一、Diffusion Probabilistic Models (DPMs) Diffusion-based generative models: forward/diffusion process:图中从右往左. 从x0经过好多个不同的q(xt|xt−1)到xT,相当于 VAE 中的 encoder. reverse process:图中从左往右. 从xT经过好多个不同的q(xt−1|xt)到x0,相当于 VAE 中的 decoder. (用pθ(xt...
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model 地址 https://arxiv.org/pdf/2403.12010.pdf 摘要 基于文本或单个图像提示生成多视角图像是创建3D内容的关键能力。在这个主题上有两个基本问题,一个是我们用什么数据进行训练,另一个是如何确保多视角一致性。本文介绍了一个新颖的框...
在最近挂arxiv的Elucidating the Design Space of Diffusion-Based Generative Models中,大佬们直接把diffusion ODE的生成效果调到比diffusion SDE还要好,这给diffusion ODE的后续下游应用也带来了非常大的希望。 4. (更新补充)离散时间的diffusion model:DDPM与DDIM 评论区有同学问离散时间的diffusion model,这里也补充一...
在最近挂arxiv的Elucidating the Design Space of Diffusion-Based Generative Models中，大佬们直接把diff...
Text-to-Video Generation Training-based TitlearXivGithubWebSitePub. & Date FastVideoEdit: Leveraging Consistency Models for Efficient Text-to-Video Editing - - Mar., 2024 Genie: Generative Interactive Environments - Feb., 2024 Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesi...
VGen is an open-source video synthesis codebase developed by the Tongyi Lab of Alibaba Group, featuring state-of-the-art video generative models. This repository includes implementations of the following methods: VGen can produce high-quality videos from the input text, images, desired motion, des...
本文综述了深度生成模型,特别是扩散模型(Diffusion model),如何赋予机器类似人类的想象力。扩散模型在生成逼真样本方面显示出巨大潜力,克服了变分自编码器中的后分布对齐障碍,缓解了生成对抗网络中的对抗性目标不稳定性。 扩散模型包括两个相互...
20、FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative Models 近年来,文本到图像生成模型的发展取得重大进展。 评估生成模型的质量是开发过程中的重要步骤之一。 评估过程可能会消耗大量的计算资源,使得所需的模型性能定期评估(例如监控训练进度)变得不切实际。 因此寻求通过选择文...
models have provided such inverse mappings, they are typically restricted to linear target properties such as stiffness. Here, to tailor the nonlinear response, we show that video diffusion generative models trained on full-field data of periodic stochastic cellular structures can successfully predict ...
Karras, T., Aila, T., Laine, S., & Lehtinen, T. (2022). Elucidating the Design Space of Diffusion-Based Generative Models. In Proceedings of the NeurIPS. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffus...