| from .base_prompter import BasePrompter | |
| from ..models.hunyuan_dit_text_encoder import HunyuanDiTCLIPTextEncoder | |
| from ..models.stepvideo_text_encoder import STEP1TextEncoder | |
| from transformers import BertTokenizer | |
| import os, torch | |
| class StepVideoPrompter(BasePrompter): | |
| def __init__( | |
| self, | |
| tokenizer_1_path=None, | |
| ): | |
| if tokenizer_1_path is None: | |
| base_path = os.path.dirname(os.path.dirname(__file__)) | |
| tokenizer_1_path = os.path.join( | |
| base_path, "tokenizer_configs/hunyuan_dit/tokenizer") | |
| super().__init__() | |
| self.tokenizer_1 = BertTokenizer.from_pretrained(tokenizer_1_path) | |
| def fetch_models(self, text_encoder_1: HunyuanDiTCLIPTextEncoder = None, text_encoder_2: STEP1TextEncoder = None): | |
| self.text_encoder_1 = text_encoder_1 | |
| self.text_encoder_2 = text_encoder_2 | |
| def encode_prompt_using_clip(self, prompt, max_length, device): | |
| text_inputs = self.tokenizer_1( | |
| prompt, | |
| padding="max_length", | |
| max_length=max_length, | |
| truncation=True, | |
| return_attention_mask=True, | |
| return_tensors="pt", | |
| ) | |
| prompt_embeds = self.text_encoder_1( | |
| text_inputs.input_ids.to(device), | |
| attention_mask=text_inputs.attention_mask.to(device), | |
| ) | |
| return prompt_embeds | |
| def encode_prompt_using_llm(self, prompt, max_length, device): | |
| y, y_mask = self.text_encoder_2(prompt, max_length=max_length, device=device) | |
| return y, y_mask | |
| def encode_prompt(self, | |
| prompt, | |
| positive=True, | |
| device="cuda"): | |
| prompt = self.process_prompt(prompt, positive=positive) | |
| clip_embeds = self.encode_prompt_using_clip(prompt, max_length=77, device=device) | |
| llm_embeds, llm_mask = self.encode_prompt_using_llm(prompt, max_length=320, device=device) | |
| llm_mask = torch.nn.functional.pad(llm_mask, (clip_embeds.shape[1], 0), value=1) | |
| return clip_embeds, llm_embeds, llm_mask | |