
+        cond_seq_dim=2048,
+        max_seq=32 * 32,
+        device=None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+
+        self.t_embedder = TimestepEmbedder(global_conddim, dtype=dtype, device=device, operations=operations)
+
+        self.cond_seq_linear = operations.Linear(
+            cond_seq_dim, dim, bias=False, dtype=dtype, device=device
+        )  # linear for something like text sequence.
+        self.init_x_linear = operations.Linear(
+            patch_size * patch_size * in_channels, dim, dtype=dtype, device=device
+        )  # init linear for patchified image.
+
+        self.positional_encoding = nn.Parameter(torch.empty(1, max_seq, dim, dtype=dtype, device=device))
+        self.register_tokens = nn.Parameter(torch.empty(1, 8, dim, dtype=dtype, device=device))
+
+        self.double_layers = nn.ModuleList([])
+        self.single_layers = nn.ModuleList([])
+
+
+        for idx in range(n_double_layers):
+            self.double_layers.append(
+                MMDiTBlock(dim, n_heads, global_conddim, is_last=(idx == n_layers - 1), dtype=dtype, device=device, operations=operations)
+            )
+
+        for idx in range(n_double_layers, n_layers):
+            self.single_layers.append(
+                DiTBlock(dim, n_heads, global_conddim, dtype=dtype, device=device, operations=operations)
+            )
+
+
+        self.final_linear = operations.Linear(
+            dim, patch_size * patch_size * out_channels, bias=False, dtype=dtype, device=device
+        )
+
+        self.modF = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(global_conddim, 2 * dim, bias=False, dtype=dtype, device=device),
+        )
+
+        self.out_channels = out_channels
+        self.patch_size = patch_size
+        self.n_double_layers = n_double_layers
+        self.n_layers = n_layers
+
+        self.h_max = round(max_seq**0.5)
+        self.w_max = round(max_seq**0.5)
+
+    @torch.no_grad()
+    def extend_pe(self, init_dim=(16, 16), target_dim=(64, 64)):
+        # extend pe
+        pe_data = self.positional_encoding.data.squeeze(0)[: init_dim[0] * init_dim[1]]
+
+        pe_as_2d = pe_data.view(init_dim[0], init_dim[1], -1).permute(2, 0, 1)
+
+        # now we need to extend this to target_dim. for this we will use interpolation.
+        # we will use torch.nn.functional.interpolate
+        pe_as_2d = F.interpolate(
+            pe_as_2d.unsqueeze(0), size=target_dim, mode="bilinear"
+        )
+        pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
+        self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
+        self.h_max, self.w_max = target_dim
+        print("PE extended to", target_dim)
+
+    def pe_selection_index_based_on_dim(self, h, w):
+        h_p, w_p = h // self.patch_size, w // self.patch_size
+        original_pe_indexes = torch.arange(self.positional_encoding.shape[1])
+        original_pe_indexes = original_pe_indexes.view(self.h_max, self.w_max)
+        starth =  self.h_max // 2 - h_p // 2
+        endh =starth + h_p
+        startw = self.w_max // 2 - w_p // 2
+        endw = startw + w_p
+        original_pe_indexes = original_pe_indexes[
+            starth:endh, startw:endw
+        ]
+        return original_pe_indexes.flatten()
+
+    def unpatchify(self, x, h, w):
+        c = self.out_channels
+        p = self.patch_size
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum("nhwpqc->nchpwq", x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs
+
+    def patchify(self, x):
+        B, C, H, W = x.size()
+        pad_h = (self.patch_size - H % self.patch_size) % self.patch_size
+        pad_w = (self.patch_size - W % self.patch_size) % self.patch_size
+
+        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
+        x = x.view(
+            B,
+            C,
+            (H + 1) // self.patch_size,
+            self.patch_size,
+            (W + 1) // self.patch_size,
+            self.patch_size,
+        )
+        x = x.permute(0, 2, 4, 1, 3, 5).flatten(-3).flatten(1, 2)
+        return x
+
+    def apply_pos_embeds(self, x, h, w):
+        h = (h + 1) // self.patch_size
+        w = (w + 1) // self.patch_size
+        max_dim = max(h, w)
+
+        cur_dim = self.h_max
+        pos_encoding = self.positional_encoding.reshape(1, cur_dim, cur_dim, -1).to(device=x.device, dtype=x.dtype)
+
+        if max_dim > cur_dim:
+            pos_encoding = F.interpolate(pos_encoding.movedim(-1, 1), (max_dim, max_dim), mode="bilinear").movedim(1, -1)
+            cur_dim = max_dim
+
+        from_h = (cur_dim - h) // 2
+        from_w = (cur_dim - w) // 2
+        pos_encoding = pos_encoding[:,from_h:from_h+h,from_w:from_w+w]
+        return x + pos_encoding.reshape(1, -1, self.positional_encoding.shape[-1])
+
+    def forward(self, x, timestep, context, **kwargs):
+        # patchify x, add PE
+        b, c, h, w = x.shape
+
+        # pe_indexes = self.pe_selection_index_based_on_dim(h, w)
+        # print(pe_indexes, pe_indexes.shape)
+
+        x = self.init_x_linear(self.patchify(x))  # B, T_x, D
+        x = self.apply_pos_embeds(x, h, w)
+        # x = x + self.positional_encoding[:, : x.size(1)].to(device=x.device, dtype=x.dtype)
+        # x = x + self.positional_encoding[:, pe_indexes].to(device=x.device, dtype=x.dtype)
+
+        # process conditions for MMDiT Blocks
+        c_seq = context  # B, T_c, D_c
+        t = timestep
+
+        c = self.cond_seq_linear(c_seq)  # B, T_c, D
+        c = torch.cat([self.register_tokens.to(device=c.device, dtype=c.dtype).repeat(c.size(0), 1, 1), c], dim=1)
+
+        global_cond = self.t_embedder(t, x.dtype)  # B, D
+
+        if len(self.double_layers) > 0:
+            for layer in self.double_layers:
+                c, x = layer(c, x, global_cond, **kwargs)
+
+        if len(self.single_layers) > 0:
+            c_len = c.size(1)
+            cx = torch.cat([c, x], dim=1)
+            for layer in self.single_layers:
+                cx = layer(cx, global_cond, **kwargs)
+
+            x = cx[:, c_len:]
+
+        fshift, fscale = self.modF(global_cond).chunk(2, dim=1)
+
+        x = modulate(x, fshift, fscale)
+        x = self.final_linear(x)
+        x = self.unpatchify(x, (h + 1) // self.patch_size, (w + 1) // self.patch_size)[:,:,:h,:w]
+        return x
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 80f6667ec..0e0e69d3b 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -6,6 +6,7 @@ from comfy.ldm.cascade.stage_b import StageB
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+import comfy.ldm.aura.mmdit
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
 import comfy.model_management
@@ -598,6 +599,17 @@ class SD3(BaseModel):
             area = input_shape[0] * input_shape[2] * input_shape[3]
             return (area * 0.3) * (1024 * 1024)
 
+class AuraFlow(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.aura.mmdit.MMDiT)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
+
 
 class StableAudio1(BaseModel):
     def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index 783a03eb7..dd5bff82a 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -105,6 +105,12 @@ def detect_unet_config(state_dict, key_prefix):
         unet_config["audio_model"] = "dit1.0"
         return unet_config
 
+    if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
+        unet_config = {}
+        unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
+        unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
+        return unet_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -253,6 +259,8 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
 def unet_prefix_from_state_dict(state_dict):
     if "model.model.postprocess_conv.weight" in state_dict: #audio models
         unet_key_prefix = "model.model."
+    elif "model.double_layers.0.attn.w1q.weight" in state_dict: #aura flow
+        unet_key_prefix = "model."
     else:
         unet_key_prefix = "model.diffusion_model."
     return unet_key_prefix
diff --git a/comfy/sd.py b/comfy/sd.py
index eeffa423a..6028029d6 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -21,6 +21,7 @@ from . import sd2_clip
 from . import sdxl_clip
 from . import sd3_clip
 from . import sa_t5
+import comfy.text_encoders.aura_t5
 
 import comfy.model_patcher
 import comfy.lora
@@ -415,6 +416,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             if weight.shape[-1] == 4096:
                 clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
                 clip_target.tokenizer = sd3_clip.SD3Tokenizer
+            elif weight.shape[-1] == 2048:
+                clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
+                clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
         elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
             clip_target.clip = sa_t5.SAT5Model
             clip_target.tokenizer = sa_t5.SAT5Tokenizer
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 21fdb7ec7..ccf8c333e 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -7,6 +7,7 @@ from . import sd2_clip
 from . import sdxl_clip
 from . import sd3_clip
 from . import sa_t5
+import comfy.text_encoders.aura_t5
 
 from . import supported_models_base
 from . import latent_formats
@@ -556,7 +557,28 @@ class StableAudio(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
 
+class AuraFlow(supported_models_base.BASE):
+    unet_config = {
+        "cond_seq_dim": 2048,
+    }
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio]
+    sampling_settings = {
+        "multiplier": 1.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.SDXL
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.AuraFlow(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow]
 
 models += [SVD_img2vid]
diff --git a/comfy/text_encoders/aura_t5.py b/comfy/text_encoders/aura_t5.py
new file mode 100644
index 000000000..0e84189aa
--- /dev/null
+++ b/comfy/text_encoders/aura_t5.py
@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from transformers import LlamaTokenizerFast
+import comfy.t5
+import os
+
+class PT5XlModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+
+class PT5XlTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=LlamaTokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1)
+
+class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer)
+
+class AuraT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, **kwargs):
+        super().__init__(device=device, dtype=dtype, name="pile_t5xl", clip_model=PT5XlModel, **kwargs)
diff --git a/comfy/text_encoders/t5_pile_config_xl.json b/comfy/text_encoders/t5_pile_config_xl.json
new file mode 100644
index 000000000..ee4e03f97
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_config_xl.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 5120,
+  "d_kv": 64,
+  "d_model": 2048,
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new file mode 100644
index 000000000..3f5132007
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diff --git a/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json b/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json
new file mode 100644
index 000000000..19fb1d5f4
--- /dev/null
+++ b/comfy/text_encoders/t5_pile_tokenizer/special_tokens_map.json
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HcmV?d00001

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

From 8e012043a9d0af3979bbe2cea8dc1ec7768f9d88 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 17:51:56 -0400
Subject: [PATCH 024/112] Add a ModelSamplingAuraFlow node to change the shift
 value.

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

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

From b6f09cf649a8e06783f148a0f5824405ed460003 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 11 Jul 2024 22:58:03 -0400
Subject: [PATCH 025/112] Add sentencepiece dependency.

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

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

From 29c2e26724d4982a3e33114eb9064f1a11f4f4ed Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 12 Jul 2024 01:08:45 -0400
Subject: [PATCH 026/112] Better tokenizing code for AuraFlow.

