| from typing import List |
| import torch |
| from transformers import T5Tokenizer, UMT5EncoderModel |
|
|
| class PatchedT5Tokenizer(T5Tokenizer): |
| def __init__( |
| self, |
| vocab: str | list[tuple[str, float]] | None = None, |
| eos_token="</s>", |
| unk_token="<unk>", |
| pad_token="<pad>", |
| _spm_precompiled_charsmap=None, |
| extra_ids=100, |
| additional_special_tokens=None, |
| **kwargs, |
| ): |
| super().__init__( |
| vocab=vocab, |
| eos_token=eos_token, |
| unk_token=unk_token, |
| pad_token=pad_token, |
| _spm_precompiled_charsmap=None, |
| extra_ids=extra_ids, |
| additional_special_tokens=additional_special_tokens, |
| **kwargs, |
| ) |
|
|
| def get_umt5_encoder( |
| model_path: str, |
| tokenizer_subfolder: str = None, |
| encoder_subfolder: str = None, |
| torch_dtype: str = torch.bfloat16, |
| comfy_files: List[str] = [ |
| "text_encoders/umt5_xxl_fp16.safetensors", |
| "text_encoders/umt5_xxl_fp8_e4m3fn_scaled.safetensors", |
| ], |
| ) -> UMT5EncoderModel: |
| """ |
| Load the UMT5 encoder model from the specified path. |
| """ |
| tokenizer = PatchedT5Tokenizer.from_pretrained(model_path, subfolder=tokenizer_subfolder) |
| print(f"Using {model_path} for UMT5 encoder.") |
| text_encoder = UMT5EncoderModel.from_pretrained( |
| model_path, subfolder=encoder_subfolder, torch_dtype=torch_dtype |
| ) |
| return tokenizer, text_encoder |
|
|
