[R] The Missing U for Efficient Diffusion Models

Anew paper proposes replacing the standard discrete U-Net architecture in diffusion models with a continuous U-Net leveraging neural ODEs. This reformulation enables modeling the denoising process continuously, leading to significant efficiency gains:

* Up to 80% faster inference
* 75% reduction in model parameters
* 70% fewer FLOPs
* Maintains or improves image quality

Key technical contributions:

* Dynamic neural ODE block modeling latent representation evolution using second-order differential equations
* Adaptive time embeddings to condition dynamics on diffusion timesteps …

architecture continuous denoising diffusion diffusion models efficiency faster inference machinelearning modeling paper parameters process standard