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: this https URL
Abstract (translated)
这篇论文介绍了TurboFill，这是一种快速的图像修复模型，通过增强几个步骤的文本到图像扩散模型，并加入一个图像修复适配器来实现高质量和高效的图像修补。虽然标准的扩散模型可以生成高质量的结果，但它们会带来高昂的计算成本。我们通过在几步蒸馏后的文本到图像模型DMD2上训练一个图像修复适配器来克服这一问题，并使用一种新颖的三步骤对抗性训练方案，以确保修复区域的真实感、结构一致性和视觉和谐。 为了评估TurboFill的效果，我们提出了两个基准测试：DilationBench用于检测不同遮罩大小下的性能表现；HumanBench则是基于复杂指令的人类反馈。实验结果表明，TurboFill在性能上超过了多步骤的BrushNet和少步骤图像修复方法，在高质量图像修补任务中设立了新的标准。 我们的项目页面链接：[this https URL](https://example.com) （注意：实际使用时请替换为正确的网址）
URL
https://arxiv.org/abs/2504.00996
