Stable Diffusion (the stablediffusion repo by Stability-AI) is an open-source implementation and reference codebase for high-resolution latent diffusion image models that power many text-to-image systems. The repository provides code for training and running Stable Diffusion-style models, instructions for installing dependencies (with notes about performance libraries like xformers), and guidance on hardware/driver requirements for efficient GPU inference and training. It’s organized as a practical, developer-focused toolkit: model code, scripts for inference, and examples for using memory-efficient attention and related optimizations are included so researchers and engineers can run or adapt the model for their own projects. The project sits within a larger ecosystem of Stability AI repositories (including inference-only reference implementations like SD3.5 and web UI projects) and the README points users toward compatible components, recommended CUDA/PyTorch versions.
Features
- Reference implementation of Stable Diffusion model code for training and inference
- Instructions and scripts to enable memory-efficient attention (xformers) for large GPUs
- Inference scripts and examples for generating images from text prompts
- Hardware and dependency guidance (CUDA/PyTorch versions, nvcc/gcc notes)
- Links and integration points with related Stability AI projects and model variants
- Suitable for research experimentation and engineering deployment
Project Samples