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

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

From 4ca9b9cc29fefaa899cba67d61a8252ae9f16c0d Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Fri, 12 Jul 2024 10:33:57 -0700
Subject: [PATCH 027/112] Add Github Workflow for releasing stable versions and
 standalone bundle. (#3949)

* Add stable release.

* Only build CUDA 12.1 + 3.11 Python.

* Upgrade checkout and setup-python to latest version.

* lzma2

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

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

From ce2473bb016748a4d98e9bce02d675b4fc22f4ac Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 12 Jul 2024 15:25:07 -0400
Subject: [PATCH 028/112] Add link to AuraFlow example in Readme.

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

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

From a3dffc447a7b9f4948037a988fd143eeffd87bd1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 13 Jul 2024 13:51:40 -0400
Subject: [PATCH 029/112] Support AuraFlow Lora and loading model weights in
 diffusers format.

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

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

From 79547efb65ebb41a964ce7686170c4e6c3641eb2 Mon Sep 17 00:00:00 2001
From: pythongosssss <125205205+pythongosssss@users.noreply.github.com>
Date: Sun, 14 Jul 2024 07:04:40 +0100
Subject: [PATCH 030/112] New menu fixes - fix send to workflow (#3909)

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

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

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

From 7914c47d5afb1c5ffab49d665f9a1ac86a458821 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 14 Jul 2024 10:07:36 -0400
Subject: [PATCH 031/112] Quick fix for the promax controlnet.

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

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

From 1305fb294ca69d0a44d88c5bf7ce8c682abd0c8a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 15 Jul 2024 17:36:24 -0400
Subject: [PATCH 032/112] Refactor: Move some code to the comfy/text_encoders
 folder.

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

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

From 136c93cb477e4c5c308791746583499805ab29aa Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 15 Jul 2024 20:01:49 -0400
Subject: [PATCH 033/112] Fix bug with workflow not registering change.

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

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

From 33346fd9b8856942187ad8c818498a080a2027b5 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 15 Jul 2024 20:36:03 -0400
Subject: [PATCH 034/112] Fix bug with custom nodes on other drives.

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

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

From 99458e8aca3c48c7b2de3c404f66536991c37182 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 16 Jul 2024 11:26:11 -0400
Subject: [PATCH 035/112] Add `FrontendManager` to manage non-default front-end
 impl (#3897)

* Add frontend manager

* Add tests

* nit

* Add unit test to github CI

* Fix path

* nit

* ignore

* Add logging

* Install test deps

* Remove 'stable' keyword support

* Update test

* Add web-root arg

* Rename web-root to front-end-root

* Add test on non-exist version number

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

* Inline cmd args

* nit

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

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

From e1630391d6d190c3dd9ef4609f0c84af72be54af Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 16 Jul 2024 11:29:38 -0400
Subject: [PATCH 036/112] Allow version names like v0.0.1 for the
 FrontendManager.

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

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

From 821f93872ecf3f1769b8abebd640a804839f4ab9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 16 Jul 2024 15:18:24 -0400
Subject: [PATCH 037/112] Allow model sampling to set number of timesteps.

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

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

From 8270c625301034767c77b7d7b1e5a135172d1ebb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 16 Jul 2024 17:01:40 -0400
Subject: [PATCH 038/112] Add SetUnionControlNetType to set the type of the
 union controlnet model.

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

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

From 60383f3b64aa4477edfea325879bb04d3695161c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 16 Jul 2024 17:08:25 -0400
Subject: [PATCH 039/112] Move controlnet nodes to conditioning/controlnet.

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

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

From f2298799ba58e915a1f22fec336d455c0913f409 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 16 Jul 2024 18:20:39 -0400
Subject: [PATCH 040/112] Fix annotation (#4035)

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

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

From c5a48b15bddab089f0054af0ed72eea868263ef6 Mon Sep 17 00:00:00 2001
From: Thomas Ward <teward@thomas-ward.net>
Date: Tue, 16 Jul 2024 18:27:09 -0400
Subject: [PATCH 041/112] Make default hash lib configurable without code
 changes via CLI argument (#3947)

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

* server.py: compare_image_hash configurable hash function

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

* Add hasher() to node_helpers

* hashlib selection moved to node_helpers

* default-hashing-function instead of dupe checking hasher

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

* Use args.default_hashing_function

* Use safer handling for node_helpers.hasher()

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

* Stray parentheses are evil.

* Indentation fix.

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

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

From 1cde6b2eff5721175725e9cc5f80191d8276e1e8 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Tue, 16 Jul 2024 21:15:08 -0400
Subject: [PATCH 042/112] Disallow use of eval with pylint (#4033)

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

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

From 281ad42df4fae74ed11c1e7ff14cff6b7db6498c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 17 Jul 2024 10:16:31 -0400
Subject: [PATCH 043/112] Fix lowvram union controlnet bug.

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

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

From 6f7869f365ca4ec34b017f9002b9dcb9bce56b1a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 17 Jul 2024 13:05:38 -0400
Subject: [PATCH 044/112] Get clip vision image size from config.

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

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

From 855789403b346c7d7772e2c7ed0a6a327f56047f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=96=B5=E5=93=A9=E4=B8=AA=E5=92=AA?= <wailovet@163.com>
Date: Thu, 18 Jul 2024 01:12:50 +0800
Subject: [PATCH 045/112] support clip-vit-large-patch14-336 (#4042)

* support clip-vit-large-patch14-336

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

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

From 374e093e09c94b528d7c9dfc337c65cc5c433ee3 Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Wed, 17 Jul 2024 13:11:10 -0700
Subject: [PATCH 046/112] Disable audio widget trying to get previews (#4044)

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

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

From ff6ca2a892c25c5c6edb25c0c674445a7fe6b562 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 18 Jul 2024 17:20:05 -0400
Subject: [PATCH 047/112] Move PAG to model_patches/unet section.

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

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

From 011b11d8d767da3355555fe1253b162f55e985c5 Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Thu, 18 Jul 2024 18:59:18 -0700
Subject: [PATCH 048/112] LoadAudio restores file value from workflow (#4043)

* LoadAudio restores file value from workflow

* use onAfterGraphConfigured

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

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

From 6ab8cad22ee376c0a9a506924cca278bb650afcc Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 19 Jul 2024 17:44:56 -0400
Subject: [PATCH 049/112] Implement beta sampling scheduler.

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

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

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

From 11b74147ee67944c1707b4cc0b4db1cedc73aacc Mon Sep 17 00:00:00 2001
From: Greg Wainer <gregcebowainer@hotmail.com>
Date: Fri, 19 Jul 2024 17:39:04 -0500
Subject: [PATCH 050/112] Fix/webp exif little endian (#4061)

* Fix for isLittleEndian flag in parseExifData.

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

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

From 95fa9545f167ccf4010849c70045a67a8800aa31 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 20 Jul 2024 12:27:42 -0400
Subject: [PATCH 051/112] Only append zero to noise schedule if last sigma
 isn't zero.

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

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

From 5b69cfe7c343e8672fc350ec35e17f0d046297ca Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Sun, 21 Jul 2024 15:29:10 -0400
Subject: [PATCH 052/112] Add timestamp to execution messages (#4076)

* Add timestamp to execution messages

* Add execution_end message

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

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

From f836e69346844c65dcf2418346db9a9a9b32a445 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 21 Jul 2024 16:16:45 -0400
Subject: [PATCH 053/112] Fix bug with SaveAudio node with --gpu-only

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

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

From 6045ed31f898e278e4693f2a4e210393fc9153d0 Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Sun, 21 Jul 2024 21:15:01 -0400
Subject: [PATCH 054/112] Supress frontend exception on unhandled message type
 (#4078)

* Supress frontend exception on unhandled message type

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

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

From 4151fbfa8ab705d0a7e632c7d7c23ea5c436a60b Mon Sep 17 00:00:00 2001
From: Chenlei Hu <chenlei.hu@mail.utoronto.ca>
Date: Mon, 22 Jul 2024 11:27:32 -0400
Subject: [PATCH 055/112] Add error message on union controlnet (#4081)

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

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

From b2c995f623fd7169db87dfd6d356e535d0bb99ce Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 22 Jul 2024 11:30:38 -0400
Subject: [PATCH 056/112] "auto" type is only relevant to the
 SetUnionControlNetType node.

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

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

From 14764aa2e2e2b282c4a4dffbfab4c01d3e46e8a7 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 22 Jul 2024 12:21:45 -0400
Subject: [PATCH 057/112] Rename LLAMATokenizer to SPieceTokenizer.

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

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

From 334ba48cea2961994e92c2fb25de9417b19897ed Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 23 Jul 2024 14:13:32 -0400
Subject: [PATCH 058/112] More generic unet prefix detection code.

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

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

From 88ed89303463ab030654ef87f7f54d72ee3979bb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 23 Jul 2024 14:17:42 -0400
Subject: [PATCH 059/112] Allow SPieceTokenizer to load model from a byte
 string.

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

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

From 0a4c49c57ca792535174d27aea87449257527b2f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 23 Jul 2024 15:35:28 -0400
Subject: [PATCH 060/112] Support MT5.

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

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

From 10b43ceea52d86395d10a7029eda2822fd65dfd1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 24 Jul 2024 01:12:59 -0400
Subject: [PATCH 061/112] Remove duplicate code.

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

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

From 19944ad252106e46a40e0b952c86c1cebc8486ab Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 24 Jul 2024 12:49:29 -0400
Subject: [PATCH 062/112] Add code to fix issues with new pytorch version on
 the standalone.

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

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

From ce80e69fb89f5f8e48273977e94b7a3b0421f6e6 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 24 Jul 2024 13:50:34 -0400
Subject: [PATCH 063/112] Avoid loading the dll when it's not necessary.

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

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

From 10c919f4c77b3615f0efa9014e8b77f294c23a2d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 24 Jul 2024 16:43:53 -0400
Subject: [PATCH 064/112] Make it possible to load tokenizer data from
 checkpoints.

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

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

From f87810cd3ed2cc3922811422181d0572f98b103d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 25 Jul 2024 10:52:09 -0400
Subject: [PATCH 065/112] Let tokenizers return weights to be stored in the
 saved checkpoint.

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

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

From a5f4292f9f41ab78759e6e2de490fe6240f5d9ea Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Thu, 25 Jul 2024 18:21:08 -0400
Subject: [PATCH 066/112] Basic hunyuan dit implementation. (#4102)

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

* Basic hunyuan dit implementation.

* Fix some resolutions not working.

* Support hydit checkpoint save.

* Init with right dtype.

* Switch to optimized attention in pooler.

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

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

From 61a2b00bc2f06dec3b570dfde2eb43890613d054 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 25 Jul 2024 19:06:43 -0400
Subject: [PATCH 067/112] Add HunyuanDiT support to readme.

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

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

From 25b51b1a8b6be9c3dadd1d755c78394009c4d1d4 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 25 Jul 2024 22:42:54 -0400
Subject: [PATCH 068/112] Hunyuan DiT lora support.

