#2020th of 24 in Image and videoFor image, video and design, image and design
generative-models
Stability AI reference code for SDXL, Stable Video Diffusion, SV3D and SV4D
- Stars
- 27.3k
- License
- MIT
- Last commit
- Dec 2025
- Last release
- Jul 2023
- Language
- Python
26,795 on 31 Mar 202627,305 on 20 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Config-driven PyTorch codebase (the `sgm` package) from Stability AI for sampling and training diffusion models: SDXL 0.9/1.0, SDXL-Turbo, Stable Video Diffusion, SV3D and SV4D 2.0. It ships Streamlit and Gradio demos plus standalone sampling scripts, with PyTorch Lightning training configs. Weights are downloaded separately from Hugging Face into `checkpoints/`.
Who it is for: Researchers and engineers running or training Stability diffusion models
Strengths
- Covers text-to-image, image-to-video, multi-view (SV3D) and video-to-4D (SV4D 2.0) in one repo
- Config-driven modules via YAML and instantiate_from_config, with example training configs
- Streamlit and Gradio demos plus simple CLI sampling scripts
- Low-VRAM flags such as --encoding_t, --decoding_t and --img_size for video models
Weaknesses
- Last release was 2023-07-27; newer models are announced only in the README
- Weights not bundled; several have research-only or RAIL++-M licenses separate from the MIT code
- Tested on Python 3.10 only; package does not declare dependencies
- Manual install of PyTorch, requirements and sdata; no Docker image
What it needs
- GPU required
- Needs PyTorch 2.0, PyTorch Lightning, Streamlit, Hugging Face model weights, Stability-AI/datapipelines (sdata), rembg (optional)
- Models: SDXL-base-1.0, SDXL-refiner-1.0, SDXL-Turbo, Stable Video Diffusion (SVD, SVD-XT), SV3D_u
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