44 lines
2.0 KiB
Python
44 lines
2.0 KiB
Python
import os
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import torch
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from comfy import sd1_clip
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import comfy.text_encoders.t5
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import comfy.text_encoders.sd3_clip
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import comfy.model_management
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from transformers import T5TokenizerFast
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class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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# make sure empty tokens match
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self.special_tokens.pop("end")
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class PixArtT5XXL(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, model_options={}):
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super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
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class T5XXLTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer_path = os.path.join(
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os.path.dirname(os.path.dirname(os.path.realpath(__file__))),
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"tokenizers", "t5_tokenizer",
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)
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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=1)
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class PixArtTokenizer(sd1_clip.SD1Tokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
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# TODO: don't duplicate this?
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def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
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class PixArtTEModel_(PixArtT5XXL):
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def __init__(self, device="cpu", dtype=None, 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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if dtype is None:
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dtype = dtype_t5
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super().__init__(device=device, dtype=dtype, model_options=model_options)
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return PixArtTEModel_
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