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

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

From a9ac56fc0db5777de0edf2fe4b8ed628ccab1293 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 04:32:33 -0400
Subject: [PATCH 069/112] Own BertModel implementation that works with lowvram.

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

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

From afe732bef960d753661acb5b886ba42573dd3720 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 11:52:58 -0400
Subject: [PATCH 070/112] Hunyuan dit can now accept longer prompts.

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

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

From 8328a2d8cdabd0e42b856dd0193ebc24ea41c359 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 12:11:32 -0400
Subject: [PATCH 071/112] Let hunyuan dit work with all prompt lengths.

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

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

From b6779d8df310bcac115d9949fcc6c7502b4c9551 Mon Sep 17 00:00:00 2001
From: filtered <176114999+webfiltered@users.noreply.github.com>
Date: Sat, 27 Jul 2024 02:25:42 +1000
Subject: [PATCH 072/112] Fix undo incorrectly undoing text input (#4114)

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

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

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

From 6225a7827c17bc237167f8500029d282f4c91950 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 13:04:48 -0400
Subject: [PATCH 073/112] Add CLIPTextEncodeHunyuanDiT.

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

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

From cf4418b806af5f7f67e3ce5b6ee386360b410bbb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 13:07:39 -0400
Subject: [PATCH 074/112] Don't treat Bert model like CLIP.

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

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

From 17b41f622ef64ba9a9ce38da0f1038e718508dc6 Mon Sep 17 00:00:00 2001
From: Robin Huang <robin.j.huang@gmail.com>
Date: Fri, 26 Jul 2024 11:37:40 -0700
Subject: [PATCH 075/112] Change windows standalone URL to stable release.
 (#4065)

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

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

From 45a2842d7fe2ed92c3402c0a17b55bd46366b407 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Fri, 26 Jul 2024 14:52:00 -0400
Subject: [PATCH 076/112] Set stable releases as a prerelease initially.

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

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

From e746965c5051660ffa10330c00461094270f0207 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 27 Jul 2024 01:20:18 -0400
Subject: [PATCH 077/112] Update nightly package workflow.

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

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

From 07f6a1a685d16a1b7e2c4b05d94670d2543ec29e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 27 Jul 2024 03:15:22 -0400
Subject: [PATCH 078/112] Handle case in the updater when master branch is not
 in local repo.

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

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

From e6829e7ac5bef5db8099005b5b038c49e173e87c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 27 Jul 2024 04:41:46 -0400
Subject: [PATCH 079/112] Add a way to set custom dependencies in the release
 workflow.

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

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

From f82d09c9b40fd9ebbc080bc5662ddb39787b1ec9 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sat, 27 Jul 2024 04:48:19 -0400
Subject: [PATCH 080/112] Update packaging workflow.

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

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

From 93000580261971971ebb12aff03f6bc3ce30a9f2 Mon Sep 17 00:00:00 2001
From: Silver <65376327+silveroxides@users.noreply.github.com>
Date: Sat, 27 Jul 2024 22:19:50 +0200
Subject: [PATCH 081/112] Add dpmpp_2s_ancestral as custom sampler (#4101)

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

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

From ab76abc7676ea30726661b85c4fb85f84a3ff5aa Mon Sep 17 00:00:00 2001
From: bymyself <abolkonsky.rem@gmail.com>
Date: Sat, 27 Jul 2024 20:34:19 -0700
Subject: [PATCH 082/112] Active workflow use primary fg color (#4090)

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

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

From 4ba7fa0244badcf901f2b8ddbfb8539c6398672f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Sun, 28 Jul 2024 01:19:20 -0400
Subject: [PATCH 083/112] Refactor: Move sd2_clip.py to text_encoders folder.

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

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

From c75b50607b4ab78ef9c7c5c9e0c8672146ead91b Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 29 Jul 2024 11:15:37 -0400
Subject: [PATCH 084/112] Less confusing exception if pillow() function fails.

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

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

From 66d35c07ce44b07011314ad7a28b2bdbcbb4e4cc Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Mon, 29 Jul 2024 20:27:40 -0400
Subject: [PATCH 085/112] Improve artifacts on hydit, auraflow and SD3 on
 specific resolutions.

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

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

From 79040635dace9466a9f3fe20b5604b8e8e79f44f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 30 Jul 2024 05:01:34 -0400
Subject: [PATCH 086/112] Remove unnecessary code.

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

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

From 25853d0be8be6622195afaba2bc92e49e518bdcc Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 30 Jul 2024 05:03:20 -0400
Subject: [PATCH 087/112] Use common function for casting weights to input.

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

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

From 82cae45d44df3bd2d042c32d9b56cd3056bd0f7f Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Tue, 30 Jul 2024 14:20:28 -0400
Subject: [PATCH 088/112] Fix potential issue with non clip text embeddings.

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

diff --git a/comfy/clip_config_bigg.json b/comfy/clip_config_bigg.json
index 32d82ff39..35261deef 100644
--- a/comfy/clip_config_bigg.json
+++ b/comfy/clip_config_bigg.json
@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1280,
   "initializer_factor": 1.0,
diff --git a/comfy/clip_model.py b/comfy/clip_model.py
index 14f43c568..ab775309c 100644
--- a/comfy/clip_model.py
+++ b/comfy/clip_model.py
@@ -87,6 +87,7 @@ class CLIPTextModel_(torch.nn.Module):
         heads = config_dict["num_attention_heads"]
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
+        self.eos_token_id = config_dict["eos_token_id"]
 
         super().__init__()
         self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
@@ -111,7 +112,7 @@ class CLIPTextModel_(torch.nn.Module):
         if i is not None and final_layer_norm_intermediate:
             i = self.final_layer_norm(i)
 
-        pooled_output = x[torch.arange(x.shape[0], device=x.device), input_tokens.to(dtype=torch.int, device=x.device).argmax(dim=-1),]
+        pooled_output = x[torch.arange(x.shape[0], device=x.device), (torch.round(input_tokens).to(dtype=torch.int, device=x.device) == self.eos_token_id).int().argmax(dim=-1),]
         return x, i, pooled_output
 
 class CLIPTextModel(torch.nn.Module):
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index c7bc1e4dc..f209bed41 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -140,15 +140,13 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
     def set_up_textual_embeddings(self, tokens, current_embeds):
         out_tokens = []
-        next_new_token = token_dict_size = current_embeds.weight.shape[0] - 1
+        next_new_token = token_dict_size = current_embeds.weight.shape[0]
         embedding_weights = []
 
         for x in tokens:
             tokens_temp = []
             for y in x:
                 if isinstance(y, numbers.Integral):
-                    if y == token_dict_size: #EOS token
-                        y = -1
                     tokens_temp += [int(y)]
                 else:
                     if y.shape[0] == current_embeds.weight.shape[1]:
@@ -164,11 +162,10 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         n = token_dict_size
         if len(embedding_weights) > 0:
             new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
-            new_embedding.weight[:token_dict_size] = current_embeds.weight[:-1]
+            new_embedding.weight[:token_dict_size] = current_embeds.weight
             for x in embedding_weights:
                 new_embedding.weight[n] = x
                 n += 1
-            new_embedding.weight[n] = current_embeds.weight[-1] #EOS embedding
             self.transformer.set_input_embeddings(new_embedding)
 
         processed_tokens = []
diff --git a/comfy/sd1_clip_config.json b/comfy/sd1_clip_config.json
index 0158a1fd5..3ba8c6b5b 100644
--- a/comfy/sd1_clip_config.json
+++ b/comfy/sd1_clip_config.json
@@ -6,7 +6,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "quick_gelu",
   "hidden_size": 768,
   "initializer_factor": 1.0,
diff --git a/comfy/text_encoders/sd2_clip_config.json b/comfy/text_encoders/sd2_clip_config.json
index 85cec832b..00893cfdc 100644
--- a/comfy/text_encoders/sd2_clip_config.json
+++ b/comfy/text_encoders/sd2_clip_config.json
@@ -5,7 +5,7 @@
   "attention_dropout": 0.0,
   "bos_token_id": 0,
   "dropout": 0.0,
-  "eos_token_id": 2,
+  "eos_token_id": 49407,
   "hidden_act": "gelu",
   "hidden_size": 1024,
   "initializer_factor": 1.0,

From b85216a3c0d4fc443c87cd2af362c1f4d3be50ce Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 31 Jul 2024 00:52:34 -0400
Subject: [PATCH 089/112] Lower T5 memory usage by a few hundred MB.

---
 comfy/ldm/hydit/models.py |  2 +-
 comfy/ops.py              | 33 ++++++++++++++++++++++++---------
 comfy/text_encoders/t5.py | 15 ++++++++-------
 3 files changed, 33 insertions(+), 17 deletions(-)

diff --git a/comfy/ldm/hydit/models.py b/comfy/ldm/hydit/models.py
index 37836fc31..c70dbf92a 100644
--- a/comfy/ldm/hydit/models.py
+++ b/comfy/ldm/hydit/models.py
@@ -355,7 +355,7 @@ class HunYuanDiT(nn.Module):
         if self.use_style_cond:
             if style is None:
                 style = torch.zeros((extra_vec.shape[0],), device=x.device, dtype=torch.int)
-            style_embedding = self.style_embedder(style)
+            style_embedding = self.style_embedder(style, out_dtype=x.dtype)
             extra_vec = torch.cat([extra_vec, style_embedding], dim=1)
 
         # Concatenate all extra vectors
diff --git a/comfy/ops.py b/comfy/ops.py
index 6d88a1b9b..47e8d7a9d 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -19,17 +19,27 @@
 import torch
 import comfy.model_management
 
-def cast_to_input(weight, input, non_blocking=False):
-    return weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
 
-def cast_bias_weight(s, input):
+def cast_to(weight, dtype=None, device=None, non_blocking=False):
+    return weight.to(device=device, dtype=dtype, non_blocking=non_blocking)
+
+def cast_to_input(weight, input, non_blocking=False):
+    return cast_to(weight, input.dtype, input.device, non_blocking=non_blocking)
+
+def cast_bias_weight(s, input=None, dtype=None, device=None):
+    if input is not None:
+        if dtype is None:
+            dtype = input.dtype
+        if device is None:
+            device = input.device
+
     bias = None
-    non_blocking = comfy.model_management.device_should_use_non_blocking(input.device)
+    non_blocking = comfy.model_management.device_should_use_non_blocking(device)
     if s.bias is not None:
-        bias = cast_to_input(s.bias, input, non_blocking=non_blocking)
+        bias = cast_to(s.bias, dtype, device, non_blocking=non_blocking)
         if s.bias_function is not None:
             bias = s.bias_function(bias)
-    weight = cast_to_input(s.weight, input, non_blocking=non_blocking)
+    weight = cast_to(s.weight, dtype, device, non_blocking=non_blocking)
     if s.weight_function is not None:
         weight = s.weight_function(weight)
     return weight, bias
@@ -176,14 +186,19 @@ class disable_weight_init:
             self.bias = None
             return None
 
