GitHub

Generative Models by Stability AI

What is the project about?

This project is about developing and releasing state-of-the-art generative models, focusing primarily on diffusion models for image and video generation. It includes models like Stable Diffusion (various versions), Stable Video Diffusion, SDXL, SD-Turbo, SV3D, and SV4D.

What problem does it solve?

The project addresses the need for high-quality, efficient, and controllable generation of visual content (images and videos). It tackles challenges in:

  • Text-to-Image Generation: Creating images from textual descriptions.
  • Image-to-Image Generation: Modifying existing images based on prompts or other inputs.
  • Image-to-Video Generation: Creating short videos from a single input image.
  • Novel View Synthesis: Generating videos showing an object from different viewpoints, even rotating views, based on an input image or video.
  • 4D Generation: Creating 4D scenes (3D + time) from video inputs.
  • Speed of Generation: Creating images very quickly, approaching real-time generation.

What are the features of the project?

  • Multiple Generative Models: A suite of models for different tasks (text-to-image, image-to-video, 3D/4D generation).
  • High-Resolution Output: Models capable of generating high-resolution images and videos (e.g., 576x1024, 576x576).
  • Controllable Generation: Options for controlling the output, such as specifying camera paths (SV3D_p), elevations, and azimuths.
  • Efficient Sampling: Fast diffusion models (SD-Turbo, SDXL-Turbo) for rapid image creation.
  • Temporal Consistency: Video models (SVD, SV3D, SV4D) designed to maintain consistency across video frames.
  • Modular Design: A config-driven architecture that allows for flexible combination and customization of submodules.
  • Background Removal: Options and recommendations for handling background in input videos for better results.
  • Low VRAM Support: Options to run models on GPUs with limited VRAM.
  • Research Focus: Many models are initially released for research purposes, with open licenses for broader use later.
  • Streamlit Demos: Interactive web demos for easy experimentation with the models.
  • Invisible Watermarking: Generated images include an invisible watermark for identification.
  • Training Support: Provides example training configurations and supports training with PyTorch Lightning.

What are the technologies used in the project?

  • Python: The primary programming language.
  • PyTorch: The deep learning framework.
  • PyTorch Lightning: A framework for organizing and training PyTorch models.
  • Diffusion Models: The core generative modeling technique.
  • Transformers: Used in some diffusion backbones.
  • OpenCLIP: Used for text encoding in some models.
  • Hugging Face Transformers: Used for model distribution and management.
  • Streamlit: For creating interactive web demos.
  • Gradio: For creating interactive web demos.
  • rembg: (Optional) For background removal.
  • Clipdrop/SAM2: (Recommended) For high-quality foreground segmentation.
  • WebDataset: For large-scale training data handling.
  • Hatch: For PEP 517 compliant packaging.

What are the benefits of the project?

  • Open Source: Many models are released under permissive licenses, promoting research and development.
  • State-of-the-Art Results: Provides access to cutting-edge generative models.
  • Flexibility and Customization: The modular design allows researchers and developers to build upon and adapt the models.
  • Ease of Use: Streamlit demos and provided scripts simplify the process of using the models.
  • Community Engagement: Active development and updates, with news and releases regularly announced.
  • Reproducibility: Training configurations are provided.

What are the use cases of the project?

  • Content Creation: Generating images and videos for art, design, marketing, and entertainment.
  • Research: Studying and advancing the field of generative models.
  • Data Augmentation: Creating synthetic data for training other machine learning models.
  • Image Editing: Modifying and enhancing existing images.
  • 3D Modeling: Creating 3D representations of objects from images.
  • Virtual Reality/Augmented Reality: Generating content for VR/AR applications.
  • Game Development: Creating assets for games.
  • Scientific Visualization: Generating visualizations of data or simulations.
  • Rapid Prototyping: Quickly generating visual concepts.
generative-models screenshot