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README.md

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 license: apache-2.0
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+tags:
+- diffusion
+- image-to-image
+- depth-estimation
+- optical-flow
+- amodal-segmentation
+---
+
+# Scaling Properties of Diffusion Models for Perceptual Tasks
+
+### CVPR 2025
+
+**Rahul Ravishankar\*, Zeeshan Patel\*, Jathushan Rajasegaran, Jitendra Malik**
+
+[[Paper](https://arxiv.org/abs/2411.08034)] · [[Project Page](https://scaling-diffusion-perception.github.io/)]
+
+
+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.
+
+
+## Getting started
+
+You can download our DiT-MoE Generalist model [here](https://huggingface.co/zeeshanp/scaling_diffusion_perception/blob/main/dit_moe_generalist.pt). Please see instructions on how to use our model in the [GitHub README](https://github.com/scaling-diffusion-perception/scaling-diffusion-perception)·