-        def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
-            return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse)
+        def forward_comfy_cast_weights(self, input, out_dtype=None):
+            output_dtype = out_dtype
+            if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
+                out_dtype = None
+            weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
+            return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
 
         def forward(self, *args, **kwargs):
             if self.comfy_cast_weights:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
+                if "out_dtype" in kwargs:
+                    kwargs.pop("out_dtype")
                 return super().forward(*args, **kwargs)
 
     @classmethod
diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
index ba628478d..2109f4eaa 100644
--- a/comfy/text_encoders/t5.py
+++ b/comfy/text_encoders/t5.py
@@ -1,6 +1,7 @@
 import torch
 import math
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
 
 class T5LayerNorm(torch.nn.Module):
     def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
@@ -11,7 +12,7 @@ class T5LayerNorm(torch.nn.Module):
     def forward(self, x):
         variance = x.pow(2).mean(-1, keepdim=True)
         x = x * torch.rsqrt(variance + self.variance_epsilon)
-        return self.weight.to(device=x.device, dtype=x.dtype) * x
+        return comfy.ops.cast_to_input(self.weight, x) * x
 
 activations = {
     "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
@@ -82,7 +83,7 @@ class T5Attention(torch.nn.Module):
         if relative_attention_bias:
             self.relative_attention_num_buckets = 32
             self.relative_attention_max_distance = 128
-            self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
+            self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype)
 
     @staticmethod
     def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
@@ -132,7 +133,7 @@ class T5Attention(torch.nn.Module):
         relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
         return relative_buckets
 
-    def compute_bias(self, query_length, key_length, device):
+    def compute_bias(self, query_length, key_length, device, dtype):
         """Compute binned relative position bias"""
         context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
         memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
@@ -143,7 +144,7 @@ class T5Attention(torch.nn.Module):
             num_buckets=self.relative_attention_num_buckets,
             max_distance=self.relative_attention_max_distance,
         )
-        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
+        values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype)  # shape (query_length, key_length, num_heads)
         values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
         return values
 
@@ -152,7 +153,7 @@ class T5Attention(torch.nn.Module):
         k = self.k(x)
         v = self.v(x)
         if self.relative_attention_bias is not None:
-            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
+            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype)
 
         if past_bias is not None:
             if mask is not None:
@@ -225,7 +226,7 @@ class T5(torch.nn.Module):
 
         self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations)
         self.dtype = dtype
-        self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
+        self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype)
 
     def get_input_embeddings(self):
         return self.shared
@@ -234,5 +235,5 @@ class T5(torch.nn.Module):
         self.shared = embeddings
 
     def forward(self, input_ids, *args, **kwargs):
-        x = self.shared(input_ids)
+        x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
         return self.encoder(x, *args, **kwargs)

From 2c038ccef0f819ee8693a94dd880f05a4eb3808c Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 31 Jul 2024 01:32:35 -0400
Subject: [PATCH 090/112] Lower CLIP memory usage by a bit.

---
 comfy/clip_model.py         | 23 ++++++++++++-----------
 comfy/sd1_clip.py           |  7 ++++---
 comfy/text_encoders/bert.py | 21 +++++++++++----------
 comfy/text_encoders/t5.py   |  2 +-
 4 files changed, 28 insertions(+), 25 deletions(-)

diff --git a/comfy/clip_model.py b/comfy/clip_model.py
index ab775309c..3c67b737a 100644
--- a/comfy/clip_model.py
+++ b/comfy/clip_model.py
@@ -1,5 +1,6 @@
 import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
 
 class CLIPAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -71,13 +72,13 @@ class CLIPEncoder(torch.nn.Module):
         return x, intermediate
 
 class CLIPEmbeddings(torch.nn.Module):
-    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None):
+    def __init__(self, embed_dim, vocab_size=49408, num_positions=77, dtype=None, device=None, operations=None):
         super().__init__()
-        self.token_embedding = torch.nn.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.token_embedding = operations.Embedding(vocab_size, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens):
-        return self.token_embedding(input_tokens) + self.position_embedding.weight
+    def forward(self, input_tokens, dtype=torch.float32):
+        return self.token_embedding(input_tokens, out_dtype=dtype) + comfy.ops.cast_to(self.position_embedding.weight, dtype=dtype, device=input_tokens.device)
 
 
 class CLIPTextModel_(torch.nn.Module):
@@ -90,12 +91,12 @@ class CLIPTextModel_(torch.nn.Module):
         self.eos_token_id = config_dict["eos_token_id"]
 
         super().__init__()
-        self.embeddings = CLIPEmbeddings(embed_dim, dtype=torch.float32, device=device)
+        self.embeddings = CLIPEmbeddings(embed_dim, dtype=dtype, device=device, operations=operations)
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.final_layer_norm = operations.LayerNorm(embed_dim, dtype=dtype, device=device)
 
-    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
-        x = self.embeddings(input_tokens)
+    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=torch.float32):
+        x = self.embeddings(input_tokens, dtype=dtype)
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
@@ -154,11 +155,11 @@ class CLIPVisionEmbeddings(torch.nn.Module):
 
         num_patches = (image_size // patch_size) ** 2
         num_positions = num_patches + 1
-        self.position_embedding = torch.nn.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
+        self.position_embedding = operations.Embedding(num_positions, embed_dim, dtype=dtype, device=device)
 
     def forward(self, pixel_values):
         embeds = self.patch_embedding(pixel_values).flatten(2).transpose(1, 2)
-        return torch.cat([self.class_embedding.to(embeds.device).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + self.position_embedding.weight.to(embeds.device)
+        return torch.cat([comfy.ops.cast_to_input(self.class_embedding, embeds).expand(pixel_values.shape[0], 1, -1), embeds], dim=1) + comfy.ops.cast_to_input(self.position_embedding.weight, embeds)
 
 
 class CLIPVision(torch.nn.Module):
@@ -170,7 +171,7 @@ class CLIPVision(torch.nn.Module):
         intermediate_size = config_dict["intermediate_size"]
         intermediate_activation = config_dict["hidden_act"]
 
-        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=torch.float32, device=device, operations=operations)
+        self.embeddings = CLIPVisionEmbeddings(embed_dim, config_dict["num_channels"], config_dict["patch_size"], config_dict["image_size"], dtype=dtype, device=device, operations=operations)
         self.pre_layrnorm = operations.LayerNorm(embed_dim)
         self.encoder = CLIPEncoder(num_layers, embed_dim, heads, intermediate_size, intermediate_activation, dtype, device, operations)
         self.post_layernorm = operations.LayerNorm(embed_dim)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index f209bed41..d32121d1b 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -94,7 +94,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         with open(textmodel_json_config) as f:
             config = json.load(f)
 
-        self.transformer = model_class(config, dtype, device, comfy.ops.manual_cast)
+        self.operations = comfy.ops.manual_cast
+        self.transformer = model_class(config, dtype, device, self.operations)
         self.num_layers = self.transformer.num_layers
 
         self.max_length = max_length
@@ -161,7 +162,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
 
         n = token_dict_size
         if len(embedding_weights) > 0:
-            new_embedding = torch.nn.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
+            new_embedding = self.operations.Embedding(next_new_token + 1, current_embeds.weight.shape[1], device=current_embeds.weight.device, dtype=current_embeds.weight.dtype)
             new_embedding.weight[:token_dict_size] = current_embeds.weight
             for x in embedding_weights:
                 new_embedding.weight[n] = x
@@ -194,7 +195,7 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         if self.enable_attention_masks:
             attention_mask_model = attention_mask
 
-        outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
+        outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32)
         self.transformer.set_input_embeddings(backup_embeds)
 
         if self.layer == "last":
diff --git a/comfy/text_encoders/bert.py b/comfy/text_encoders/bert.py
index b76e76660..fc9bac1d2 100644
--- a/comfy/text_encoders/bert.py
+++ b/comfy/text_encoders/bert.py
@@ -1,5 +1,6 @@
 import torch
 from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
 
 class BertAttention(torch.nn.Module):
     def __init__(self, embed_dim, heads, dtype, device, operations):
@@ -86,19 +87,19 @@ class BertEncoder(torch.nn.Module):
 class BertEmbeddings(torch.nn.Module):
     def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations):
         super().__init__()
-        self.word_embeddings = torch.nn.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
-        self.position_embeddings = torch.nn.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
-        self.token_type_embeddings = torch.nn.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
+        self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
+        self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
+        self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
 
         self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device)
 
-    def forward(self, input_tokens, token_type_ids=None):
-        x = self.word_embeddings(input_tokens)
-        x += self.position_embeddings.weight[:x.shape[1]]
+    def forward(self, input_tokens, token_type_ids=None, dtype=None):
+        x = self.word_embeddings(input_tokens, out_dtype=dtype)
+        x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x)
         if token_type_ids is not None:
-            x += self.token_type_embeddings(token_type_ids)
+            x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype)
         else:
-            x += self.token_type_embeddings.weight[0]
+            x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x)
         x = self.LayerNorm(x)
         return x
 
@@ -112,8 +113,8 @@ class BertModel_(torch.nn.Module):
         self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations)
         self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations)
 
-    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
-        x = self.embeddings(input_tokens)
+    def forward(self, input_tokens, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
+        x = self.embeddings(input_tokens, dtype=dtype)
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
index 2109f4eaa..b6491090a 100644
--- a/comfy/text_encoders/t5.py
+++ b/comfy/text_encoders/t5.py
@@ -200,7 +200,7 @@ class T5Stack(torch.nn.Module):
         self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
         # self.dropout = nn.Dropout(config.dropout_rate)
 
-    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
+    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])

From a5991a7aa6f5dae3af820151abe15cb63ac86ac8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 31 Jul 2024 01:34:57 -0400
Subject: [PATCH 091/112] Fix hunyuan dit text encoder weights always being in
 fp32.

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

diff --git a/comfy/text_encoders/hydit.py b/comfy/text_encoders/hydit.py
index ac0c893bc..9dfa288b1 100644
--- a/comfy/text_encoders/hydit.py
+++ b/comfy/text_encoders/hydit.py
@@ -52,8 +52,8 @@ class HyditTokenizer:
 class HyditModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None):
         super().__init__()
-        self.hydit_clip = HyditBertModel()
-        self.mt5xl = MT5XLModel()
+        self.hydit_clip = HyditBertModel(dtype=dtype)
+        self.mt5xl = MT5XLModel(dtype=dtype)
 
         self.dtypes = set()
         if dtype is not None:

From c24f897352238f040e162a81d253c290635d44fd Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Wed, 31 Jul 2024 02:00:19 -0400
Subject: [PATCH 092/112] Fix to get fp8 working on T5 base.

