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| from comfy import sd1_clip | |
| import torch | |
| import os | |
| class SDXLClipG(sd1_clip.SDClipModel): | |
| def __init__(self, device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None): | |
| if layer == "penultimate": | |
| layer="hidden" | |
| layer_idx=-2 | |
| textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json") | |
| super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, | |
| special_tokens={"start": 49406, "end": 49407, "pad": 0}, layer_norm_hidden_state=False) | |
| def load_sd(self, sd): | |
| return super().load_sd(sd) | |
| class SDXLClipGTokenizer(sd1_clip.SDTokenizer): | |
| def __init__(self, tokenizer_path=None, embedding_directory=None): | |
| super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g') | |
| class SDXLTokenizer: | |
| def __init__(self, embedding_directory=None): | |
| self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory) | |
| self.clip_g = SDXLClipGTokenizer(embedding_directory=embedding_directory) | |
| def tokenize_with_weights(self, text:str, return_word_ids=False): | |
| out = {} | |
| out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids) | |
| out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) | |
| return out | |
| def untokenize(self, token_weight_pair): | |
| return self.clip_g.untokenize(token_weight_pair) | |
| class SDXLClipModel(torch.nn.Module): | |
| def __init__(self, device="cpu", dtype=None): | |
| super().__init__() | |
| self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False) | |
| self.clip_g = SDXLClipG(device=device, dtype=dtype) | |
| def clip_layer(self, layer_idx): | |
| self.clip_l.clip_layer(layer_idx) | |
| self.clip_g.clip_layer(layer_idx) | |
| def reset_clip_layer(self): | |
| self.clip_g.reset_clip_layer() | |
| self.clip_l.reset_clip_layer() | |
| def encode_token_weights(self, token_weight_pairs): | |
| token_weight_pairs_g = token_weight_pairs["g"] | |
| token_weight_pairs_l = token_weight_pairs["l"] | |
| g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g) | |
| l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) | |
| return torch.cat([l_out, g_out], dim=-1), g_pooled | |
| def load_sd(self, sd): | |
| if "text_model.encoder.layers.30.mlp.fc1.weight" in sd: | |
| return self.clip_g.load_sd(sd) | |
| else: | |
| return self.clip_l.load_sd(sd) | |
| class SDXLRefinerClipModel(sd1_clip.SD1ClipModel): | |
| def __init__(self, device="cpu", dtype=None): | |
| super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG) | |