mirror of
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59 lines
2.9 KiB
Python
59 lines
2.9 KiB
Python
from ..transformers_compat import T5TokenizerFast
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from .t5 import T5
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from .. import sd1_clip
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from ..component_model import files
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from ..component_model.files import get_path_as_dict
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class T5XXLModel(sd1_clip.SDClipModel):
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def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, 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, "t5_old_config_xxl.json", package=__package__)
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t5xxl_quantization_metadata = model_options.get("t5xxl_quantization_metadata", None)
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if t5xxl_quantization_metadata is not None:
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model_options = model_options.copy()
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model_options["quantization_metadata"] = t5xxl_quantization_metadata
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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=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options)
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class CosmosT5XXL(sd1_clip.SD1ClipModel):
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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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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=None):
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if tokenizer_data is None:
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tokenizer_data = {}
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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=1024, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, tokenizer_data=tokenizer_data)
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class CosmosT5Tokenizer(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="t5xxl", tokenizer=T5XXLTokenizer)
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def te(dtype_t5=None, t5_quantization_metadata=None):
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class CosmosTEModel_(CosmosT5XXL):
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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["t5xxl_quantization_metadata"] = t5_quantization_metadata
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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 CosmosTEModel_
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