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

diff --git a/comfy/text_encoders/t5.py b/comfy/text_encoders/t5.py
index b6491090a..a1420c6cd 100644
--- a/comfy/text_encoders/t5.py
+++ b/comfy/text_encoders/t5.py
@@ -236,4 +236,6 @@ class T5(torch.nn.Module):
 
     def forward(self, input_ids, *args, **kwargs):
         x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
+        if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+            x = torch.nan_to_num(x) #Fix for fp8 T5 base
         return self.encoder(x, *args, **kwargs)

From e2382b6adb70c65416f3e90a168cbbc5ffe491bd Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 03:58:58 -0400
Subject: [PATCH 093/112] Make lowvram less aggressive when there are large
 amounts of free memory.

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

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 047193290..c54a360a7 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -446,7 +446,7 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
+            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - extra_mem)))
             if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 

From 7ad574bffd31b80c7c89c828c87e5b0557a29b99 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 09:42:17 -0400
Subject: [PATCH 094/112] Mac supports bf16 just make sure you are using the
 latest pytorch.

---
 comfy/model_management.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index c54a360a7..bcd86a033 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -649,12 +649,12 @@ def supports_cast(device, dtype): #TODO
         return True
     if dtype == torch.float16:
         return True
-    if is_device_mps(device):
-        return False
     if directml_enabled: #TODO: test this
         return False
     if dtype == torch.bfloat16:
         return True
+    if is_device_mps(device):
+        return False
     if dtype == torch.float8_e4m3fn:
         return True
     if dtype == torch.float8_e5m2:
@@ -876,9 +876,9 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
         if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow
             return False
 
-    if device is not None: #TODO not sure about mps bf16 support
+    if device is not None:
         if is_device_mps(device):
-            return False
+            return True
 
     if FORCE_FP32:
         return False

From 1589b58d3e29e44623a1f3f595917b98f2301c3e Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 04:03:59 -0400
Subject: [PATCH 095/112] Basic Flux Schnell and Flux Dev model implementation.

---
 comfy/latent_formats.py     |  11 ++
 comfy/ldm/flux/layers.py    | 257 ++++++++++++++++++++++++++++++++++++
 comfy/ldm/flux/math.py      |  29 ++++
 comfy/ldm/flux/model.py     | 136 +++++++++++++++++++
 comfy/model_base.py         |  21 +++
 comfy/model_detection.py    |  17 +++
 comfy/model_sampling.py     |  40 ++++++
 comfy/sd.py                 |   5 +
 comfy/supported_models.py   |  41 +++++-
 comfy/text_encoders/flux.py |  64 +++++++++
 folder_paths.py             |   2 +-
 nodes.py                    |   4 +-
 12 files changed, 624 insertions(+), 3 deletions(-)
 create mode 100644 comfy/ldm/flux/layers.py
 create mode 100644 comfy/ldm/flux/math.py
 create mode 100644 comfy/ldm/flux/model.py
 create mode 100644 comfy/text_encoders/flux.py

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 4b4a9eda2..34c7bb3da 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -139,3 +139,14 @@ class SD3(LatentFormat):
 
 class StableAudio1(LatentFormat):
     latent_channels = 64
+
+class Flux(SD3):
+    def __init__(self):
+        self.scale_factor = 0.3611
+        self.shift_factor = 0.1159
+
+    def process_in(self, latent):
+        return (latent - self.shift_factor) * self.scale_factor
+
+    def process_out(self, latent):
+        return (latent / self.scale_factor) + self.shift_factor
diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
new file mode 100644
index 000000000..bb5e02b6d
--- /dev/null
+++ b/comfy/ldm/flux/layers.py
@@ -0,0 +1,257 @@
+import math
+from dataclasses import dataclass
+
+import torch
+from einops import rearrange
+from torch import Tensor, nn
+
+from .math import attention, rope
+import comfy.ops
+
+
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: Tensor) -> Tensor:
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+            dim=-3,
+        )
+
+        return emb.unsqueeze(1)
+
+
+def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
+    """
+    Create sinusoidal timestep embeddings.
+    :param t: a 1-D Tensor of N indices, one per batch element.
+                      These may be fractional.
+    :param dim: the dimension of the output.
+    :param max_period: controls the minimum frequency of the embeddings.
+    :return: an (N, D) Tensor of positional embeddings.
+    """
+    t = time_factor * t
+    half = dim // 2
+    freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(
+        t.device
+    )
+
+    args = t[:, None].float() * freqs[None]
+    embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+    if dim % 2:
+        embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+    if torch.is_floating_point(t):
+        embedding = embedding.to(t)
+    return embedding
+
+
+class MLPEmbedder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.in_layer = operations.Linear(in_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+        self.silu = nn.SiLU()
+        self.out_layer = operations.Linear(hidden_dim, hidden_dim, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x: Tensor) -> Tensor:
+        return self.out_layer(self.silu(self.in_layer(x)))
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.scale = nn.Parameter(torch.empty((dim), dtype=dtype, device=device))
+
+    def forward(self, x: Tensor):
+        x_dtype = x.dtype
+        x = x.float()
+        rrms = torch.rsqrt(torch.mean(x**2, dim=-1, keepdim=True) + 1e-6)
+        return (x * rrms).to(dtype=x_dtype) * comfy.ops.cast_to(self.scale, dtype=x_dtype, device=x.device)
+
+
+class QKNorm(torch.nn.Module):
+    def __init__(self, dim: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+        self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+        q = self.query_norm(q)
+        k = self.key_norm(k)
+        return q.to(v), k.to(v)
+
+
+class SelfAttention(nn.Module):
+    def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+
+    def forward(self, x: Tensor, pe: Tensor) -> Tensor:
+        qkv = self.qkv(x)
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+        x = attention(q, k, v, pe=pe)
+        x = self.proj(x)
+        return x
+
+
+@dataclass
+class ModulationOut:
+    shift: Tensor
+    scale: Tensor
+    gate: Tensor
+
+
+class Modulation(nn.Module):
+    def __init__(self, dim: int, double: bool, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.is_double = double
+        self.multiplier = 6 if double else 3
+        self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
+
+    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+        out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
+
+        return (
+            ModulationOut(*out[:3]),
+            ModulationOut(*out[3:]) if self.is_double else None,
+        )
+
+
+class DoubleStreamBlock(nn.Module):
+    def __init__(self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        self.num_heads = num_heads
+        self.hidden_size = hidden_size
+        self.img_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        self.img_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.img_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.img_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+        self.txt_mod = Modulation(hidden_size, double=True, dtype=dtype, device=device, operations=operations)
+        self.txt_norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, dtype=dtype, device=device, operations=operations)
+
+        self.txt_norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.txt_mlp = nn.Sequential(
+            operations.Linear(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype, device=device),
+            nn.GELU(approximate="tanh"),
+            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
+        img_mod1, img_mod2 = self.img_mod(vec)
+        txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+        # prepare image for attention
+        img_modulated = self.img_norm1(img)
+        img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+        img_qkv = self.img_attn.qkv(img_modulated)
+        img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+        # prepare txt for attention
+        txt_modulated = self.txt_norm1(txt)
+        txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+        txt_qkv = self.txt_attn.qkv(txt_modulated)
+        txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+        # run actual attention
+        q = torch.cat((txt_q, img_q), dim=2)
+        k = torch.cat((txt_k, img_k), dim=2)
+        v = torch.cat((txt_v, img_v), dim=2)
+
+        attn = attention(q, k, v, pe=pe)
+        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
+
+        # calculate the img bloks
+        img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+        img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift)
+
+        # calculate the txt bloks
+        txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
+        txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift)
+        return img, txt
+
+
+class SingleStreamBlock(nn.Module):
+    """
+    A DiT block with parallel linear layers as described in
+    https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        qk_scale: float | None = None,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+        self.hidden_dim = hidden_size
+        self.num_heads = num_heads
+        head_dim = hidden_size // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        # qkv and mlp_in
+        self.linear1 = operations.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim, dtype=dtype, device=device)
+        # proj and mlp_out
+        self.linear2 = operations.Linear(hidden_size + self.mlp_hidden_dim, hidden_size, dtype=dtype, device=device)
+
+        self.norm = QKNorm(head_dim, dtype=dtype, device=device, operations=operations)
+
+        self.hidden_size = hidden_size
+        self.pre_norm = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.mlp_act = nn.GELU(approximate="tanh")
+        self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+        mod, _ = self.modulation(vec)
+        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+
+        q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
+        q, k = self.norm(q, k, v)
+
+        # compute attention
+        attn = attention(q, k, v, pe=pe)
+        # compute activation in mlp stream, cat again and run second linear layer
+        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+        return x + mod.gate * output
+
+
+class LastLayer(nn.Module):
+    def __init__(self, hidden_size: int, patch_size: int, out_channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device))
+
+    def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+        shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+        x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+        x = self.linear(x)
+        return x
diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
new file mode 100644
index 000000000..e4ef624ef
--- /dev/null
+++ b/comfy/ldm/flux/math.py
@@ -0,0 +1,29 @@
+import torch
+from einops import rearrange
+from torch import Tensor
+from comfy.ldm.modules.attention import optimized_attention
+
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
+    q, k = apply_rope(q, k, pe)
+
+    heads = q.shape[1]
+    x = optimized_attention(q, k, v, heads, skip_reshape=True)
+    return x
+
+
+def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
+    assert dim % 2 == 0
+    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+    omega = 1.0 / (theta**scale)
+    out = torch.einsum("...n,d->...nd", pos, omega)
+    out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
+    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+    return out.float()
+
+
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
new file mode 100644
index 000000000..f77834c15
--- /dev/null
+++ b/comfy/ldm/flux/model.py
@@ -0,0 +1,136 @@
+#Original code can be found on: https://github.com/black-forest-labs/flux
+
+from dataclasses import dataclass
+
+import torch
+from torch import Tensor, nn
+
+from .layers import (
+    DoubleStreamBlock,
+    EmbedND,
+    LastLayer,
+    MLPEmbedder,
+    SingleStreamBlock,
+    timestep_embedding,
+)
+
+from einops import rearrange, repeat
+
+@dataclass
+class FluxParams:
+    in_channels: int
+    vec_in_dim: int
+    context_in_dim: int
+    hidden_size: int
+    mlp_ratio: float
+    num_heads: int
+    depth: int
+    depth_single_blocks: int
+    axes_dim: list[int]
+    theta: int
+    qkv_bias: bool
+    guidance_embed: bool
+
+
+class Flux(nn.Module):
+    """
+    Transformer model for flow matching on sequences.
+    """
+
+    def __init__(self, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+        super().__init__()
+        self.dtype = dtype
+        params = FluxParams(**kwargs)
+        self.params = params
+        self.in_channels = params.in_channels
+        self.out_channels = self.in_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(
+                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
+            )
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
+        self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
+        self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size, dtype=dtype, device=device, operations=operations)
+        self.guidance_in = (
+            MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations) if params.guidance_embed else nn.Identity()
+        )
+        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio, dtype=dtype, device=device, operations=operations)
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels, dtype=dtype, device=device, operations=operations)
+
+    def forward_orig(
+        self,
+        img: Tensor,
+        img_ids: Tensor,
+        txt: Tensor,
+        txt_ids: Tensor,
+        timesteps: Tensor,
+        y: Tensor,
+        guidance: Tensor | None = None,
+    ) -> Tensor:
+        if img.ndim != 3 or txt.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
+        # running on sequences img
+        img = self.img_in(img)
+        vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
+        if self.params.guidance_embed:
+            if guidance is None:
+                raise ValueError("Didn't get guidance strength for guidance distilled model.")
+            vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
+
+        vec = vec + self.vector_in(y)
+        txt = self.txt_in(txt)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        pe = self.pe_embedder(ids)
+
+        for block in self.double_blocks:
+            img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
+
+        img = torch.cat((txt, img), 1)
+        for block in self.single_blocks:
+            img = block(img, vec=vec, pe=pe)
+        img = img[:, txt.shape[1] :, ...]
+
+        img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
+        return img
+
+    def forward(self, x, timestep, context, y, guidance, **kwargs):
+        bs, c, h, w = x.shape
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+
+        img_ids = torch.zeros((h // 2, w // 2, 3), device=x.device, dtype=x.dtype)
+        img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=x.device, dtype=x.dtype)[None, :]
+        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+        out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance)
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h // 2, w=w // 2, ph=2, pw=2)
diff --git a/comfy/model_base.py b/comfy/model_base.py
index 9d60c1fb4..7c7b4c3ff 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -10,6 +10,8 @@ import comfy.ldm.aura.mmdit
 import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
 import comfy.ldm.audio.embedders
+import comfy.ldm.flux.model
+
 import comfy.model_management
 import comfy.conds
 import comfy.ops
@@ -26,6 +28,7 @@ class ModelType(Enum):
     EDM = 5
     FLOW = 6
     V_PREDICTION_CONTINUOUS = 7
+    FLUX = 8
 
