wangbo/ComfyUI
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Merge branch 'master' into dr-support-pip-cm

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Dr.Lt.Data 2025-11-17 00:56:17 +09:00
commit 7b9ad5208e
2 changed files with 13 additions and 6 deletions
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2
README.md
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@ -221,7 +221,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
This is the command to install the nightly with ROCm 7.0 which might have some performance improvements:
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0```
```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.1```
### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.

17
comfy/sd1_clip.py
View File

@ -460,7 +460,7 @@ 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, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}):
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, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, tokenizer_data={}, tokenizer_args={}):
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, **tokenizer_args)
@ -468,6 +468,7 @@ class SDTokenizer:
self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length)
self.end_token = None
self.min_padding = min_padding
self.pad_left = pad_left
empty = self.tokenizer('')["input_ids"]
self.tokenizer_adds_end_token = has_end_token
@ -522,6 +523,12 @@ class SDTokenizer:
return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
return (embed, leftover)
def pad_tokens(self, tokens, amount):
if self.pad_left:
for i in range(amount):
tokens.insert(0, (self.pad_token, 1.0, 0))
else:
tokens.extend([(self.pad_token, 1.0, 0)] * amount)
def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs):
'''
@ -600,7 +607,7 @@ class SDTokenizer:
if self.end_token is not None:
batch.append((self.end_token, 1.0, 0))
if self.pad_to_max_length:
batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
self.pad_tokens(batch, remaining_length)
#start new batch
batch = []
if self.start_token is not None:
@ -614,11 +621,11 @@ class SDTokenizer:
if self.end_token is not None:
batch.append((self.end_token, 1.0, 0))
if min_padding is not None:
batch.extend([(self.pad_token, 1.0, 0)] * min_padding)
self.pad_tokens(batch, min_padding)
if self.pad_to_max_length and len(batch) < self.max_length:
batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
self.pad_tokens(batch, self.max_length - len(batch))
if min_length is not None and len(batch) < min_length:
batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch)))
self.pad_tokens(batch, min_length - len(batch))
if not return_word_ids:
batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]