A Task is Worth One Word: Learning with Task Prompts for High-Quality Versatile Image Inpainting
This addresses the problem of achieving high-quality, versatile inpainting for users needing both context-aware filling and object synthesis, representing a novel integration rather than an incremental improvement.
The paper tackles the challenge of versatile image inpainting for tasks like background filling and object synthesis by introducing PowerPaint, a model that uses learnable task prompts to achieve state-of-the-art performance across multiple inpainting tasks.
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/.