 
 from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
@@ -53,6 +56,9 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
         c = V_PREDICTION
         s = ModelSamplingContinuousV
+    elif model_type == ModelType.FLUX:
+        c = comfy.model_sampling.CONST
+        s = comfy.model_sampling.ModelSamplingFlux
 
     class ModelSampling(s, c):
         pass
@@ -681,3 +687,18 @@ class HunyuanDiT(BaseModel):
 
         out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
         return out
+
+class Flux(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux)
+
+    def encode_adm(self, **kwargs):
+        return kwargs["pooled_output"]
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+        return out
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index ea4959370..dda9797b7 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -128,6 +128,23 @@ def detect_unet_config(state_dict, key_prefix):
             unet_config["image_model"] = "hydit1"
         return unet_config
 
+    if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys: #Flux
+        dit_config = {}
+        dit_config["image_model"] = "flux"
+        dit_config["in_channels"] = 64
+        dit_config["vec_in_dim"] = 768
+        dit_config["context_in_dim"] = 4096
+        dit_config["hidden_size"] = 3072
+        dit_config["mlp_ratio"] = 4.0
+        dit_config["num_heads"] = 24
+        dit_config["depth"] = 19
+        dit_config["depth_single_blocks"] = 38
+        dit_config["axes_dim"] = [16, 56, 56]
+        dit_config["theta"] = 10000
+        dit_config["qkv_bias"] = True
+        dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
+        return dit_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index 25bb7e043..4a0f2db60 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -272,3 +272,43 @@ class StableCascadeSampling(ModelSamplingDiscrete):
 
         percent = 1.0 - percent
         return self.sigma(torch.tensor(percent))
+
+
+def flux_time_shift(mu: float, sigma: float, t):
+    return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
+class ModelSamplingFlux(torch.nn.Module):
+    def __init__(self, model_config=None):
+        super().__init__()
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(shift=sampling_settings.get("shift", 1.15))
+
+    def set_parameters(self, shift=1.15, timesteps=10000):
+        self.shift = shift
+        ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps))
+        self.register_buffer('sigmas', ts)
+
+    @property
+    def sigma_min(self):
+        return self.sigmas[0]
+
+    @property
+    def sigma_max(self):
+        return self.sigmas[-1]
+
+    def timestep(self, sigma):
+        return sigma
+
+    def sigma(self, timestep):
+        return flux_time_shift(self.shift, 1.0, timestep)
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 1.0
+        if percent >= 1.0:
+            return 0.0
+        return 1.0 - percent
diff --git a/comfy/sd.py b/comfy/sd.py
index 8bf8d1087..c9bc16397 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -23,6 +23,7 @@ import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
 
 import comfy.model_patcher
 import comfy.lora
@@ -387,6 +388,7 @@ class CLIPType(Enum):
     SD3 = 3
     STABLE_AUDIO = 4
     HUNYUAN_DIT = 5
+    FLUX = 6
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
@@ -438,6 +440,9 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
         elif clip_type == CLIPType.HUNYUAN_DIT:
             clip_target.clip = comfy.text_encoders.hydit.HyditModel
             clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
+        elif clip_type == CLIPType.FLUX:
+            clip_target.clip = comfy.text_encoders.flux.FluxClipModel
+            clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
             clip_target.tokenizer = sdxl_clip.SDXLTokenizer
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index ddd0c173b..43e8f5d1b 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -9,6 +9,7 @@ import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
 import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
 
 from . import supported_models_base
 from . import latent_formats
@@ -619,7 +620,45 @@ class HunyuanDiT1(HunyuanDiT):
         "linear_end" : 0.03,
     }
 
+class Flux(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "flux",
+        "guidance_embed": True,
+    }
 
-models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1]
+    sampling_settings = {
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Flux(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.FluxClipModel)
+
+class FluxSchnell(Flux):
+    unet_config = {
+        "image_model": "flux",
+        "guidance_embed": False,
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 1.0,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
+        return out
+
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, HunyuanDiT, HunyuanDiT1, Flux, FluxSchnell]
 
 models += [SVD_img2vid]
diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
new file mode 100644
index 000000000..2759a38a8
--- /dev/null
+++ b/comfy/text_encoders/flux.py
@@ -0,0 +1,64 @@
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+from transformers import T5TokenizerFast
+import torch
+import os
+
+class T5XXLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5)
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256)
+
+
+class FluxTokenizer:
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        out = {}
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.clip_g.untokenize(token_weight_pair)
+
+    def state_dict(self):
+        return {}
+
+
+class FluxClipModel(torch.nn.Module):
+    def __init__(self, device="cpu", dtype=None):
+        super().__init__()
+        self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False)
+        self.t5xxl = T5XXLModel(device=device, dtype=dtype)
+        self.dtypes = set([dtype])
+
+    def set_clip_options(self, options):
+        self.clip_l.set_clip_options(options)
+        self.t5xxl.set_clip_options(options)
+
+    def reset_clip_options(self):
+        self.clip_l.reset_clip_options()
+        self.t5xxl.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_l = token_weight_pairs["l"]
+        token_weight_pars_t5 = token_weight_pairs["t5xxl"]
+
+        t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
+        l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+        return t5_out, l_pooled
+
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+            return self.clip_l.load_sd(sd)
+        else:
+            return self.t5xxl.load_sd(sd)
+
diff --git a/folder_paths.py b/folder_paths.py
index 2baf8ce1c..71faa2df4 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -3,7 +3,7 @@ import time
 import logging
 from typing import Set, List, Dict, Tuple
 
-supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
+supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl', '.sft'])
 
 SupportedFileExtensionsType = Set[str]
 ScanPathType = List[str]
diff --git a/nodes.py b/nodes.py
index dc66dd3ef..93d24ae52 100644
--- a/nodes.py
+++ b/nodes.py
@@ -859,7 +859,7 @@ class DualCLIPLoader:
     def INPUT_TYPES(s):
         return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
                               "clip_name2": (folder_paths.get_filename_list("clip"), ),
-                              "type": (["sdxl", "sd3"], ),
+                              "type": (["sdxl", "sd3", "flux"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -873,6 +873,8 @@ class DualCLIPLoader:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
         elif type == "sd3":
             clip_type = comfy.sd.CLIPType.SD3
+        elif type == "flux":
+            clip_type = comfy.sd.CLIPType.FLUX
 
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)

From 8d34211a7abd06d659279cfa2d9d3d0cada75f58 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 09:57:01 -0400
Subject: [PATCH 096/112] Fix old python versions no longer working.

---
 comfy/ldm/flux/layers.py | 11 +++++------
 comfy/ldm/flux/math.py   |  2 +-
 comfy/ldm/flux/model.py  |  4 ++--
 3 files changed, 8 insertions(+), 9 deletions(-)

diff --git a/comfy/ldm/flux/layers.py b/comfy/ldm/flux/layers.py
index bb5e02b6d..99f498106 100644
--- a/comfy/ldm/flux/layers.py
+++ b/comfy/ldm/flux/layers.py
@@ -8,9 +8,8 @@ from torch import Tensor, nn
 from .math import attention, rope
 import comfy.ops
 
-
 class EmbedND(nn.Module):
-    def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+    def __init__(self, dim: int, theta: int, axes_dim: list):
         super().__init__()
         self.dim = dim
         self.theta = theta
@@ -79,7 +78,7 @@ class QKNorm(torch.nn.Module):
         self.query_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
         self.key_norm = RMSNorm(dim, dtype=dtype, device=device, operations=operations)
 
-    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+    def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple:
         q = self.query_norm(q)
         k = self.key_norm(k)
         return q.to(v), k.to(v)
@@ -118,7 +117,7 @@ class Modulation(nn.Module):
         self.multiplier = 6 if double else 3
         self.lin = operations.Linear(dim, self.multiplier * dim, bias=True, dtype=dtype, device=device)
 
-    def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+    def forward(self, vec: Tensor) -> tuple:
         out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
 
         return (
@@ -156,7 +155,7 @@ class DoubleStreamBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> tuple[Tensor, Tensor]:
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor):
         img_mod1, img_mod2 = self.img_mod(vec)
         txt_mod1, txt_mod2 = self.txt_mod(vec)
 
@@ -203,7 +202,7 @@ class SingleStreamBlock(nn.Module):
         hidden_size: int,
         num_heads: int,
         mlp_ratio: float = 4.0,
-        qk_scale: float | None = None,
+        qk_scale: float = None,
         dtype=None,
         device=None,
         operations=None
diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index e4ef624ef..d9bb568ac 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -21,7 +21,7 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     return out.float()
 
 
-def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
+def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
     xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
     xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
     xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index f77834c15..ae34052df 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -26,7 +26,7 @@ class FluxParams:
     num_heads: int
     depth: int
     depth_single_blocks: int
-    axes_dim: list[int]
+    axes_dim: list
     theta: int
     qkv_bias: bool
     guidance_embed: bool
@@ -92,7 +92,7 @@ class Flux(nn.Module):
         txt_ids: Tensor,
         timesteps: Tensor,
         y: Tensor,
-        guidance: Tensor | None = None,
+        guidance: Tensor = None,
     ) -> Tensor:
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")

From 5f98de7697de9aed79561a3f764c0e7d9766f8f1 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 11:05:56 -0400
Subject: [PATCH 097/112] Load flux t5 in fp8 if weights are in fp8.

