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Fix a few things in text enc code for models with no eos token.
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@ -199,11 +199,18 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
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attention_mask = None
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if self.enable_attention_masks or self.zero_out_masked or self.return_attention_masks:
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attention_mask = torch.zeros_like(tokens)
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end_token = self.special_tokens.get("end", -1)
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end_token = self.special_tokens.get("end", None)
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if end_token is None:
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cmp_token = self.special_tokens.get("pad", -1)
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else:
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cmp_token = end_token
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for x in range(attention_mask.shape[0]):
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for y in range(attention_mask.shape[1]):
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attention_mask[x, y] = 1
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if tokens[x, y] == end_token:
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if tokens[x, y] == cmp_token:
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if end_token is None:
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attention_mask[x, y] = 0
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break
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attention_mask_model = None
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@ -522,10 +529,14 @@ class SDTokenizer:
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for i, t_group in enumerate(tokens):
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#determine if we're going to try and keep the tokens in a single batch
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is_large = len(t_group) >= self.max_word_length
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if self.end_token is not None:
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has_end_token = 1
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else:
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has_end_token = 0
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while len(t_group) > 0:
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if len(t_group) + len(batch) > self.max_length - 1:
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remaining_length = self.max_length - len(batch) - 1
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if len(t_group) + len(batch) > self.max_length - has_end_token:
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remaining_length = self.max_length - len(batch) - has_end_token
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#break word in two and add end token
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if is_large:
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batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
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