Imagen Editor与EditBench：推进与评估文本引导的图像修复
Imagen Editor and EditBench: Advancing and Evaluating Text-Guided Image Inpainting
AI总结：
本文提出Imagen Editor级联扩散模型，通过训练时目标检测器提议掩码提升文本引导图像修复的忠实度，并构建EditBench基准，经人工评估验证其在文本-图像对齐上优于DALL-E 2和Stable Diffusion。
摘要：
文本引导的图像编辑在支持创意应用方面可能产生变革性影响。一个关键挑战是生成既忠实于输入文本提示、又与输入图像保持一致的编辑结果。我们提出了Imagen Editor，这是一个级联扩散模型，通过在文本引导的图像修复任务上对Imagen进行微调而构建。Imagen Editor的编辑结果忠实于文本提示，这是通过在训练期间使用目标检测器来提议修复掩码实现的。此外，Imagen Editor通过将级联流程以原始高分辨率图像为条件，捕捉输入图像中的精细细节。为了改进定性和定量评估，我们引入了EditBench，一个用于文本引导图像修复的系统性基准。EditBench在自然图像和生成图像上评估修复编辑，涵盖物体、属性和场景。通过在EditBench上进行广泛的人工评估，我们发现训练期间的物体掩码带来了文本-图像对齐的全面改进——使得Imagen Editor优于DALL-E 2和Stable Diffusion——并且作为一个群体，这些模型在物体渲染方面优于文本渲染，在处理材质/颜色/尺寸属性方面优于处理数量/形状属性。
英文摘要：
Text-guided image editing can have a transformative impact in supporting creative applications. A key challenge is to generate edits that are faithful to input text prompts, while consistent with input images. We present Imagen Editor, a cascaded diffusion model built, by fine-tuning Imagen on text-guided image inpainting. Imagen Editor's edits are faithful to the text prompts, which is accomplished by using object detectors to propose inpainting masks during training. In addition, Imagen Editor captures fine details in the input image by conditioning the cascaded pipeline on the original high resolution image. To improve qualitative and quantitative evaluation, we introduce EditBench, a systematic benchmark for text-guided image inpainting. EditBench evaluates inpainting edits on natural and generated images exploring objects, attributes, and scenes. Through extensive human evaluation on EditBench, we find that object-masking during training leads to across-the-board improvements in text-image alignment -- such that Imagen Editor is preferred over DALL-E 2 and Stable Diffusion -- and, as a cohort, these models are better at object-rendering than text-rendering, and handle material/color/size attributes better than count/shape attributes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
