33 lines
1.4 KiB
Python
33 lines
1.4 KiB
Python
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import torch
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from modules import sd_hijack_clip, devices
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class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords):
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def __init__(self, wrapped, hijack):
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super().__init__(wrapped, hijack)
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self.id_start = wrapped.config.bos_token_id
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self.id_end = wrapped.config.eos_token_id
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self.id_pad = wrapped.config.pad_token_id
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self.comma_token = self.tokenizer.get_vocab().get(',', None) # alt diffusion doesn't have </w> bits for comma
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def encode_with_transformers(self, tokens):
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# there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a
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# trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer
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# layer to work with - you have to use the last
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attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64)
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features = self.wrapped(input_ids=tokens, attention_mask=attention_mask)
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z = features['projection_state']
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return z
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def encode_embedding_init_text(self, init_text, nvpt):
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embedding_layer = self.wrapped.roberta.embeddings
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ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
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embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
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return embedded
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