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from torch import Tensor, nn
from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel,
T5Tokenizer)
class HFEmbedder(nn.Module):
def __init__(self, version: str, max_length: int, is_clip, **hf_kwargs):
super().__init__()
self.is_clip = is_clip
self.max_length = max_length
self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
if version == 'black-forest-labs/FLUX.1-dev':
if self.is_clip:
self.tokenizer: T5Tokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length, subfolder="tokenizer")
self.hf_module: T5EncoderModel = CLIPTextModel.from_pretrained(version,subfolder='text_encoder' , **hf_kwargs)
else:
self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length, subfolder="tokenizer_2")
self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version,subfolder='text_encoder_2' , **hf_kwargs)
else:
if self.is_clip:
self.tokenizer: CLIPTokenizer = CLIPTokenizer.from_pretrained(version, max_length=max_length)
self.hf_module: CLIPTextModel = CLIPTextModel.from_pretrained(version, **hf_kwargs)
else:
self.tokenizer: T5Tokenizer = T5Tokenizer.from_pretrained(version, max_length=max_length)
self.hf_module: T5EncoderModel = T5EncoderModel.from_pretrained(version, **hf_kwargs)
self.hf_module = self.hf_module.eval().requires_grad_(False)
def forward(self, text: list[str]) -> Tensor:
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=False,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
outputs = self.hf_module(
input_ids=batch_encoding["input_ids"].to(self.hf_module.device),
attention_mask=None,
output_hidden_states=False,
)
return outputs[self.output_key]
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