---
 comfy/model_management.py       | 11 +++++++++++
 comfy/sd.py                     |  8 +++++++-
 comfy/text_encoders/flux.py     | 13 ++++++++++---
 comfy/text_encoders/sd3_clip.py |  9 +--------
 4 files changed, 29 insertions(+), 12 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index bcd86a033..07c137270 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -661,6 +661,17 @@ def supports_cast(device, dtype): #TODO
         return True
     return False
 
+def pick_weight_dtype(dtype, fallback_dtype, device=None):
+    if dtype is None:
+        dtype = fallback_dtype
+    elif dtype_size(dtype) > dtype_size(fallback_dtype):
+        dtype = fallback_dtype
+
+    if not supports_cast(device, dtype):
+        dtype = fallback_dtype
+
+    return dtype
+
 def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
diff --git a/comfy/sd.py b/comfy/sd.py
index c9bc16397..2be8edef2 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -441,7 +441,13 @@ def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DI
             clip_target.clip = comfy.text_encoders.hydit.HyditModel
             clip_target.tokenizer = comfy.text_encoders.hydit.HyditTokenizer
         elif clip_type == CLIPType.FLUX:
-            clip_target.clip = comfy.text_encoders.flux.FluxClipModel
+            weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+            weight = clip_data[0].get(weight_name, clip_data[1].get(weight_name, None))
+            dtype_t5 = None
+            if weight is not None:
+                dtype_t5 = weight.dtype
+
+            clip_target.clip = comfy.text_encoders.flux.flux_clip(dtype_t5=dtype_t5)
             clip_target.tokenizer = comfy.text_encoders.flux.FluxTokenizer
         else:
             clip_target.clip = sdxl_clip.SDXLClipModel
diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
index 2759a38a8..849214ce0 100644
--- a/comfy/text_encoders/flux.py
+++ b/comfy/text_encoders/flux.py
@@ -1,5 +1,6 @@
 from comfy import sd1_clip
 import comfy.text_encoders.t5
+import comfy.model_management
 from transformers import T5TokenizerFast
 import torch
 import os
@@ -34,11 +35,12 @@ class FluxTokenizer:
 
 
 class FluxClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None):
+    def __init__(self, dtype_t5=None, device="cpu", dtype=None):
         super().__init__()
+        dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
         self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False)
-        self.t5xxl = T5XXLModel(device=device, dtype=dtype)
-        self.dtypes = set([dtype])
+        self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
+        self.dtypes = set([dtype, dtype_t5])
 
     def set_clip_options(self, options):
         self.clip_l.set_clip_options(options)
@@ -62,3 +64,8 @@ class FluxClipModel(torch.nn.Module):
         else:
             return self.t5xxl.load_sd(sd)
 
+def flux_clip(dtype_t5=None):
+    class FluxClipModel_(FluxClipModel):
+        def __init__(self, device="cpu", dtype=None):
+            super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype)
+    return FluxClipModel_
diff --git a/comfy/text_encoders/sd3_clip.py b/comfy/text_encoders/sd3_clip.py
index b01fad221..143d884cb 100644
--- a/comfy/text_encoders/sd3_clip.py
+++ b/comfy/text_encoders/sd3_clip.py
@@ -54,14 +54,7 @@ class SD3ClipModel(torch.nn.Module):
             self.clip_g = None
 
         if t5:
-            if dtype_t5 is None:
-                dtype_t5 = dtype
-            elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype):
-                dtype_t5 = dtype
-
-            if not comfy.model_management.supports_cast(device, dtype_t5):
-                dtype_t5 = dtype
-
+            dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
             self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
             self.dtypes.add(dtype_t5)
         else:

From eb96c3bd82ba7eda5cac343ea2465baf6715b6d0 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 11:32:58 -0400
Subject: [PATCH 098/112] Fix .sft file loading (they are safetensors files).

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

diff --git a/comfy/utils.py b/comfy/utils.py
index 1e4b5ef88..0db9fbb62 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -11,7 +11,7 @@ import itertools
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
         device = torch.device("cpu")
-    if ckpt.lower().endswith(".safetensors"):
+    if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
         sd = safetensors.torch.load_file(ckpt, device=device.type)
     else:
         if safe_load:

From 2f88d19ef311dc078a89ef39c1f9bc9265d6435d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 11:48:19 -0400
Subject: [PATCH 099/112] Add link to Flux examples to readme.

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

diff --git a/README.md b/README.md
index 6a68f268a..a542ed4d6 100644
--- a/README.md
+++ b/README.md
@@ -12,6 +12,7 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
+- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Smart memory management: can automatically run models on GPUs with as low as 1GB vram.

From 1aa9cf3292499303260533780d25bbff99e076c8 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 12:11:57 -0400
Subject: [PATCH 100/112] Make lowvram more aggressive on low memory machines.

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

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 07c137270..de4bd4426 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -318,7 +318,7 @@ class LoadedModel:
         return self.model is other.model
 
 def minimum_inference_memory():
-    return (1024 * 1024 * 1024)
+    return (1024 * 1024 * 1024) * 1.2
 
 def unload_model_clones(model, unload_weights_only=True, force_unload=True):
     to_unload = []

From f2b80f95d2a3384609c1ffdec08457c4724d1d20 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 12:55:28 -0400
Subject: [PATCH 101/112] Better Mac support on flux model.

---
 comfy/ldm/flux/math.py  |  4 ++--
 comfy/ldm/flux/model.py | 10 ++++++----
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index d9bb568ac..7cf87947a 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -13,9 +13,9 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=pos.device)
     omega = 1.0 / (theta**scale)
-    out = torch.einsum("...n,d->...nd", pos, omega)
+    out = torch.einsum("...n,d->...nd", pos.float(), omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
     return out.float()
diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index ae34052df..21f1a877e 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -126,11 +126,13 @@ class Flux(nn.Module):
         bs, c, h, w = x.shape
         img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
 
-        img_ids = torch.zeros((h // 2, w // 2, 3), device=x.device, dtype=x.dtype)
-        img_ids[..., 1] = img_ids[..., 1] + torch.arange(h // 2, device=x.device, dtype=x.dtype)[:, None]
-        img_ids[..., 2] = img_ids[..., 2] + torch.arange(w // 2, device=x.device, dtype=x.dtype)[None, :]
+        h_len = (h // 2)
+        w_len = (w // 2)
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
+        img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
         img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance)
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h // 2, w=w // 2, ph=2, pw=2)
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)

From d7430a1651a300e8230867ce3e6d86cc0101facc Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 13:28:41 -0400
Subject: [PATCH 102/112] Add a way to load the diffusion model in fp8 with
 UNETLoader node.

---
 comfy/sd.py | 13 ++++++++-----
 nodes.py    |  6 ++++--
 2 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 2be8edef2..41ce18c80 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -567,7 +567,7 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_unet_state_dict(sd): #load unet in diffusers or regular format
+def load_unet_state_dict(sd, dtype=None): #load unet in diffusers or regular format
 
     #Allow loading unets from checkpoint files
     diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
@@ -576,7 +576,6 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
         sd = temp_sd
 
     parameters = comfy.utils.calculate_parameters(sd)
-    unet_dtype = model_management.unet_dtype(model_params=parameters)
     load_device = model_management.get_torch_device()
     model_config = model_detection.model_config_from_unet(sd, "")
 
@@ -603,7 +602,11 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
                     logging.warning("{} {}".format(diffusers_keys[k], k))
 
     offload_device = model_management.unet_offload_device()
-    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    if dtype is None:
+        unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    else:
+        unet_dtype = dtype
+
     manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
     model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
     model = model_config.get_model(new_sd, "")
@@ -614,9 +617,9 @@ def load_unet_state_dict(sd): #load unet in diffusers or regular format
         logging.info("left over keys in unet: {}".format(left_over))
     return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
-def load_unet(unet_path):
+def load_unet(unet_path, dtype=None):
     sd = comfy.utils.load_torch_file(unet_path)
-    model = load_unet_state_dict(sd)
+    model = load_unet_state_dict(sd, dtype=dtype)
     if model is None:
         logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
diff --git a/nodes.py b/nodes.py
index 93d24ae52..fbd0c6ce0 100644
--- a/nodes.py
+++ b/nodes.py
@@ -818,15 +818,17 @@ class UNETLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "unet_name": (folder_paths.get_filename_list("unet"), ),
+                              "weight_dtype": (["default", "fp8_e4m3fn", "fp8_e5m2"],)
                              }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_unet"
 
     CATEGORY = "advanced/loaders"
 
-    def load_unet(self, unet_name):
+    def load_unet(self, unet_name, weight_dtype):
+        weight_dtype = {"default":None, "fp8_e4m3fn":torch.float8_e4m3fn, "fp8_e5m2":torch.float8_e4m3fn}[weight_dtype]
         unet_path = folder_paths.get_full_path("unet", unet_name)
-        model = comfy.sd.load_unet(unet_path)
+        model = comfy.sd.load_unet(unet_path, dtype=weight_dtype)
         return (model,)
 
 class CLIPLoader:

From b4f6ebb2e88a43876caa1d0b2b8eb1e99ac57adb Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 13:33:30 -0400
Subject: [PATCH 103/112] Rename UNETLoader node to "Load Diffusion Model".

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

diff --git a/nodes.py b/nodes.py
index fbd0c6ce0..5b6321b69 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1847,6 +1847,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "StyleModelLoader": "Load Style Model",
     "CLIPVisionLoader": "Load CLIP Vision",
     "UpscaleModelLoader": "Load Upscale Model",
+    "UNETLoader": "Load Diffusion Model",
     # Conditioning
     "CLIPVisionEncode": "CLIP Vision Encode",
     "StyleModelApply": "Apply Style Model",

From 48eb1399c02bdae7e14b2208c448b69b382d0090 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 13:41:27 -0400
Subject: [PATCH 104/112] Try to fix mac issue.

