mirror of
https://git.datalinker.icu/kijai/ComfyUI-Hunyuan3DWrapper.git
synced 2025-12-10 05:14:29 +08:00
87 lines
3.8 KiB
Python
Executable File
87 lines
3.8 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 DiffusionPipeline
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from diffusers import EulerAncestralDiscreteScheduler
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class Multiview_Diffusion_Net():
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def __init__(self, config) -> None:
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self.device = config.device
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self.view_size = 512
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multiview_ckpt_path = config.multiview_ckpt_path
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current_file_path = os.path.abspath(__file__)
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custom_pipeline_path = os.path.join(os.path.dirname(current_file_path), '..', 'hunyuanpaint')
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pipeline = DiffusionPipeline.from_pretrained(
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multiview_ckpt_path,
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custom_pipeline=custom_pipeline_path, torch_dtype=torch.float16)
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pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config,
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timestep_spacing='trailing')
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pipeline.set_progress_bar_config(disable=True)
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self.pipeline = pipeline.to(self.device)
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def seed_everything(self, 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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def __call__(self, input_image, control_images, camera_info):
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self.seed_everything(0)
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input_image = input_image.resize((self.view_size, self.view_size))
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for i in range(len(control_images)):
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control_images[i] = control_images[i].resize((self.view_size, self.view_size))
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if control_images[i].mode == 'L':
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control_images[i] = control_images[i].point(lambda x: 255 if x > 1 else 0, mode='1')
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kwargs = dict(generator=torch.Generator(device=self.pipeline.device).manual_seed(0))
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num_view = len(control_images) // 2
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normal_image = [[control_images[i] for i in range(num_view)]]
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position_image = [[control_images[i + num_view] for i in range(num_view)]]
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camera_info_gen = [camera_info]
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camera_info_ref = [[0]]
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kwargs['width'] = self.view_size
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kwargs['height'] = self.view_size
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kwargs['num_in_batch'] = num_view
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kwargs['camera_info_gen'] = camera_info_gen
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kwargs['camera_info_ref'] = camera_info_ref
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kwargs["normal_imgs"] = normal_image
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kwargs["position_imgs"] = position_image
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mvd_image = self.pipeline(input_image, num_inference_steps=30, **kwargs).images
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return mvd_image
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