Exploring the Behavior of Open-Source Diffusion Model Inpainting Algorithms

Abstract

The present study aimed to examine the performance of an open-source diffusion model inpainting algorithm under varying conditions of inpainting strength and mask radius. However, the results obtained were unexpected and raise significant concerns. Our findings indicate that the algorithm not only modifies the pixels within the designated mask, as intended, but also alters pixels out side of the mask, even those that are unrelated to the inpainted subject. This unexpected behavior has potentially significant implications, particularly in the context of utilizing this algorithm for fine-detailed medical imaging, where the consequences of inaccurate inpainting could be severe. Utilizing heatmaps and the calculation of mean squared error (MSE), we observed that areas of the image characterized by consistent pixel color, such as the sky or water, tend to undergo minimal alteration during the inpainting process. However, areas of the image that are more varied in color and texture, such as mountain ridges and grass, tend to experience higher, yet relatively low levels of alteration. The heatmaps further reveal that the edge of the inpainting mask is a particularly sensitive area for pixel alteration. The second experiment conducted in this study, which involved varying the radius of the inpainting mask while keeping the strength constant, showed that as the mask radius increases, the MSE may increase or decrease in a trend like manner. This study provides valuable insights into the behavior of the inpainting algorithm, and highlights areas that may require further research. It is important to investigate the relationship between inpainting strength and mask radius in more detail, as well as identify the specific characteristics of images that contribute to their lower MSE. Additionally, the unexpected results of this study regarding the alteration of pixels outside the masked area require further investigation and consideration in the field of utilizing diffusion models for inpainting.