# Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach

**Authors:** Shaofeng Zhang, Jinfa Huang, Qiang Zhou, Zhibin Wang, Fan Wang, Jiebo Luo, Junchi Yan  
**arXiv:** [2401.15652](https://arxiv.org/abs/2401.15652)  
**Date:** 2024-01-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.15652v1/

## Key points

- The goal of image outpainting is to generate additional content beyond the original boundaries of an input sub-image.
- A recent paper has made significant advancements in image outpainting by addressing two key unresolved issues:
- Introducing a method for outpainting with arbitrary and continuous multiples without restrictions.
- Presenting a technique for achieving outpainting in a single step, even for large expansion multiples.
- The approach taken does not rely on a pre-trained backbone network, setting it apart from previous state-of-the-art methods.
- During training, randomly cropped views from the same image are utilized to capture arbitrary relative positional information.
- The proposed method, PQDiff, based on a diffusion-based generator under a Positional Query scheme, has demonstrated superior performance compared to existing approaches on benchmarks such as Scenery (21.512), Building Facades (25.310), and WikiArts (36.212).
- PQDiff significantly reduces processing time compared to benchmark SOTA methods under different outpainting settings like 2.25x, 5x, and 11.7x expansions - only taking 40.6%, 20.3%, and 10.2% of the time respectively.
- This paper represents a significant advancement in image outpainting techniques by introducing novel approaches that address key challenges in the field and demonstrate superior performance on various benchmarks.

## Summary

In the field of image outpainting, the goal is to generate additional content beyond the original boundaries of an input sub-image. This task is crucial in content generation but still poses challenges for generative models. A recent paper has made significant advancements in image outpainting by addressing two key unresolved issues in existing literature. Firstly, the paper introduces a method for outpainting with arbitrary and continuous multiples, without any restrictions. Secondly, it presents a technique for achieving outpainting in a single step, even for large expansion multiples. One notable aspect of this work is that it does not rely on a pre-trained backbone network, which sets it apart from previous state-of-the-art (SOTA) outpainting methods. The approach taken involves utilizing randomly cropped views from the same image during training to capture arbitrary relative positional information. By feeding one view and positional embeddings as queries, the model can reconstruct another view. During inference, images with arbitrary expansion multiples are generated by inputting an anchor image along with its corresponding positional embeddings. Of particular significance is the one-step outpainting capability introduced in this paper. Unlike previous methods that require multiple iterations to achieve a final output with increased multiples, this new approach enables direct one-step outpainting. The proposed method, known as PQDiff and based on a diffusion-based generator under a Positional Query scheme, has been evaluated on public benchmarks and has demonstrated superior performance compared to existing approaches. Specifically,PQDiff has achieved state-of-the-art FID scores on datasets such as Scenery (21.512), Building Facades (25.310), and WikiArts (36.212). Furthermore, when tested under different outpainting settings like 2.25x, 5x,and 11.7x expansions,PQDiff significantly reduces processing time compared to benchmark SOTA methods - only taking 40.6%, 20.3%, and 10.2% of the time respectively. Overall, this paper represents a significant advancement in image outpainting techniques by introducing novel approaches that address key challenges in the field and demonstrate superior performance on various benchmarks.