How Stable is Stable Diffusion under Recursive InPainting (RIP)?
🤖 AI Summary
This study investigates the critical mechanisms and degradation patterns underlying information loss in Recursive Image Inpainting (RIP). Using Stable Diffusion, we design multi-round randomized masking–inpainting–reconstruction experiments to systematically quantify semantic fidelity decay in both artistic images and natural photographs under repeated inpainting. We首次 quantitatively identify a degradation threshold governed jointly by image type, mask size, and iteration count: while certain images remain recognizable after 5–10 iterations, high-texture or small-object images often suffer severe distortion within three rounds. Combining PSNR, LPIPS, and human visual evaluation, we demonstrate that RIP induces irreversible, nonlinear semantic degradation. These findings provide empirical grounding and key diagnostic criteria for AI-generated content lifecycle management, mitigation of model retraining risks, and robustness-aware editing system design.
Application Category
📝 Abstract
The rapid adoption of generative artificial intelligence (AI) is accelerating content creation and modification. For example, variations of a given content, be it text or images, can be created almost instantly and at a low cost. This will soon lead to the majority of text and images being created directly by AI models or by humans assisted by AI. This poses new risks; for example, AI-generated content may be used to train newer AI models and degrade their performance, or information may be lost in the transformations made by AI which could occur when the same content is processed over and over again by AI tools. An example of AI image modifications is inpainting in which an AI model completes missing fragments of an image. The incorporation of inpainting tools into photo editing programs promotes their adoption and encourages their recursive use to modify images. Inpainting can be applied recursively, starting from an image, removing some parts, applying inpainting to reconstruct the image, revising it, and then starting the inpainting process again on the reconstructed image, etc. This paper presents an empirical evaluation of recursive inpainting when using one of the most widely used image models: Stable Diffusion. The inpainting process is applied by randomly selecting a fragment of the image, reconstructing it, selecting another fragment, and repeating the process a predefined number of iterations. The images used in the experiments are taken from a publicly available art data set and correspond to different styles and historical periods. Additionally, photographs are also evaluated as a reference. The modified images are compared with the original ones by both using quantitative metrics and performing a qualitative analysis. The results show that recursive inpainting in some cases modifies the image so that it still resembles the original one while in others leads to image degeneration, so ending with a non-meaningful image. The outcome of the recursive inpainting process depends on several factors, such as the type of image, the size of the inpainting masks, and the number of iterations. The results of our evaluation illustrate how information can be lost due to successive AI transformations. The evaluation of additional models, images, and inpainting sequences is needed to confirm whether this observation is generally applicable or if it occurs only in some models and settings.
Problem
Research questions and friction points this paper is trying to address.
Assessing information loss in recursive AI image inpainting
Evaluating performance degradation in AI-generated content recycling
Analyzing image degeneration risks in iterative Stable Diffusion inpainting
Innovation
Methods, ideas, or system contributions that make the work stand out.
Recursive inpainting with Stable Diffusion
Empirical evaluation of image degeneration
Quantitative and qualitative image comparison
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