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Comfyui-CatVTON

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Last updated
2024-10-03

Comfyui-CatVTON is a specialized extension for ComfyUI that enables virtual try-on capabilities using diffusion models, allowing users to visualize how clothing items might look on different subjects. It is designed to be lightweight and efficient, making it accessible even on systems with limited resources.

  • Supports a lightweight network architecture with a total of 899.06 million parameters.
  • Utilizes parameter-efficient training, with only 49.57 million parameters being trainable.
  • Requires less than 8GB of VRAM for inference at a resolution of 1024x768.

Context

Comfyui-CatVTON is an adaptation of the CatVTON model tailored for use within the ComfyUI framework. Its primary purpose is to facilitate virtual clothing try-ons, allowing users to apply various garments to images of individuals seamlessly.

Key Features & Benefits

This extension stands out due to its lightweight design, which ensures that it can run efficiently on a variety of hardware setups. The parameter-efficient training method means that it can adapt to new styles without demanding extensive computational resources, making it practical for users looking to experiment with fashion visualization.

Advanced Functionalities

The tool incorporates advanced capabilities such as simplified inference processes that require minimal VRAM, enabling users to generate high-quality outputs without needing high-end graphics cards. This makes it particularly beneficial for users with limited hardware.

Practical Benefits

By integrating Comfyui-CatVTON into their workflows, users can significantly enhance their ability to visualize clothing options on different models. This not only improves the quality of virtual try-ons but also streamlines the process, allowing for quicker iterations and greater creative control.

Credits/Acknowledgments

The Comfyui-CatVTON project is based on the original CatVTON model created by Zheng-Chong, with modifications made to fit the ComfyUI framework. The repository is available on GitHub and includes references to the original work for further exploration.