DDPM 定义 这个部分,作者直接用了一个类去写,写得非常好:improved_diffusion/gaussian_diffusion.py 结合文章和代码去理解这个DDPM 给一个x0, 定义这个前向过程q,每次加一个高斯噪声: 公式1: 表达这个从x0,生成x1到xT的概率。因为是马尔可夫链,就是已知前一时刻,求后一时刻的概率的连乘。 公式2:已经知道前一...
github:https://github.com/openai/improved-diffusion 贡献 噪声机制更新,使用cosine 引入了方差项的学习 方差学习 faster sampling DDPM是一步一步的往上采样,这里有一个strided sampling schedule,也就是每次网上采样100步,参数都没变化。个人感觉没啥意义。 zbloom:扩散模型笔记1-概述/发展 参考:v=kuUAvTh8Zxk...
Get a GitHub badge TaskDatasetModelMetric NameMetric ValueGlobal RankResultBenchmark Image GenerationCIFAR-10Improved DDPM (DINOv2)FD212.3# 5 Compare Image GenerationCIFAR-10Improved DDPMFID3.27# 54 Compare bits/dimension2.94# 25 Compare Image GenerationImageNet 256x256Improved DDPMFID12.3# 45 ...
Improved-DDPM Improved Denoising Diffusion Probabilistic Models Abstract Denoising diffusion probabilistic models (DDPM) are a class of generative models which have recently been shown to produce excellent samples. We show that with a few simple modifications, DDPMs can also achieve competitive log-like...
Denoising diffusion probabilistic models (DDPM) are a class of generative models which have re- cently been shown to produce excellent sam- ples. We show that with a few simple modifi- cations, DDPMs can also achieve competitive log-likelihoods while maintaining high sample quality. Additionally...
代码阅读:github.com/openai/impro 摘要 DDPM作为生成模型:DDPM(Denoising Diffusion Probabilistic Models)是一种生成模型,当时被证明能产生高质量的样本。 简单修改带来的提升:研究表明,通过一些简单的修改,DDPM不仅能保持生成高质量样本的能力,还能获得竞争性的对数似然值。 反向扩散过程的优化:发现学习反向扩散过程中的...
In recent years, generative models, including VAE [10], GAN [11], and DDPM [12], have made significant progress in computer vision. However, natural language generation presents unique challenges due to its discrete and temporal nature. To address these challenges, a common approach is to use...