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arXiv · 2312.03594

A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting

Junhao Zhuang·Yanhong Zeng·Wenran Liu·Chun Yuan·Kai Chen
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Abstract

Advancing image inpainting is challenging as it requires filling user-specified regions for various intents, such as background filling and object synthesis. Existing approaches focus on either context-aware filling or object synthesis using text descriptions. However, achieving both tasks simultaneously is challenging due to differing training strategies. To overcome this challenge, we introduce PowerPaint, the first high-quality and versatile inpainting model that excels in multiple inpainting tasks. First, we introduce learnable task prompts along with tailored fine-tuning strategies to guide the model's focus on different inpainting targets explicitly. This enables PowerPaint to accomplish various inpainting tasks by utilizing different task prompts, resulting in state-of-the-art performance. Second, we demonstrate the versatility of the task prompt in PowerPaint by showcasing its effectiveness as a negative prompt for object removal. Moreover, we leverage prompt interpolation techniques to enable controllable shape-guided object inpainting, enhancing the model's applicability in shape-guided applications. Finally, we conduct extensive experiments and applications to verify the effectiveness of PowerPaint. We release our codes and models on our project page: https://powerpaint.github.io/.

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Junhao Zhuang, Yanhong Zeng, Wenran Liu, Chun Yuan, Kai Chen. 2024-07-23. A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting. https://arxiv.org/abs/2312.03594

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