--- title: bert ---

fastvideo.models.encoders.bert.HunyuanClip

HunyuanClip(model_dir, max_length = 77)

Hunyuan clip code copied from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py hunyuan's clip used BertModel and BertTokenizer, so we copy it.

Source
class HunyuanClip(nn.Module):
    """
        Hunyuan clip code copied from https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/hunyuandit/pipeline_hunyuandit.py
        hunyuan's clip used BertModel and BertTokenizer, so we copy it.
    """

    def __init__(self, model_dir, max_length=77):
        super().__init__()

        self.max_length = max_length
        self.tokenizer = BertTokenizer.from_pretrained(os.path.join(model_dir, 'tokenizer'))
        self.text_encoder = BertModel.from_pretrained(os.path.join(model_dir, 'clip_text_encoder'))

    @torch.no_grad
    def forward(self, prompts, with_mask=True):
        self.device = next(self.text_encoder.parameters()).device
        text_inputs = self.tokenizer(
            prompts,
            padding="max_length",
            max_length=self.max_length,
            truncation=True,
            return_attention_mask=True,
            return_tensors="pt",
        )
        prompt_embeds = self.text_encoder(
            text_inputs.input_ids.to(self.device),
            attention_mask=text_inputs.attention_mask.to(self.device) if with_mask else None,
        )
        return prompt_embeds.last_hidden_state, prompt_embeds.pooler_output

fastvideo.models.encoders.bert.HunyuanClip.forward

forward(prompts, with_mask = True)
Source
@torch.no_grad
def forward(self, prompts, with_mask=True):
    self.device = next(self.text_encoder.parameters()).device
    text_inputs = self.tokenizer(
        prompts,
        padding="max_length",
        max_length=self.max_length,
        truncation=True,
        return_attention_mask=True,
        return_tensors="pt",
    )
    prompt_embeds = self.text_encoder(
        text_inputs.input_ids.to(self.device),
        attention_mask=text_inputs.attention_mask.to(self.device) if with_mask else None,
    )
    return prompt_embeds.last_hidden_state, prompt_embeds.pooler_output