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# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. All rights reserved.
# Copyright (c) 2024 Black Forest Labs and The XLabs-AI Team. All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from torch import Tensor, nn
from transformers import (CLIPTextModel, CLIPTokenizer, T5EncoderModel,
T5Tokenizer)
class HFEmbedder(nn.Module):
def __init__(self, version: str, max_length: int, **hf_kwargs):
super().__init__()
self.is_clip = "clip" in version.lower()
self.max_length = max_length
self.output_key = "pooler_output" if self.is_clip else "last_hidden_state"
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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