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| from .utils import Prompter | |
| from transformers import BertModel, T5EncoderModel, BertTokenizer, AutoTokenizer | |
| import warnings | |
| class HunyuanDiTPrompter(Prompter): | |
| def __init__( | |
| self, | |
| tokenizer_path="configs/hunyuan_dit/tokenizer", | |
| tokenizer_t5_path="configs/hunyuan_dit/tokenizer_t5" | |
| ): | |
| super().__init__() | |
| self.tokenizer = BertTokenizer.from_pretrained(tokenizer_path) | |
| with warnings.catch_warnings(): | |
| warnings.simplefilter("ignore") | |
| self.tokenizer_t5 = AutoTokenizer.from_pretrained(tokenizer_t5_path) | |
| def encode_prompt_using_signle_model(self, prompt, text_encoder, tokenizer, max_length, clip_skip, device): | |
| text_inputs = tokenizer( | |
| prompt, | |
| padding="max_length", | |
| max_length=max_length, | |
| truncation=True, | |
| return_attention_mask=True, | |
| return_tensors="pt", | |
| ) | |
| text_input_ids = text_inputs.input_ids | |
| attention_mask = text_inputs.attention_mask.to(device) | |
| prompt_embeds = text_encoder( | |
| text_input_ids.to(device), | |
| attention_mask=attention_mask, | |
| clip_skip=clip_skip | |
| ) | |
| return prompt_embeds, attention_mask | |
| def encode_prompt( | |
| self, | |
| text_encoder: BertModel, | |
| text_encoder_t5: T5EncoderModel, | |
| prompt, | |
| clip_skip=1, | |
| clip_skip_2=1, | |
| positive=True, | |
| device="cuda" | |
| ): | |
| prompt = self.process_prompt(prompt, positive=positive) | |
| # CLIP | |
| prompt_emb, attention_mask = self.encode_prompt_using_signle_model(prompt, text_encoder, self.tokenizer, self.tokenizer.model_max_length, clip_skip, device) | |
| # T5 | |
| prompt_emb_t5, attention_mask_t5 = self.encode_prompt_using_signle_model(prompt, text_encoder_t5, self.tokenizer_t5, self.tokenizer_t5.model_max_length, clip_skip_2, device) | |
| return prompt_emb, attention_mask, prompt_emb_t5, attention_mask_t5 | |