三十五、Versatile Diffusion: Text, Images and Variations All in One Diffusion Model 2024.01 三十六、Diffusion Model-Based Image Editing: A Survey 2024.02 三十七、Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers 2024.03 三十八、It's All About Your Sketch: Democratising Sketch Control in...
这种接近性可以在嵌入空间中实现有意义的线性插值,而对于远处嵌入,这种插值不会表现出线性行为。 Model fine-tuning:当通过生成扩散过程时,获得的优化嵌入e_{opt}不一定会精确地导致输入图像 x,因为我们的优化运行了少量步骤(参见图 7 中的左上角图像)。因此,在方法的第二阶段,我们通过使用公式 2 中给出的相同...
Denoising diffusion models have emerged as a powerful tool for various image generation and editing tasks, facilitating the synthesis of visual content in an unconditional or input-conditional manner. The core idea behind them is learning to reverse the process of gradually adding noise to images, ...
The repository is based on our surveyDiffusion Model-Based Image Editing: A Survey. Yi Huang*, Jiancheng Huang*, Yifan Liu*, Mingfu Yan*, Jiaxi Lv*, Jianzhuang Liu*, Wei Xiong, He Zhang, Liangliang Cao, Shifeng Chen Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sci...
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：...
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...
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 ...
013 (2023-11-28) VideoAssembler Identity-Consistent Video Generation with Reference Entities using Diffusion Model https://arxiv.org/pdf/2311.17338.pdf 014 (2023-11-28) Microstructure reconstruction of 2D/3D random materials via diffusion-based deep generative models ...
SDEdit本身并没有文本引导的功能,它支持的是简笔画(Given stroke input)或在图像上用简笔画做修改(Stroke-based image editing) 论文将SDEdit与当时SoTA的图像编辑方法进行了比较。SDEdit大大提高了对guide信息的忠诚性,同时生成的图片也更满足真实性。
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...