mirror of
https://gitee.com/Tencent/Hunyuan3D-1.git
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80 lines
3.9 KiB
Python
80 lines
3.9 KiB
Python
# Open Source Model Licensed under the Apache License Version 2.0 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 ("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 torch
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from .utils import seed_everything, timing_decorator, auto_amp_inference
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from .utils import get_parameter_number, set_parameter_grad_false
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from diffusers import HunyuanDiTPipeline, AutoPipelineForText2Image
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class Text2Image():
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def __init__(self, pretrain="weights/hunyuanDiT", device="cuda:0", save_memory=False):
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'''
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save_memory: if GPU memory is low, can set it
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'''
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self.save_memory = save_memory
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self.device = device
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self.pipe = AutoPipelineForText2Image.from_pretrained(
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pretrain,
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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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)
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set_parameter_grad_false(self.pipe.transformer)
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print('text2image transformer model', get_parameter_number(self.pipe.transformer))
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if not save_memory:
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self.pipe = self.pipe.to(device)
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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,残缺,多余的手指,变异的手," \
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"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," \
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"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"
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@torch.no_grad()
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@timing_decorator('text to image')
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@auto_amp_inference
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def __call__(self, *args, **kwargs):
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if self.save_memory:
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self.pipe = self.pipe.to(self.device)
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torch.cuda.empty_cache()
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res = self.call(*args, **kwargs)
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self.pipe = self.pipe.to("cpu")
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else:
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res = self.call(*args, **kwargs)
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torch.cuda.empty_cache()
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return res
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def call(self, prompt, seed=0, steps=25):
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'''
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inputs:
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prompr: str
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seed: int
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steps: int
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return:
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rgb: PIL.Image
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'''
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prompt = prompt + ",白色背景,3D风格,最佳质量"
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seed_everything(seed)
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generator = torch.Generator(device=self.device)
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if seed is not None: generator = generator.manual_seed(int(seed))
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rgb = self.pipe(prompt=prompt, negative_prompt=self.neg_txt, num_inference_steps=steps,
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pag_scale=1.3, width=1024, height=1024, generator=generator, return_dict=False)[0][0]
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torch.cuda.empty_cache()
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return rgb
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