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arXiv · 2504.00996

TurboFill: Adapting Few-step Text-to-image Model for Fast Image Inpainting

Liangbin Xie·Daniil Pakhomov·Zhonghao Wang·Zongze Wu·Ziyan Chen·Yuqian Zhou·Haitian Zheng·Zhifei Zhang·Zhe Lin·Jiantao Zhou·Chao Dong
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Abstract

This paper introduces TurboFill, a fast image inpainting model that enhances a few-step text-to-image diffusion model with an inpainting adapter for high-quality and efficient inpainting. While standard diffusion models generate high-quality results, they incur high computational costs. We overcome this by training an inpainting adapter on a few-step distilled text-to-image model, DMD2, using a novel 3-step adversarial training scheme to ensure realistic, structurally consistent, and visually harmonious inpainted regions. To evaluate TurboFill, we propose two benchmarks: DilationBench, which tests performance across mask sizes, and HumanBench, based on human feedback for complex prompts. Experiments show that TurboFill outperforms both multi-step BrushNet and few-step inpainting methods, setting a new benchmark for high-performance inpainting tasks. Our project page: https://liangbinxie.github.io/projects/TurboFill/

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Liangbin Xie, Daniil Pakhomov, Zhonghao Wang, Zongze Wu, Ziyan Chen, Yuqian Zhou, Haitian Zheng, Zhifei Zhang, Zhe Lin, Jiantao Zhou, Chao Dong. 2025-04-01. TurboFill: Adapting Few-step Text-to-image Model for Fast Image Inpainting. https://arxiv.org/abs/2504.00996

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