Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models (2304.03322v1)

Abstract: Image inpainting refers to the task of generating a complete, natural image based on a partially revealed reference image. Recently, many research interests have been focused on addressing this problem using fixed diffusion models. These approaches typically directly replace the revealed region of the intermediate or final generated images with that of the reference image or its variants. However, since the unrevealed regions are not directly modified to match the context, it results in incoherence between revealed and unrevealed regions. To address the incoherence problem, a small number of methods introduce a rigorous Bayesian framework, but they tend to introduce mismatches between the generated and the reference images due to the approximation errors in computing the posterior distributions. In this paper, we propose COPAINT, which can coherently inpaint the whole image without introducing mismatches. COPAINT also uses the Bayesian framework to jointly modify both revealed and unrevealed regions, but approximates the posterior distribution in a way that allows the errors to gradually drop to zero throughout the denoising steps, thus strongly penalizing any mismatches with the reference image. Our experiments verify that COPAINT can outperform the existing diffusion-based methods under both objective and subjective metrics. The codes are available at https://github.com/UCSB-NLP-Chang/CoPaint/.

• The paper introduces CoPaint, which employs a Bayesian framework in each denoising step to achieve coherent image inpainting.
• The approach progressively reduces approximation errors to zero, ensuring consistency between visible and masked image regions.
• CoPaint outperforms prior methods on datasets like CelebA-HQ and ImageNet, demonstrating significant improvements in LPIPS and overall image quality.

Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models

The paper presents CoPaint, a novel approach to the task of image inpainting leveraging denoising diffusion implicit models (DDIMs). Image inpainting, a crucial problem in computer vision, involves generating a complete image from a partially visible reference image. Existing diffusion model-based methods confront issues of incoherence between revealed and unrevealed regions due to their reliance on simple replacement operations, which often fail to harmonize the unrevealed parts with the context of the revealed image portion.

CoPaint seeks to resolve these coherence issues by adopting a Bayesian framework in each denoising step, effectively ensuring that both the revealed and unrevealed regions cooperate for a harmonious result. The approach utilizes a novel approximation technique for posterior distributions within the denoising stages, achieving error reduction to zero by the final step, thereby effectively aligning the generated image with the reference.

Core Methodological Advances

1. Joint Optimization via Bayesian Framework: CoPaint proposes an innovative Bayesian approach, allowing for coherent modification of both revealed and unrevealed image segments throughout the denoising process. This method circumvents the approximations and Monte Carlo methods usually required for posterior distributions in diffusion models.
2. Gradual Error Reduction: The paper introduces a mechanism for progressively reducing approximation errors at each denoising step. The authors demonstrate that, by focusing on one-step approximations of the final image, the errors can be gradually reduced, culminating in zero by the denoising endpoint.
3. Enhanced Coherence via Time Travel: The paper incorporates additional algorithmic designs, notably a "time travel" mechanism akin to RePaint, to ensure intermediate images maintain coherence. This process involves returning temporarily to previous time steps, allowing for further refinement and consistency.

Experimental Evaluations

CoPaint outperformed existing methods on prominent datasets like CelebA-HQ and ImageNet, showcasing superior image completeness and coherence under both objective metrics, like Low Perceptual Image Patch Similarity (LPIPS), and subjective human evaluations. The paper reports a notable reduction in LPIPS compared with RePaint, solidifying the method's efficacy, particularly for complex datasets like ImageNet.

Implications and Future Directions

This research provides a significant methodological advance for diffusion model-based image inpainting by addressing longstanding incoherence issues. The practical applications of CoPaint extend beyond traditional inpainting tasks, potentially influencing related areas such as super-resolution and other image restoration tasks. Future work may focus on enhancing computational efficiency, exploring multi-step approximations with improved resource distribution, and mitigating bias inherent in the model training data.

In sum, CoPaint marks an important development in the domain of image generation, offering enhanced coherence and computational efficiency in image inpainting tasks. The approach not only fortifies theoretical understanding but also opens avenues for new applications in automated image editing and restoration. This research lays the groundwork for further innovations in generative modeling, broadening the horizon for image processing applications.