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Use the end token for the text encoder attention mask.

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comfyanonymous 2024-06-07 03:05:23 -04:00
parent 0dccb4617d
commit 56333d4850
1 changed files with 2 additions and 2 deletions
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comfy/sd1_clip.py
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@ -168,11 +168,11 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
attention_mask = None attention_mask = None
if self.enable_attention_masks: if self.enable_attention_masks:
attention_mask = torch.zeros_like(tokens) attention_mask = torch.zeros_like(tokens)
max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1 end_token = self.special_tokens.get("end", -1)
for x in range(attention_mask.shape[0]): for x in range(attention_mask.shape[0]):
for y in range(attention_mask.shape[1]): for y in range(attention_mask.shape[1]):
attention_mask[x, y] = 1 attention_mask[x, y] = 1
if tokens[x, y] == max_token: if tokens[x, y] == end_token:
break break
outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state) outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)