This repository contains a simple PyTorch implementation of the paper Flow Matching for Generative Modeling. 2D Flow Matching Example The gif below demonstrates mapping a single Gaussian distribution to a checkerboard distribution, with the vector field visualized. And, here is another example of moons...
[ICLR 2025] Pyramidal Flow Matching for Efficient Video Generative Modeling pyramid-flow.github.io/ Topics video-generation diffusion-models flow-matching Resources Readme License MIT license Activity Stars 2.9k stars Watchers 46 watching Forks 282 forks Report repository Releases No relea...
思路总览类似于DDPM( Denoising Diffusion Probabilistic Models),Flow Matching (FLOW MATCHING FOR GENERATIVE MODELING)思考同一个问题,能否将真实的… Jiff 小白也可以清晰理解diffusion原理: DDPM 前言现在大火的stable diffusion系列,Sora,stable video diffusion等视频生成模型都是基于了diffusion模型。而diffusion模型的...
Flow Matchingfor Generative Modeling Method Key Word:生成模型,ODE数值方法 Function: image2image, 如由噪声生成图片, 去噪, 超分辨率等 Advantage: 较Score matching无需退火, 较DDPM算法更稳定灵活 Example 将噪声分布逐渐移动到目标图像分布 x_init=torch.randn((128,2),dtype=torch.float32)# 随机噪声model=...
Besides, the separateoptimization of multiple sub-models also hinders the sharing of their acquired knowledge.This work presents an eff icient video generative modeling framework that transcends the limitationsof the previous cascaded approaches. Our motivation stems from the observation in Fig. 1a that...
Code of Pyramidal Flow Matching for Efficient Video Generative Modeling - jy0205/Pyramid-Flow 立即访问 相似资源 头号影院 免费短剧任意看 Ai一键万字论文 DeepSeek-R1插件 豆包AI聊天 Midjourney AI作图 320.AI-全球顶级AI汇聚地 秒创数字人直播助手 - 首页 ...
IT之家附上参考地址 Pyramidal Flow Matching for Efficient Video Generative Modeling New high quality AI video generator Pyramid Flow launches — and it’s fully open source! Hugging Face Github Pyramidal Flow Matching for Efficient Video Generative Modeling...
相关研究以「Diffusion probabilistic models enhance variational autoencoder for crystal structure generative modeling」为题，发布在 Nature 上。论文地址：https://www.nature.com/articles/s41598-024-51400-4 2023 年，Google DeepMind 材料团队发布用于材料探索的图神经网络模型 GNoME，在短时间内发现了 220 万种...
论文标题:Mean Flows for One-step Generative Modeling 论文地址:https://arxiv.org/pdf/2505.13447v1 文章提出了一种名为 MeanFlow 的单步生成建模框架,通过引入平均速度(average velocity)的概念来改进现有的流匹配方法,并在 ImageNet 256×256 数据集上取得了显著优于以往单步扩散 / 流模型的结果,FID 分数达到...
Exploring the vast and largely uncharted territory of amino acid sequences is crucial for understanding complex protein functions and the engineering of novel therapeutic proteins. Whilst generative machine learning has advanced protein sequence modellin