# MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation

**Authors:** Omer Bar-Tal, Lior Yariv, Yaron Lipman, Tali Dekel  
**arXiv:** [2302.08113](https://arxiv.org/abs/2302.08113)  
**Date:** 2023-02-16  
**Summary page:** https://www.summarizepaper.com/en/paper/2302.08113v1/

## Key points

- Recent advancements in text-to-image generation using diffusion models have improved image quality
- User controllability and fast adaptation to new tasks are challenges addressed through expensive re-training or ad-hoc adaptations
- MultiDiffusion is a unified framework that enables versatile and controllable image generation without further training or fine-tuning
- MultiDiffusion combines multiple diffusion generation processes with shared parameters or constraints through optimization
- MultiDiffusion can generate high-quality and diverse images while adhering to user-provided controls such as aspect ratio and spatial guiding signals
- Comparison with baselines shows state-of-the-art controlled generation quality even compared to task-specific methods
- MultiDiffusion is computationally efficient and does not introduce overhead
- Diffusion models are generative probabilistic models used for approximating data distributions, gaining popularity in various domains
- MultiDiffusion offers enhanced user controllability and adaptability for text-to-image generation without extensive re-training or fine-tuning.

## Summary

Recent advancements in text-to-image generation using diffusion models have significantly improved the quality of generated images. However, achieving user controllability and fast adaptation to new tasks still pose challenges that are currently addressed through expensive re-training and fine-tuning processes or ad-hoc adaptations for specific image generation tasks. In this work, the authors propose MultiDiffusion, a unified framework that enables versatile and controllable image generation without the need for further training or fine-tuning. The key component of MultiDiffusion is a new generation process that combines multiple diffusion generation processes with shared parameters or constraints through an optimization task. The authors demonstrate that MultiDiffusion can be applied to generate high-quality and diverse images while adhering to user-provided controls such as desired aspect ratio (e.g., panorama) and spatial guiding signals ranging from tight segmentation masks to bounding boxes. They compare their approach with relevant baselines and show that it achieves state-of-the-art controlled generation quality even when compared to methods specifically trained for these tasks. Additionally, MultiDiffusion is computationally efficient and does not introduce any overhead. The paper also provides an overview of related work on diffusion models, which are generative probabilistic models used to approximate data distributions. Diffusion models have gained popularity due to their success in learning complex distributions and generating diverse high-quality samples in various domains such as images, videos, 3D scenes, and motion sequences. Overall, this work presents MultiDiffusion as a promising framework for text-to-image generation with enhanced user controllability and adaptability offering a practical solution for generating high-quality images while incorporating user defined constraints without the need for extensive re-training or fine tuning. The project webpage provides additional information about the implementation and results of MultiDiffusion.