---
 comfy/ldm/flux/math.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/comfy/ldm/flux/math.py b/comfy/ldm/flux/math.py
index 7cf87947a..88c2b6bb4 100644
--- a/comfy/ldm/flux/math.py
+++ b/comfy/ldm/flux/math.py
@@ -2,6 +2,7 @@ import torch
 from einops import rearrange
 from torch import Tensor
 from comfy.ldm.modules.attention import optimized_attention
+import comfy.model_management
 
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
     q, k = apply_rope(q, k, pe)
@@ -13,12 +14,17 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=pos.device)
+    if comfy.model_management.is_device_mps(pos.device):
+        device = torch.device("cpu")
+    else:
+        device = pos.device
+
+    scale = torch.linspace(0, (dim - 2) / dim, steps=dim//2, dtype=torch.float64, device=device)
     omega = 1.0 / (theta**scale)
-    out = torch.einsum("...n,d->...nd", pos.float(), omega)
+    out = torch.einsum("...n,d->...nd", pos.to(dtype=torch.float32, device=device), omega)
     out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
     out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
-    return out.float()
+    return out.to(dtype=torch.float32, device=pos.device)
 
 
 def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):

From a6decf1e620907347c9c5d8c815172f349b19c21 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 16:18:14 -0400
Subject: [PATCH 105/112] Fix bfloat16 potentially not being enabled on mps.

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

diff --git a/comfy/model_management.py b/comfy/model_management.py
index de4bd4426..b4f32d648 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -897,7 +897,10 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     if directml_enabled:
         return False
 
-    if cpu_mode() or mps_mode():
+    if mps_mode():
+        return True
+
+    if cpu_mode():
         return False
 
     if is_intel_xpu():

From 1c61361fd2478068e69816e78a0689db6664b65d Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 16:28:11 -0400
Subject: [PATCH 106/112] Fast preview support for Flux.

---
 comfy/latent_formats.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 34c7bb3da..ecb03b010 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -144,6 +144,24 @@ class Flux(SD3):
     def __init__(self):
         self.scale_factor = 0.3611
         self.shift_factor = 0.1159
+        self.latent_rgb_factors =[
+            [-0.0404,  0.0159,  0.0609],
+            [ 0.0043,  0.0298,  0.0850],
+            [ 0.0328, -0.0749, -0.0503],
+            [-0.0245,  0.0085,  0.0549],
+            [ 0.0966,  0.0894,  0.0530],
+            [ 0.0035,  0.0399,  0.0123],
+            [ 0.0583,  0.1184,  0.1262],
+            [-0.0191, -0.0206, -0.0306],
+            [-0.0324,  0.0055,  0.1001],
+            [ 0.0955,  0.0659, -0.0545],
+            [-0.0504,  0.0231, -0.0013],
+            [ 0.0500, -0.0008, -0.0088],
+            [ 0.0982,  0.0941,  0.0976],
+            [-0.1233, -0.0280, -0.0897],
+            [-0.0005, -0.0530, -0.0020],
+            [-0.1273, -0.0932, -0.0680]
+        ]
 
     def process_in(self, latent):
         return (latent - self.shift_factor) * self.scale_factor

From d965474aaae2f1b461e0925a7e1519b740393994 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 16:39:59 -0400
Subject: [PATCH 107/112] Make ComfyUI split batches a higher priority than
 weight offload.

---
 comfy/model_management.py | 10 +++++++---
 comfy/sampler_helpers.py  |  4 +++-
 2 files changed, 10 insertions(+), 4 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index b4f32d648..da0b989a8 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -379,11 +379,15 @@ def free_memory(memory_required, device, keep_loaded=[]):
             if mem_free_torch > mem_free_total * 0.25:
                 soft_empty_cache()
 
-def load_models_gpu(models, memory_required=0, force_patch_weights=False):
+def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None):
     global vram_state
 
     inference_memory = minimum_inference_memory()
     extra_mem = max(inference_memory, memory_required)
+    if minimum_memory_required is None:
+        minimum_memory_required = extra_mem
+    else:
+        minimum_memory_required = max(inference_memory, minimum_memory_required)
 
     models = set(models)
 
@@ -446,8 +450,8 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False):
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - extra_mem)))
-            if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary
+            lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required)))
+            if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 
         if vram_set_state == VRAMState.NO_VRAM:
diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py
index a18abd9e9..4a2ec123b 100644
--- a/comfy/sampler_helpers.py
+++ b/comfy/sampler_helpers.py
@@ -61,7 +61,9 @@ def prepare_sampling(model, noise_shape, conds):
     device = model.load_device
     real_model = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
-    comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)
+    memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory
+    minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory
+    comfy.model_management.load_models_gpu([model] + models, memory_required=memory_required, minimum_memory_required=minimum_memory_required)
     real_model = model.model
 
     return real_model, conds, models

From d420bc792af0b61a6ef7410c65fa2d4dcc646c56 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 17:49:46 -0400
Subject: [PATCH 108/112] Tweak the memory usage formulas for Flux and SD.

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

diff --git a/comfy/model_base.py b/comfy/model_base.py
index 7c7b4c3ff..994b414cc 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -256,7 +256,7 @@ class BaseModel(torch.nn.Module):
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
             area = input_shape[0] * math.prod(input_shape[2:])
-            return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
+            return (area * 0.3) * (1024 * 1024)
 
 
 def unclip_adm(unclip_conditioning, device, noise_augmentor, noise_augment_merge=0.0, seed=None):
@@ -702,3 +702,15 @@ class Flux(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
         return out
+
+    def memory_required(self, input_shape):
+        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
+            dtype = self.get_dtype()
+            if self.manual_cast_dtype is not None:
+                dtype = self.manual_cast_dtype
+            #TODO: this probably needs to be tweaked
+            area = input_shape[0] * input_shape[2] * input_shape[3]
+            return (area * comfy.model_management.dtype_size(dtype) * 0.020) * (1024 * 1024)
+        else:
+            area = input_shape[0] * input_shape[2] * input_shape[3]
+            return (area * 0.3) * (1024 * 1024)

From a531001cc772305364a319a760fcd5034e28411a Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 18:53:25 -0400
Subject: [PATCH 109/112] Add CLIPTextEncodeFlux.

---
 comfy_extras/nodes_flux.py | 27 +++++++++++++++++++++++++++
 nodes.py                   |  1 +
 2 files changed, 28 insertions(+)
 create mode 100644 comfy_extras/nodes_flux.py

diff --git a/comfy_extras/nodes_flux.py b/comfy_extras/nodes_flux.py
new file mode 100644
index 000000000..6c1e8b0e0
--- /dev/null
+++ b/comfy_extras/nodes_flux.py
@@ -0,0 +1,27 @@
+
+class CLIPTextEncodeFlux:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "clip": ("CLIP", ),
+            "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
+            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning"
+
+    def encode(self, clip, clip_l, t5xxl, guidance):
+        tokens = clip.tokenize(clip_l)
+        tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+
+        output = clip.encode_from_tokens(tokens, return_pooled=True, return_dict=True)
+        cond = output.pop("cond")
+        output["guidance"] = guidance
+        return ([[cond, output]], )
+
+NODE_CLASS_MAPPINGS = {
+    "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
+}
diff --git a/nodes.py b/nodes.py
index 5b6321b69..1994f1190 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2043,6 +2043,7 @@ def init_builtin_extra_nodes():
         "nodes_gits.py",
         "nodes_controlnet.py",
         "nodes_hunyuan.py",
+        "nodes_flux.py",
     ]
 
     import_failed = []

From e638f2858a93ea3b94edc2938b213ebc1fcf4e20 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 21:03:26 -0400
Subject: [PATCH 110/112] Hack to make all resolutions work on Flux models.

---
 comfy/ldm/flux/model.py | 14 ++++++++++----
 1 file changed, 10 insertions(+), 4 deletions(-)

diff --git a/comfy/ldm/flux/model.py b/comfy/ldm/flux/model.py
index 21f1a877e..e7931c16d 100644
--- a/comfy/ldm/flux/model.py
+++ b/comfy/ldm/flux/model.py
@@ -124,10 +124,16 @@ class Flux(nn.Module):
 
     def forward(self, x, timestep, context, y, guidance, **kwargs):
         bs, c, h, w = x.shape
-        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
+        patch_size = 2
+        pad_h = (patch_size - h % 2) % patch_size
+        pad_w = (patch_size - w % 2) % patch_size
 
-        h_len = (h // 2)
-        w_len = (w // 2)
+        x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='circular')
+
+        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+
+        h_len = ((h + (patch_size // 2)) // patch_size)
+        w_len = ((w + (patch_size // 2)) // patch_size)
         img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
         img_ids[..., 1] = img_ids[..., 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype)[:, None]
         img_ids[..., 2] = img_ids[..., 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype)[None, :]
@@ -135,4 +141,4 @@ class Flux(nn.Module):
 
         txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
         out = self.forward_orig(img, img_ids, context, txt_ids, timestep, y, guidance)
-        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)
+        return rearrange(out, "b (h w) (c ph pw) -> b c (h ph) (w pw)", h=h_len, w=w_len, ph=2, pw=2)[:,:,:h,:w]

From ce9ac2fe0581288d4a24869dae2e04a3c2b67061 Mon Sep 17 00:00:00 2001
From: Alexander Brown <DrJKL0424@gmail.com>
Date: Thu, 1 Aug 2024 18:40:56 -0700
Subject: [PATCH 111/112] Fix clip_g/clip_l mixup (#4168)

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

diff --git a/comfy/text_encoders/flux.py b/comfy/text_encoders/flux.py
index 849214ce0..0590741bb 100644
--- a/comfy/text_encoders/flux.py
+++ b/comfy/text_encoders/flux.py
@@ -28,7 +28,7 @@ class FluxTokenizer:
         return out
 
     def untokenize(self, token_weight_pair):
-        return self.clip_g.untokenize(token_weight_pair)
+        return self.clip_l.untokenize(token_weight_pair)
 
     def state_dict(self):
         return {}

From 369f459b2058f793c1230472f04edc9fd9471b46 Mon Sep 17 00:00:00 2001
From: comfyanonymous <comfyanonymous@protonmail.com>
Date: Thu, 1 Aug 2024 22:19:53 -0400
Subject: [PATCH 112/112] Fix no longer working on old pytorch.

---
 nodes.py | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/nodes.py b/nodes.py
index 1994f1190..e296597c5 100644
--- a/nodes.py
+++ b/nodes.py
@@ -826,9 +826,14 @@ class UNETLoader:
     CATEGORY = "advanced/loaders"
 
     def load_unet(self, unet_name, weight_dtype):
-        weight_dtype = {"default":None, "fp8_e4m3fn":torch.float8_e4m3fn, "fp8_e5m2":torch.float8_e4m3fn}[weight_dtype]
+        dtype = None
+        if weight_dtype == "fp8_e4m3fn":
+            dtype = torch.float8_e4m3fn
+        elif weight_dtype == "fp8_e5m2":
+            dtype = torch.float8_e5m2
+
         unet_path = folder_paths.get_full_path("unet", unet_name)
-        model = comfy.sd.load_unet(unet_path, dtype=weight_dtype)
+        model = comfy.sd.load_unet(unet_path, dtype=dtype)
         return (model,)
 
 class CLIPLoader: