Recursive InPainting with Stable Difussion 2 Inpainting

Recursive inpainting with Stable Difussion The dataset contains images extracted from WikiArt that have been recursively versioned using inpainting techniques with a version of the Stable Diffusion model oriented towards instructional inpainting called stable-diffusion-2-inpainting. The dataset contains two folders with two different experiments. Each experiment consists of performing recursive inpainting on 512x512 px images until reaching a percentage of pixels affected by the inpainting of 400% compared to the original image. Each experiment contains the following files: original_imgs_grid.png file with all the base images in an ordered grid. mask_64x64/[number_figure]_64x64.png with the inpainting version of [number_figure] and 64x64 mask mask_128x128/[number_figure]_128x128.png with the inpainting version of [number_figure] and 128x128 mask mask_256x256/[number_figure]_256x256.png with the inpainting version of [number_figure] and 256x256 mask lpips_distances.xlsx with the results of applying the Learned Perceptual Image Path Similarity (LPIPS) metric in its variants SqueezeNet, AlexNet, and VGG. The metrics are calculated with respect to the original image and the previous image. There are two different experiments: lpips_distances_100_images_1_seed: contains recursive inpaintings of 100 different images with a single seed. This allows the analysis of different images when performing recursive inpainting. lpips_distances_10_images_15_seeds: contains recursive inpaintings of 10 different images with 15 different seeds. This allows the analysis of the differences in inpaintings when applying different seeds in the inpainting on the same image. The folder code includes the a README.md with the instructions and the scripts to run the experiments基于稳定扩散（Stable Diffusion）的递归图像修复 本数据集包含从WikiArt中提取的图像，所有图像均通过采用名为stable-diffusion-2-inpainting的、面向指导性图像修复的稳定扩散模型版本所实现的图像修复技术，生成了递归迭代版本。 数据集包含两个文件夹，对应两组不同的实验。每组实验均针对512×512像素的图像执行递归图像修复，直至受修复操作影响的像素总数相较于原始图像达到400%。 每组实验包含以下文件： 1. original_imgs_grid.png：以有序网格形式排布的所有基础图像汇总文件； 2. mask_64x64/[number_figure]_64x64.png：编号为[number_figure]的图像搭配64×64掩码进行修复后的结果文件； 3. mask_128x128/[number_figure]_128x128.png：编号为[number_figure]的图像搭配128×128掩码进行修复后的结果文件； 4. mask_256x256/[number_figure]_256x256.png：编号为[number_figure]的图像搭配256×256掩码进行修复后的结果文件； 5. lpips_distances.xlsx：收录了可学习感知图像路径相似度（Learned Perceptual Image Path Similarity，LPIPS）指标的计算结果，该指标涵盖SqueezeNet、AlexNet与VGG三种变体。上述指标均以原始图像与前一次迭代生成的图像作为参照进行计算。 本数据集包含两组差异化实验： - lpips_distances_100_images_1_seed：包含100幅不同图像在单一随机种子下的递归修复结果，可用于分析递归图像修复过程中不同输入图像对结果的影响； - lpips_distances_10_images_15_seeds：包含10幅不同图像在15种不同随机种子下的递归修复结果，可用于分析同一输入图像在使用不同随机种子进行修复时，修复结果之间的差异。 代码文件夹中包含README.md文件，其中收录了实验操作说明与实验运行脚本。