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53 lines
2.6 KiB
Python
53 lines
2.6 KiB
Python
from .spiece_tokenizer import SPieceTokenizer
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from .t5 import T5
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from .. import sd1_clip
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from ..component_model.files import get_path_as_dict
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class UMT5XXlModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options=None, textmodel_json_config=None):
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if model_options is None:
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model_options = {}
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textmodel_json_config = get_path_as_dict(textmodel_json_config, "umt5_config_xxl.json", package=__package__)
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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=T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options)
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class UMT5XXlTokenizer(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 = {}
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tokenizer = tokenizer_data.get("spiece_model", None)
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super().__init__(tokenizer, pad_with_end=False, embedding_size=4096, embedding_key='umt5xxl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, pad_token=0, tokenizer_data=tokenizer_data, embedding_directory=embedding_directory)
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def state_dict(self):
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return {"spiece_model": self.tokenizer.serialize_model()}
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class WanT5Tokenizer(sd1_clip.SD1Tokenizer):
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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 = {}
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super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="umt5xxl", tokenizer=UMT5XXlTokenizer)
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class WanT5Model(sd1_clip.SD1ClipModel):
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def __init__(self, device="cpu", dtype=None, model_options=None, **kwargs):
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if model_options is None:
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model_options = {}
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super().__init__(device=device, dtype=dtype, model_options=model_options, name="umt5xxl", clip_model=UMT5XXlModel, **kwargs)
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def te(dtype_t5=None, t5_quantization_metadata=None):
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class WanTEModel(WanT5Model):
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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 t5_quantization_metadata is not None:
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model_options = model_options.copy()
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model_options["quantization_metadata"] = t5_quantization_metadata
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if dtype_t5 is not 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 WanTEModel
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