Simplifying, stabilizing, and scaling continuous-time consistency models

Diffusion models have revolutionized generative AI, enabling remarkable advances in generating realistic images, 3D models, audio, and video. However, despite their impressive results, these models are slow at sampling.

We are sharing a new approach, called sCM, which simplifies the theoretical formulation of continuous-time consistency models, allowing us to stabilize and scale their training for large scale datasets. This approach achieves comparable sample quality to leading diffusion models, while using only two sampling steps. We are also sharing our research paper⁠(opens in a new window) to support further progress in this field.

Talks with ChatGPT

Simplifying, stabilizing, and scaling continuous-time consistency models

Diffusion models have revolutionized generative AI, enabling remarkable advances in generating realistic images, 3D models, audio, and video. However, despite their impressive results, these models are slow at sampling.

We are sharing a new approach, called sCM, which simplifies the theoretical formulation of continuous-time consistency models, allowing us to stabilize and scale their training for large scale datasets. This approach achieves comparable sample quality to leading diffusion models, while using only two sampling steps. We are also sharing our research paper⁠(opens in a new window) to support further progress in this field.

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542 viewsDec 6, 2024 at 07:31