Paper ID: 2411.08034

Scaling Properties of Diffusion Models for Perceptual Tasks

Rahul Ravishankar, Zeeshan Patel, Jathushan Rajasegaran, Jitendra Malik

In this paper, we argue that iterative computation with diffusion models offers a powerful paradigm for not only generation but also visual perception tasks. We unify tasks such as depth estimation, optical flow, and segmentation under image-to-image translation, and show how diffusion models benefit from scaling training and test-time compute for these perception tasks. Through a careful analysis of these scaling behaviors, we present various techniques to efficiently train diffusion models for visual perception tasks. Our models achieve improved or comparable performance to state-of-the-art methods using significantly less data and compute. To use our code and models, see this https URL .

Submitted: Nov 12, 2024