# Stability AI Generative Models — Reference Implementation for SDXL and SVD

> The official codebase from Stability AI containing modular implementations of Stable Diffusion XL, Stable Video Diffusion, and other generative model architectures for image and video synthesis.

## Install

Save in your project root:

# Stability AI Generative Models — Reference Implementation for SDXL and SVD

## Quick Use
```bash
git clone https://github.com/Stability-AI/generative-models.git
cd generative-models
pip install -e .
# Run SDXL text-to-image
python scripts/demo/sampling.py --config configs/inference/sd_xl_base.yaml
```

## Introduction
This repository is Stability AI's official reference implementation for their generative model family, including Stable Diffusion XL (SDXL), Stable Video Diffusion (SVD), and related architectures. It provides a modular, research-friendly codebase built on PyTorch that separates model components (encoders, denoisers, samplers) into composable pieces.

## What Stability Generative Models Does
- Implements Stable Diffusion XL for high-resolution text-to-image generation
- Provides Stable Video Diffusion for image-to-video and text-to-video synthesis
- Includes modular denoiser, conditioner, and sampler components for custom pipelines
- Supports various sampling methods including DDPM, DDIM, Euler, and DPM-Solver
- Enables fine-tuning and LoRA training on custom datasets

## Architecture Overview
The codebase follows a modular design using SGM (Stability Generative Models) primitives. A DiffusionEngine orchestrates the forward and sampling processes by composing a Denoiser network, one or more Conditioners (text, image, timestep), a Discretization scheme, and a Sampler. This separation allows mixing components freely for experimentation without rewriting the training or inference loop.

## Self-Hosting & Configuration
- Requires Python 3.10+ and PyTorch 2.0+ with CUDA support
- Download model weights from Hugging Face Hub or Stability AI's model registry
- Configure generation parameters via YAML config files in the configs directory
- Use the provided Streamlit demos for interactive image and video generation
- Set up training with the sgm training scripts and custom dataset loaders

## Key Features
- Modular architecture allowing independent swapping of samplers, conditioners, and denoisers
- Support for both latent diffusion and pixel-space diffusion approaches
- Video generation via temporal attention layers in Stable Video Diffusion
- Configurable refinement pipeline for SDXL (base + refiner two-stage generation)
- Research-oriented with clean separation between model logic and training infrastructure

## Comparison with Similar Tools
- **diffusers (Hugging Face)** — user-friendly pipeline API; Stability's repo is the canonical research implementation
- **AUTOMATIC1111 WebUI** — end-user interface; this repo is the underlying model code
- **ComfyUI** — visual node editor for diffusion workflows; this provides the raw model components
- **InvokeAI** — polished creative tool; Stability's repo targets researchers and developers
- **kohya-ss** — training scripts; this repo includes both inference and the original training approach

## FAQ
**Q: What hardware is needed to run SDXL?**
A: SDXL base requires a GPU with at least 8GB VRAM for inference. Training requires significantly more (40GB+ recommended).

**Q: Can I use these models commercially?**
A: Check the specific model license. SDXL uses a permissive CreativeML Open RAIL++-M license allowing commercial use with some restrictions.

**Q: How does this differ from Hugging Face diffusers?**
A: This is the original research implementation. Hugging Face diffusers ports these models into their standardized pipeline format for easier deployment.

**Q: Does it support training from scratch?**
A: Yes. The training scripts and configs allow both fine-tuning from pretrained checkpoints and training new models from scratch.

## Sources
- https://github.com/Stability-AI/generative-models
- https://stability.ai/research

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