72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
import open_clip.tokenizer
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import torch
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from modules import sd_hijack_clip, devices
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from modules.shared import opts
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tokenizer = open_clip.tokenizer._tokenizer
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class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
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def __init__(self, wrapped, hijack):
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super().__init__(wrapped, hijack)
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self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
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self.id_start = tokenizer.encoder["<start_of_text>"]
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self.id_end = tokenizer.encoder["<end_of_text>"]
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self.id_pad = 0
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def tokenize(self, texts):
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assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
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tokenized = [tokenizer.encode(text) for text in texts]
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return tokenized
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def encode_with_transformers(self, tokens):
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# set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers
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z = self.wrapped.encode_with_transformer(tokens)
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return z
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def encode_embedding_init_text(self, init_text, nvpt):
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ids = tokenizer.encode(init_text)
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ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
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embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0)
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return embedded
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class FrozenOpenCLIPEmbedder2WithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase):
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def __init__(self, wrapped, hijack):
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super().__init__(wrapped, hijack)
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self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0]
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self.id_start = tokenizer.encoder["<start_of_text>"]
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self.id_end = tokenizer.encoder["<end_of_text>"]
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self.id_pad = 0
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def tokenize(self, texts):
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assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip'
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tokenized = [tokenizer.encode(text) for text in texts]
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return tokenized
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def encode_with_transformers(self, tokens):
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d = self.wrapped.encode_with_transformer(tokens)
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z = d[self.wrapped.layer]
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pooled = d.get("pooled")
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if pooled is not None:
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z.pooled = pooled
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return z
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def encode_embedding_init_text(self, init_text, nvpt):
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ids = tokenizer.encode(init_text)
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ids = torch.asarray([ids], device=devices.device, dtype=torch.int)
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embedded = self.wrapped.model.token_embedding.wrapped(ids.to(self.wrapped.model.token_embedding.wrapped.weight.device)).squeeze(0)
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return embedded
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