| 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 |
|
|
