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| import open_clip.tokenizer | |
| import torch | |
| from modules import sd_hijack_clip, devices | |
| from modules.shared import opts | |
| tokenizer = open_clip.tokenizer._tokenizer | |
| class FrozenOpenCLIPEmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWordsBase): | |
| def __init__(self, wrapped, hijack): | |
| super().__init__(wrapped, hijack) | |
| self.comma_token = [v for k, v in tokenizer.encoder.items() if k == ',</w>'][0] | |
| self.id_start = tokenizer.encoder["<start_of_text>"] | |
| self.id_end = tokenizer.encoder["<end_of_text>"] | |
| self.id_pad = 0 | |
| def tokenize(self, texts): | |
| assert not opts.use_old_emphasis_implementation, 'Old emphasis implementation not supported for Open Clip' | |
| tokenized = [tokenizer.encode(text) for text in texts] | |
| return tokenized | |
| def encode_with_transformers(self, tokens): | |
| # set self.wrapped.layer_idx here according to opts.CLIP_stop_at_last_layers | |
| z = self.wrapped.encode_with_transformer(tokens) | |
| return z | |
| def encode_embedding_init_text(self, init_text, nvpt): | |
| ids = tokenizer.encode(init_text) | |
| ids = torch.asarray([ids], device=devices.device, dtype=torch.int) | |
| embedded = self.wrapped.model.token_embedding.wrapped(ids).squeeze(0) | |
| return embedded | |