This repository provides the official codebase for Consistency Models, a novel approach to generative modeling that enables fast, high-quality image synthesis. It is designed for researchers and practitioners in deep learning and computer vision interested in state-of-the-art generative models. The primary benefit is significantly accelerated sampling times compared to traditional diffusion models, with single-step generation capabilities.

How It Works

Consistency Models achieve fast sampling by distilling knowledge from pre-trained diffusion models into a single, compact model. This is accomplished through a process called "consistency distillation," where the student model learns to map any point on a diffusion trajectory to the origin in a single step. This approach allows for rapid generation without sacrificing image quality, a significant advantage over multi-step sampling methods.

Quick Start & Requirements

• Install: pip install -e . or via Docker (cd docker && make build && make run).
• Prerequisites: PyTorch, Python. GPU with CUDA is recommended for performance.
• Pre-trained models are available for ImageNet-64, LSUN Bedroom-256, and LSUN Cat-256.
• Official documentation and examples are available within the repository.

Highlighted Details

• Enables single-step and multi-step sampling for image generation.
• Supports consistency distillation, consistency training, and editing algorithms.
• Integrated into Hugging Face's diffusers library via ConsistencyModelPipeline.
• Achieves fast inference, with potential further speedups using torch.compile().

Maintenance & Community

This project is an official OpenAI release. Further details on community engagement or ongoing maintenance are not explicitly detailed in the README.

Licensing & Compatibility

The codebase is based on openai/guided-diffusion, which was initially released under the MIT license. The specific license for this repository is not explicitly stated but is implied to be permissive.

Limitations & Caveats

The repository focuses on PyTorch implementations; a separate JAX version exists for CIFAR-10 experiments. Model cards should be reviewed for specific intended uses and limitations of pre-trained checkpoints.