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Merge branch 'master' into worksplit-multigpu

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This commit is contained in:
Jedrzej Kosinski 2025-04-16 19:26:48 -05:00
commit 2a54a904f4
3 changed files with 9 additions and 6 deletions
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6
comfy/ldm/hidream/model.py
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@ -13,6 +13,7 @@ from comfy.ldm.flux.layers import LastLayer
from comfy.ldm.modules.attention import optimized_attention
import comfy.model_management
import comfy.ldm.common_dit
# Copied from https://github.com/black-forest-labs/flux/blob/main/src/flux/modules/layers.py
@ -701,7 +702,8 @@ class HiDreamImageTransformer2DModel(nn.Module):
control = None,
transformer_options = {},
) -> torch.Tensor:
hidden_states = x
bs, c, h, w = x.shape
hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
timesteps = t
pooled_embeds = y
T5_encoder_hidden_states = context
@ -794,4 +796,4 @@ class HiDreamImageTransformer2DModel(nn.Module):
hidden_states = hidden_states[:, :image_tokens_seq_len, ...]
output = self.final_layer(hidden_states, adaln_input)
output = self.unpatchify(output, img_sizes)
return -output
return -output[:, :, :h, :w]

5
comfy/text_encoders/hidream.py
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@ -11,14 +11,15 @@ class HiDreamTokenizer:
def __init__(self, embedding_directory=None, tokenizer_data={}):
self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, tokenizer_data=tokenizer_data)
self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, max_length=128, tokenizer_data=tokenizer_data)
self.llama = hunyuan_video.LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=128, pad_token=128009, tokenizer_data=tokenizer_data)
def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
out = {}
out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids)
out["t5xxl"] = [t5xxl[0]] # Use only first 128 tokens
out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids)
return out

4
comfy/text_encoders/sd3_clip.py
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@ -32,9 +32,9 @@ def t5_xxl_detect(state_dict, prefix=""):
return out
class T5XXLTokenizer(sd1_clip.SDTokenizer):
def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77):
def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999):
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=min_length, tokenizer_data=tokenizer_data)
super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data)
class SD3Tokenizer: