cross-posted from: https://lemmy.world/post/3549390

stable-diffusion.cpp

Introducing stable-diffusion.cpp, a pure C/C++ inference engine for Stable Diffusion! This is a really awesome implementation to help speed up home inference of diffusion models.

Tailored for developers and AI enthusiasts, this repository offers a high-performance solution for creating and manipulating images using various quantization techniques and accelerated inference.

Key Features:

  • Efficient Implementation: Utilizing plain C/C++, it operates seamlessly like llama.cpp and is built on the ggml framework.
  • Multiple Precision Support: Choose between 16-bit, 32-bit float, and 4-bit to 8-bit integer quantization.
  • Optimized Performance: Experience memory-efficient CPU inference with AVX, AVX2, and AVX512 support for x86 architectures.
  • Versatile Modes: From original txt2img to img2img modes and negative prompt handling, customize your processing needs.
  • Cross-Platform Compatibility: Runs smoothly on Linux, Mac OS, and Windows.

Getting Started

Cloning, building, and running are made simple, and detailed examples are provided for both text-to-image and image-to-image generation. With an array of options for precision and comprehensive usage guidelines, you can easily adapt the code for your specific project requirements.

git clone --recursive https://github.com/leejet/stable-diffusion.cpp
cd stable-diffusion.cpp
  • If you have already cloned the repository, you can use the following command to update the repository to the latest code.
cd stable-diffusion.cpp
git pull origin master
git submodule update

More Details

  • Plain C/C++ implementation based on ggml, working in the same way as llama.cpp
  • 16-bit, 32-bit float support
  • 4-bit, 5-bit and 8-bit integer quantization support
  • Accelerated memory-efficient CPU inference
    • Only requires ~2.3GB when using txt2img with fp16 precision to generate a 512x512 image
  • AVX, AVX2 and AVX512 support for x86 architectures
  • Original txt2img and img2img mode
  • Negative prompt
  • stable-diffusion-webui style tokenizer (not all the features, only token weighting for now)
  • Sampling method
    • Euler A
  • Supported platforms
    • Linux
    • Mac OS
    • Windows

This is a really exciting repo. I’ll be honest, I don’t think I am as well versed in what’s going on for diffusion inference - but I do know more efficient and effective methods running those models are always welcome by people frequently using diffusers. Especially for those who need to multi-task and maintain performance headroom.