content: # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
  # except for the third-party components listed below.
  # Hunyuan 3D does not impose any additional limitations beyond what is outlined
  # in the repsective licenses of these third-party components.
  # Users must comply with all terms and conditions of original licenses of these third-party
  # components and must ensure that the usage of the third party components adheres to
  # all relevant laws and regulations.
  
  # For avoidance of doubts, Hunyuan 3D means the large language models and
  # their software and algorithms, including trained model weights, parameters (including
  # optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
  # fine-tuning enabling code and other elements of the foregoing made publicly available
  # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
  
  import os
  import random
  
  import numpy as np
  import torch
  from diffusers import AutoPipelineForText2Image
  
  
  def seed_everything(seed):
      random.seed(seed)
      np.random.seed(seed)
      torch.manual_seed(seed)
      os.environ["PL_GLOBAL_SEED"] = str(seed)
  
  
  class HunyuanDiTPipeline:
      def __init__(
          self,
          model_path="Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled",
          device='cuda'
      ):
          self.device = device
          self.pipe = AutoPipelineForText2Image.from_pretrained(
              model_path,
              torch_dtype=torch.float16,
              enable_pag=True,
              pag_applied_layers=["blocks.(16|17|18|19)"]
          ).to(device)
          self.pos_txt = ",白色背景,3D风格,最佳质量"
          self.neg_txt = "文本,特写,裁剪,出框,最差质量,低质量,JPEG伪影,PGLY,重复,病态," \
                         "残缺,多余的手指,变异的手,画得不好的手,画得不好的脸,变异,畸形,模糊,脱水,糟糕的解剖学," \
                         "糟糕的比例,多余的肢体,克隆的脸,毁容,恶心的比例,畸形的肢体,缺失的手臂,缺失的腿," \
                         "额外的手臂,额外的腿,融合的手指,手指太多,长脖子"
  
      def compile(self):
          # accelarate hunyuan-dit transformer,first inference will cost long time
          torch.set_float32_matmul_precision('high')
          self.pipe.transformer = torch.compile(self.pipe.transformer, fullgraph=True)
          # self.pipe.vae.decode = torch.compile(self.pipe.vae.decode, fullgraph=True)
          generator = torch.Generator(device=self.pipe.device)  # infer once for hot-start
          out_img = self.pipe(
              prompt='美少女战士',
              negative_prompt='模糊',
              num_inference_steps=25,
              pag_scale=1.3,
              width=1024,
              height=1024,
              generator=generator,
              return_dict=False
          )[0][0]
  
      @torch.no_grad()
      def __call__(self, prompt, seed=0):
          seed_everything(seed)
          generator = torch.Generator(device=self.pipe.device)
          generator = generator.manual_seed(int(seed))
          out_img = self.pipe(
              prompt=prompt[:60] + self.pos_txt,
              negative_prompt=self.neg_txt,
              num_inference_steps=25,
              pag_scale=1.3,
              width=1024,
              height=1024,
              generator=generator,
              return_dict=False
          )[0][0]
          return out_img
  