This paper introduces TurboFill, a fast image inpainting model that augments a few-step text-to-image diffusion model with an inpainting adapter to achieve high-quality and efficient inpainting. While standard diffusion models can generate high quality results, they incur high computational cost. We address this limitation by directly training an inpainting adapter on a few-step distilled text-to-image model, specifically DMD2, with 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 assesses model performance across variable mask sizes, and HumanBench, based on human feedback for complex prompts. Experiments show that TurboFill significantly outperforms both existing diffusion-based inpainting models and few-step inpainting methods, setting a new benchmark for practical high-performance inpainting tasks.