Simplifying, stabilizing, and scaling continuous-time consistency models
OpenAI Blog
Researchers improved continuous-time consistency models to match the sample quality of leading diffusion models while requiring only two sampling steps instead of many. The models achieved this through simplified architecture, enhanced training stability, and improved scaling methods. This reduces the computational cost of image generation, making the approach more practical for deployment.
Why it matters
We’ve simplified, stabilized, and scaled continuous-time consistency models, achieving comparable sample quality to leading diffusion models, while using only two sampling steps.
