README.md

---
license: apache-2.0
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)·