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Running
on
Zero
| from comfy import sd1_clip | |
| import comfy.text_encoders.t5 | |
| import comfy.text_encoders.sd3_clip | |
| import comfy.model_management | |
| from transformers import T5TokenizerFast | |
| import torch | |
| import os | |
| class T5XXLTokenizer(sd1_clip.SDTokenizer): | |
| def __init__(self, embedding_directory=None, tokenizer_data={}): | |
| tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer") | |
| super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256) | |
| class FluxTokenizer: | |
| def __init__(self, embedding_directory=None, tokenizer_data={}): | |
| clip_l_tokenizer_class = tokenizer_data.get("clip_l_tokenizer_class", sd1_clip.SDTokenizer) | |
| self.clip_l = clip_l_tokenizer_class(embedding_directory=embedding_directory) | |
| self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory) | |
| def tokenize_with_weights(self, text:str, return_word_ids=False): | |
| out = {} | |
| out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids) | |
| out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids) | |
| return out | |
| def untokenize(self, token_weight_pair): | |
| return self.clip_l.untokenize(token_weight_pair) | |
| def state_dict(self): | |
| return {} | |
| class FluxClipModel(torch.nn.Module): | |
| def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}): | |
| super().__init__() | |
| dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device) | |
| clip_l_class = model_options.get("clip_l_class", sd1_clip.SDClipModel) | |
| self.clip_l = clip_l_class(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options) | |
| self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options) | |
| self.dtypes = set([dtype, dtype_t5]) | |
| def set_clip_options(self, options): | |
| self.clip_l.set_clip_options(options) | |
| self.t5xxl.set_clip_options(options) | |
| def reset_clip_options(self): | |
| self.clip_l.reset_clip_options() | |
| self.t5xxl.reset_clip_options() | |
| def encode_token_weights(self, token_weight_pairs): | |
| token_weight_pairs_l = token_weight_pairs["l"] | |
| token_weight_pairs_t5 = token_weight_pairs["t5xxl"] | |
| t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5) | |
| l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l) | |
| return t5_out, l_pooled | |
| def load_sd(self, sd): | |
| if "text_model.encoder.layers.1.mlp.fc1.weight" in sd: | |
| return self.clip_l.load_sd(sd) | |
| else: | |
| return self.t5xxl.load_sd(sd) | |
| def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None): | |
| class FluxClipModel_(FluxClipModel): | |
| def __init__(self, device="cpu", dtype=None, model_options={}): | |
| if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options: | |
| model_options = model_options.copy() | |
| model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8 | |
| super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options) | |
| return FluxClipModel_ | |