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d02c2a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | 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, # this is passing a empty byte string for some reason now.
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
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