mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-11 23:00:51 +08:00
87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
import copy
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import torch
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from transformers import T5TokenizerFast
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from .sd3_clip import T5XXLModel
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from .. import sd1_clip, model_management
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from ..component_model import files
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class T5XXLTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data=None):
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if tokenizer_data is None:
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tokenizer_data = dict()
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tokenizer_path = files.get_package_as_path("comfy.text_encoders.t5_tokenizer")
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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, tokenizer_data=tokenizer_data)
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class FluxTokenizer:
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def __init__(self, embedding_directory=None, tokenizer_data=None):
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if tokenizer_data is None:
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tokenizer_data = dict()
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self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
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self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
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def tokenize_with_weights(self, text: str, return_word_ids=False, **kwargs):
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out = {
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"l": self.clip_l.tokenize_with_weights(text, return_word_ids),
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"t5xxl": self.t5xxl.tokenize_with_weights(text, return_word_ids)
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}
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return out
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def untokenize(self, token_weight_pair):
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return self.clip_l.untokenize(token_weight_pair)
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def state_dict(self):
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return {}
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def clone(self):
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return copy.copy(self)
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class FluxClipModel(torch.nn.Module):
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def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options=None):
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super().__init__()
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if model_options is None:
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model_options = {}
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dtype_t5 = model_management.pick_weight_dtype(dtype_t5, dtype, device)
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self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
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self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
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self.dtypes = {dtype, dtype_t5}
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def set_clip_options(self, options):
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self.clip_l.set_clip_options(options)
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self.t5xxl.set_clip_options(options)
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def reset_clip_options(self):
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self.clip_l.reset_clip_options()
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self.t5xxl.reset_clip_options()
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def encode_token_weights(self, token_weight_pairs):
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token_weight_pairs_l = token_weight_pairs["l"]
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token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
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t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
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l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
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return t5_out, l_pooled
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def load_sd(self, sd):
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if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
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return self.clip_l.load_sd(sd)
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else:
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return self.t5xxl.load_sd(sd)
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def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None):
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class FluxClipModel_(FluxClipModel):
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def __init__(self, device="cpu", dtype=None, model_options=None):
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if model_options is None:
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model_options = {}
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if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
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model_options = model_options.copy()
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model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
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super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
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return FluxClipModel_
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