(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...
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...
本综述(Diffusion Models: A Comprehensive Survey of Methods and Applications)来自加州大学&Google Research的Ming-Hsuan Yang、斯坦福大学(OpenAI)的Yang Song(Score SDE一作)、北京大学崔斌实验室以及CMU、UCLA、蒙特利尔Mila研究院等众研究团队,首次对现有的扩散生成模型(diffusion model)进行了全面的总结分析,从diffus...
DIPO（Model-free Online RL with DIffusion POlicy）算法[16]，DiffCPS（Diffusion Model based Constrain...
数学功底好的话，强烈推荐Google的diffusion model教程：Understanding Diffusion Models: A Unified ...
Diffusion Models in Vision: A Survey Denoising diffusion models represent a recent emerging topic in computer vision, demonstrating remarkable results in the area of generative modeling. A diffusion model is a deep generative model that is based on two stages, a forward diffusion stage and a ...
DreaMoving: A Human Dance Video Generation Framework based on Diffusion Models - Dec., 2023 MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model Nov., 2023 Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation Nov., 2023 MagicDance: Rea...
022 (2023-08-25) Diff-Retinex Rethinking Low-light Image Enhancement with A Generative Diffusion Model https://arxiv.org/pdf/2308.13164.pdf 023 (2023-08-24) A Survey of Diffusion Based Image Generation Models Issues and Their Solutions
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 ...
扩散概率模型(DPMs)在高分辨率图像生成方面显示出显著性能,但由于通常需要大量采样步骤,其采样效率仍有待提高。高阶ODE求解在DPMs中的应用的最新进展使得能够以更少的采样步骤生成高质量图像。然而,大多数采样方法仍使用均匀的时间步长,在使用少量步骤时并不是最优的。