Diffusion Model-Based Image Editing: A Survey (TPAMI 2025) - SiatMMLab/Awesome-Diffusion-Model-Based-Image-Editing-Methods
(1) PixArt-α, a Transformer-based T2I diffusion model, which significantly reduces computational demands of training while maintaining competitive image generation quality to the current state-of-the-art image generators, reaching near-commercial application standards. (2) to achieve this goal, three...
作者将intruction-base image editing任务建模为生成任务,并用diffusion model进行求解。核心创新点有两个 详细定义了instruction-based image edit处理的任务,并设计了一个高效高质量的数据构建方法。 为提升模型对instruction的理解能力,引入learnable task embedding,能较好的解决上述问题。并且提出task inversion的训练方法...
Meanwhile, to ensure the controllability of the editing process, we de- sign an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance to increase the similar- ity to the exemplar image. The whole framework involves a single forward ...
Collaborative Diffusion 是一个通用框架,它不仅适用于图片生成,还可以让 text-based editing 和 mask-based editing 方法合作起来。我们利用在生成任务上训练的 Dynamic Diffusers 来预测 Influence Functions,并将其直接用到 editing 中。如下图所示: 完整的实验细节和实验结果,以及更多图片结果,请参考论文。
image. Meanwhile, to ensure the controllability of the editing process, we design an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance to increase the similarity to the exemplar image. The whole framework involves a single forward of the diffusion model without ...
Diffusion Model+Anything！(扩散模型+任何东西！)2022年的下半年注定是扩散模型发展最为迅猛和关键的半年。在经过前一年的不懈探索后，扩散模型的理论研究逐渐平稳，研究的方向逐步转向了大规模的应用实践。在这半年，在这段时间里，我们见证了众多领域的突破性应用，包括但不限于：Image Restoration的爆发应用：...
image. Meanwhile, to ensure the controllability of the editing process, we design an arbitrary shape mask for the exemplar image and leverage the classifier-free guidance to increase the similarity to the exemplar image. The whole framework involves a single forward of the diffusion model without...
DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing paper https://arxiv.org/pdf/2306.14435.pdf code https://yujun-shi.github.io/projects/dragdiffusion.html image-20230921163050042 Abstract GAN泛化性能上界由其模型大小决定 ...
Image prior 使用一个效果优秀的well-trained diffusion model(stable-diffusion)初始化作为strong image prior. Reason: 在latent-space中给定任何向量会生成可信的图像特性 利用Pre-trained的CLIP可以提取语义信息,与采用CLIP Image embedding 有相似的表示,是不错的初始化 Strong Augmentation(以下两个增强可以大大提...