PFB-Diff：面向文本驱动图像编辑的渐进式特征混合扩散方法
PFB-Diff: Progressive Feature Blending Diffusion for Text-driven Image Editing
AI总结：
本文提出PFB-Diff方法，通过多层级渐进式特征混合和交叉注意力掩码机制，在无需训练的情况下实现高质量的文本驱动图像编辑，有效解决目标/背景替换和属性编辑任务中的伪影问题。
摘要：
扩散模型已经展示了其生成多样且高质量图像的能力，引发了人们对其在真实图像编辑应用中潜力的广泛关注。然而，现有基于扩散的局部图像编辑方法常常由于在潜在层面对加噪目标图像与扩散潜变量进行混合而产生不希望的伪影，因为这些潜变量缺乏维持图像一致性所需的语义信息。为了解决这些问题，我们提出了PFB-Diff，一种面向扩散图像编辑的渐进式特征混合方法。与以往方法不同，PFB-Diff通过多层级特征混合将文本引导的生成内容无缝整合到目标图像中。深层特征中编码的丰富语义以及从高层到低层的渐进混合方案确保了编辑图像的语义连贯性和高质量。此外，我们在交叉注意力层中引入了一种注意力掩码机制，将特定词语的影响限制在期望区域内，进一步提升了背景编辑和多目标替换的性能。PFB-Diff能够有效处理多种编辑任务，包括目标/背景替换和目标属性编辑。我们的方法在编辑准确性和图像质量方面展现了优越的性能，且无需微调或训练。我们的实现代码可在 https://github.com/CMACH508/PFB-Diff 获取。
英文摘要：
Diffusion models have demonstrated their ability to generate diverse and high-quality images, sparking considerable interest in their potential for real image editing applications. However, existing diffusion-based approaches for local image editing often suffer from undesired artifacts due to the latent-level blending of the noised target images and diffusion latent variables, which lack the necessary semantics for maintaining image consistency. To address these issues, we propose PFB-Diff, a Progressive Feature Blending method for Diffusion-based image editing. Unlike previous methods, PFB-Diff seamlessly integrates text-guided generated content into the target image through multi-level feature blending. The rich semantics encoded in deep features and the progressive blending scheme from high to low levels ensure semantic coherence and high quality in edited images. Additionally, we introduce an attention masking mechanism in the cross-attention layers to confine the impact of specific words to desired regions, further improving the performance of background editing and multi-object replacement. PFB-Diff can effectively address various editing tasks, including object/background replacement and object attribute editing. Our method demonstrates its superior performance in terms of editing accuracy and image quality without the need for fine-tuning or training. Our implementation is available at https://github.com/CMACH508/PFB-Diff.
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。
