mirror of
https://git.datalinker.icu/kijai/ComfyUI-Hunyuan3DWrapper.git
synced 2025-12-08 20:34:28 +08:00
93 lines
3.9 KiB
Python
Executable File
93 lines
3.9 KiB
Python
Executable File
# Open Source Model Licensed under the Apache License Version 2.0
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# and Other Licenses of the Third-Party Components therein:
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# The below Model in this distribution may have been modified by THL A29 Limited
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# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
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# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
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# The below software and/or models in this distribution may have been
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# modified by THL A29 Limited ("Tencent Modifications").
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# All Tencent Modifications are Copyright (C) THL A29 Limited.
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# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
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# except for the third-party components listed below.
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# Hunyuan 3D does not impose any additional limitations beyond what is outlined
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# in the repsective licenses of these third-party components.
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# Users must comply with all terms and conditions of original licenses of these third-party
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# components and must ensure that the usage of the third party components adheres to
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# all relevant laws and regulations.
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# For avoidance of doubts, Hunyuan 3D means the large language models and
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# their software and algorithms, including trained model weights, parameters (including
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# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
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# fine-tuning enabling code and other elements of the foregoing made publicly available
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# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
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import os
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import random
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import numpy as np
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import torch
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from diffusers import AutoPipelineForText2Image
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def seed_everything(seed):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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os.environ["PL_GLOBAL_SEED"] = str(seed)
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class HunyuanDiTPipeline:
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def __init__(
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self,
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model_path="Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled",
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device='cuda'
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):
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self.device = device
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self.pipe = AutoPipelineForText2Image.from_pretrained(
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model_path,
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torch_dtype=torch.float16,
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enable_pag=True,
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pag_applied_layers=["blocks.(16|17|18|19)"]
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).to(device)
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self.pos_txt = ",白色背景,3D风格,最佳质量"
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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," \
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"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," \
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"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," \
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"额外的手臂,额外的腿,融合的手指,手指太多,长脖子"
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def compile(self):
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# accelarate hunyuan-dit transformer,first inference will cost long time
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torch.set_float32_matmul_precision('high')
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self.pipe.transformer = torch.compile(self.pipe.transformer, fullgraph=True)
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# self.pipe.vae.decode = torch.compile(self.pipe.vae.decode, fullgraph=True)
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generator = torch.Generator(device=self.pipe.device) # infer once for hot-start
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out_img = self.pipe(
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prompt='美少女战士',
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negative_prompt='模糊',
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num_inference_steps=25,
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pag_scale=1.3,
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width=1024,
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height=1024,
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generator=generator,
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return_dict=False
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)[0][0]
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@torch.no_grad()
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def __call__(self, prompt, seed=0):
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seed_everything(seed)
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generator = torch.Generator(device=self.pipe.device)
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generator = generator.manual_seed(int(seed))
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out_img = self.pipe(
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prompt=prompt[:60] + self.pos_txt,
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negative_prompt=self.neg_txt,
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num_inference_steps=25,
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pag_scale=1.3,
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width=1024,
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height=1024,
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generator=generator,
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return_dict=False
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)[0][0]
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return out_img
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