GitHub - cumulo-autumn/StreamDiffusion: StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation
GitHub - cumulo-autumn/StreamDiffusion: StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generationhttps://github.com/cumulo-autumn/StreamDiffusion

GitHub - cumulo-autumn/StreamDiffusion: StreamDiffusion: A Pipeline-Level Solution for Real-Time Interactive Generation

🚀 Revolutionize real-time interactive image generation with StreamDiffusion! 🎨 Optimize performance with stream batch processing, residual classifier-free guidance & more. 🖥️ Try out txt2img and img2img demos now! #AI #imagegeneration #StreamDiffusion

  • StreamDiffusion is a diffusion pipeline for real-time interactive image generation, optimizing performance and efficiency.
  • Key features include stream batch processing, residual classifier-free guidance, and stochastic similarity filter.
  • It efficiently manages input and output operations with IO queues and optimizes caching strategies with pre-computation for KV-caches.
  • Model acceleration tools are utilized for optimization and performance boost.
  • Installation involves cloning the repository, creating the environment, installing PyTorch, and StreamDiffusion.
  • Real-time txt2img and img2img demos are available, with usage examples provided for image-to-image and text-to-image generation.
  • SD-Turbo can be used for faster generation, enhancing performance.
  • Optional features like Stochastic Similarity Filter and Residual CFG (RCFG) can be enabled for specific functionalities.
  • The development team includes Aki, Ararat, Chenfeng Xu, and others.
  • Acknowledgements go to contributors and authors of LCM-LoRA, KohakuV2, and SD-Turbo.
  • StreamDiffusion offers a pipeline-level solution for real-time interactive image generation.