Introducing Stable-Diffusion.cpp (Inference in Pure C/C++)
cross-posted from: 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
txt2imgtoimg2imgmodes 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.
<span style="color:#323232;">git clone --recursive https://github.com/leejet/stable-diffusion.cpp </span><span style="color:#323232;">cd stable-diffusion.cpp </span>
- If you have already cloned the repository, you can use the following command to update the repository to the latest code.
<span style="color:#323232;">cd stable-diffusion.cpp </span><span style="color:#323232;">git pull origin master </span><span style="color:#323232;">git submodule update </span>
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
txt2imgandimg2imgmode- 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.