Open Source Project


HCP-Diffusion is a universal Stable-Diffusion toolbox based on diffusers.


HCP-Diffusion is a project developed by the Human-Computer-Physical (HCP) Lab at Sun Yat-sen University, designed to be a universal Stable-Diffusion toolbox that leverages diffusers for training Stable Diffusion models. Its core purpose is to streamline the experimentation and development process in the realm of diffusion-based generative models by providing a comprehensive toolkit. This repository stands out as a unified framework for algorithms related to diffusion models, enabling users to customize and integrate various algorithms such as LoRA, DreamArtist, and ControlNet through a modular approach. This significantly reduces the complexity and barriers to innovation in the field.

The framework is built with flexibility and scalability at its forefront, allowing developers to easily combine different algorithms like building blocks, thereby eliminating the need to dive into the intricacies of code implementation for each new project. It supports a range of training and inference optimization methods, enhancing the application of diffusion models across various tasks and datasets. With a focus on making the development process as efficient as possible, HCP-Diffusion employs a uniform configuration file to manage different components and algorithms, further improving the usability of the framework.

One of the key advantages of HCP-Diffusion is its ability to support integration with advanced optimization techniques, including deepspeed, colossal-AI, and offload, which significantly improves performance. Additionally, the inclusion of a web UI for one-click training and inference lowers the technical threshold for users, making it more accessible to a broader audience of innovators and researchers. By reducing the barriers to entry and streamlining the development and training processes, HCP-Diffusion stands as a pivotal tool in the advancement and application of diffusion models in generative AI.

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