23 lines
1.5 KiB
Python
23 lines
1.5 KiB
Python
from comfy import sd1_clip
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from transformers import T5TokenizerFast
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import comfy.text_encoders.t5
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import os
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class T5BaseModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
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textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json")
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super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True)
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class T5BaseTokenizer(sd1_clip.SDTokenizer):
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def __init__(self, embedding_directory=None, tokenizer_data={}):
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tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
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super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128)
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class SAT5Tokenizer(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="t5base", tokenizer=T5BaseTokenizer)
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class SAT5Model(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, **kwargs):
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super().__init__(device=device, dtype=dtype, name="t5base", clip_model=T5BaseModel, **kwargs)
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