FreeInpaint: Tuning-free Prompt Alignment and Visual Rationality Enhancement in Image Inpainting

2025-12-24

AI Summary

Text-guided image inpainting faces the challenge of simultaneously achieving prompt alignment and visual coherence. This paper proposes a fine-tuning-free, plug-and-play framework that directly optimizes latent variables of diffusion models during inference. Our method addresses this challenge through two key innovations: (1) a novel prior-guided optimization mechanism operating in the noise space, and (2) a composite guidance objective tailored for inpainting—integrating attention-region constraints with multi-step intermediate latent guidance. Crucially, the approach preserves the pre-trained model intact while jointly enhancing prompt fidelity and visual coherence. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches across multiple benchmarks and quantitative metrics, exhibiting strong generalization capability and practical plug-and-play utility.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Text-guided image inpainting endeavors to generate new content within specified regions of images using textual prompts from users. The primary challenge is to accurately align the inpainted areas with the user-provided prompts while maintaining a high degree of visual fidelity. While existing inpainting methods have produced visually convincing results by leveraging the pre-trained text-to-image diffusion models, they still struggle to uphold both prompt alignment and visual rationality simultaneously. In this work, we introduce FreeInpaint, a plug-and-play tuning-free approach that directly optimizes the diffusion latents on the fly during inference to improve the faithfulness of the generated images. Technically, we introduce a prior-guided noise optimization method that steers model attention towards valid inpainting regions by optimizing the initial noise. Furthermore, we meticulously design a composite guidance objective tailored specifically for the inpainting task. This objective efficiently directs the denoising process, enhancing prompt alignment and visual rationality by optimizing intermediate latents at each step. Through extensive experiments involving various inpainting diffusion models and evaluation metrics, we demonstrate the effectiveness and robustness of our proposed FreeInpaint.

Problem

• Improves prompt alignment in text-guided image inpainting
• Enhances visual rationality of inpainted image regions
• Optimizes diffusion latents without fine-tuning for better results

Innovation

• Optimizes diffusion latents during inference for faithfulness
• Uses prior-guided noise optimization for attention steering
• Implements composite guidance objective for denoising enhancement