ComfyUI Extension: ComfyUI_CustomNet

Authored by smthemex

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you can using customnet in comfyUI

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    README

    A CustomNet node for ComfyUI

    A CustomNet node for ComfyUI

    CustomNet: Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models. CustomNet From: CustomNet

    Update

    2024/08/11 --同步官方的内绘模型及代码,优化模型加载方式,现在模型跟常规的SD模型在一个地方,优化模型加载方式,

    1.Installation

    In the .\ComfyUI \ custom_node directory, run the following:

    git clone https://github.com/smthemex/ComfyUI_CustomNet.git     
    

    2.requirements

    每个人的环境不同,但是carvekit-colab是必须装的,是内置的脱底工具包,懒得去掉了,你可以先用其他sam节点处理物体图。首次运行,会安装carvekit-colab的模型文件,无梯子的注意。
    need carvekit-colab==4.1.0

    3 Download the model

    3.1 normal:
    下载customnet_v1.pth模型,并放在ComfyUI/models/checkpoints/目录下:
    Download the weights of Customnet “customnet_v1.pth” and put it to “ComfyUI/models/checkpoints/” link

    └── ComfyUI/models/checkpoints/
        ├── customnet_v1.pth
    

    3.2 inpainting:
    下载customnet_inpaint_v1.pt模型,并放在ComfyUI/models/checkpoints/目录下:
    Download the weights of Customnet “customnet_inpaint_v1.pt” and put it to “ComfyUI/models/checkpoints/” link

    └── ComfyUI/models/checkpoints/
        ├── customnet_inpaint_v1.pt
    

    3.3 clip and carvekit: 首次使用会下载3个的模型文件,须连外网:,分别是
    clip:文件目录一般在C:/User/你的用户名/.cache/clip/ViT-L-14.pt
    carvekit的2个脱底模型:
    目录C:/User/你的用户名/.cache/carvekit/checkpoints/fba/fba_matting.pth
    目录C:/User/你的用户名/.cache/carvekit/checkpoints/tracer_b7/tracer_b7.pth

    6 Tips

    ---白底的物体图得到最好的效果; ---底模训练就是256的,所以没法做大图,除非腾讯把大图的模型放出来。
    ---The object image with a white background achieves the best effect;

    5 Example

    normal 常规脱底置于提示测的背景前面,最新的演示; Latest Presentation

    inpainting 内绘模型,最新的演示; Latest Presentation

    polar 主体上下视角 既往的演示, Previous demonstrations

    zaimuth 主体左右视角 既往的演示, Previous demonstrations

    position X0 Y0 主体在背景中的位置 既往的演示, Previous demonstrations

    6 Citation

    @misc{yuan2023customnet,
        title={CustomNet: Zero-shot Object Customization with Variable-Viewpoints in Text-to-Image Diffusion Models}, 
        author={Ziyang Yuan and Mingdeng Cao and Xintao Wang and Zhongang Qi and Chun Yuan and Ying Shan},
        year={2023},
        eprint={2310.19784},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
    }
    }