Scaling Properties of Diffusion Models for Perceptual Tasks

• URL: http://arxiv.org/abs/2411.08034v3
• Date: Sun, 17 Nov 2024 03:45:07 GMT
• Title: Scaling Properties of Diffusion Models for Perceptual Tasks
• Authors: Rahul Ravishankar, Zeeshan Patel, Jathushan Rajasegaran, Jitendra Malik,
• Abstract summary: We show how diffusion models benefit from scaling training and test-time compute for perceptual tasks. Our models achieve competitive performance to state-of-the-art methods using significantly less data and compute.
• Score: 39.89262997776814
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 amodal segmentation under the framework of image-to-image translation, and show how diffusion models benefit from scaling training and test-time compute for these perceptual tasks. Through a careful analysis of these scaling properties, we formulate compute-optimal training and inference recipes to scale diffusion models for visual perception tasks. Our models achieve competitive performance to state-of-the-art methods using significantly less data and compute. To access our code and models, see https://scaling-diffusion-perception.github.io .

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