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