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Support generating attention masks for left padded text encoders. (#12454)

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comfyanonymous 2026-02-13 17:15:23 -08:00 committed by GitHub
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commit 831351a29e
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2 changed files with 12 additions and 11 deletions
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15
comfy/sd1_clip.py
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@ -171,8 +171,9 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
def process_tokens(self, tokens, device):
end_token = self.special_tokens.get("end", None)
pad_token = self.special_tokens.get("pad", -1)
if end_token is None:
cmp_token = self.special_tokens.get("pad", -1)
cmp_token = pad_token
else:
cmp_token = end_token
@ -186,15 +187,21 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
other_embeds = []
eos = False
index = 0
left_pad = False
for y in x:
if isinstance(y, numbers.Integral):
if eos:
token = int(y)
if index == 0 and token == pad_token:
left_pad = True
if eos or (left_pad and token == pad_token):
attention_mask.append(0)
else:
attention_mask.append(1)
token = int(y)
left_pad = False
tokens_temp += [token]
if not eos and token == cmp_token:
if not eos and token == cmp_token and not left_pad:
if end_token is None:
attention_mask[-1] = 0
eos = True

8
comfy/text_encoders/ace15.py
View File

@ -10,7 +10,6 @@ import comfy.utils
def sample_manual_loop_no_classes(
model,
ids=None,
paddings=[],
execution_dtype=None,
cfg_scale: float = 2.0,
temperature: float = 0.85,
@ -36,9 +35,6 @@ def sample_manual_loop_no_classes(
embeds, attention_mask, num_tokens, embeds_info = model.process_tokens(ids, device)
embeds_batch = embeds.shape[0]
for i, t in enumerate(paddings):
attention_mask[i, :t] = 0
attention_mask[i, t:] = 1
output_audio_codes = []
past_key_values = []
@ -135,13 +131,11 @@ def generate_audio_codes(model, positive, negative, min_tokens=1, max_tokens=102
pos_pad = (len(negative) - len(positive))
positive = [model.special_tokens["pad"]] * pos_pad + positive
paddings = [pos_pad, neg_pad]
ids = [positive, negative]
else:
paddings = []
ids = [positive]
return sample_manual_loop_no_classes(model, ids, paddings, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
return sample_manual_loop_no_classes(model, ids, cfg_scale=cfg_scale, temperature=temperature, top_p=top_p, top_k=top_k, min_p=min_p, seed=seed, min_tokens=min_tokens, max_new_tokens=max_tokens)
class ACE15Tokenizer(sd1_clip.SD1Tokenizer):