Detailed Information on Denoising Diffusion Models DM可以用信噪比SNR(t)=\frac {\alpha^2_t} {\sigma^2_t}组成的序列(\alpha_t)^T_{t=1}和(\sigma_t)^T_{t=1}来指定,故从数据样本x_0开始,定义一个前向diffusion过程q:q(x_t|x_0)=N(x_t|\alpha_tx_0,\sigma^2_tI)\\ 指定s <t时,...
这个库主要包括三大类元素:models(各种神经网络的实现,unet、vae 等)、schedulers(diffusion 相关的操作,加噪去噪等)、pipelines(high level 封装,相当于 models+schedulers,这个应该是方便用户直接用的)。 这里直接看diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py的采样过程,定义在__call__函数中:...
Pre title: SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis accepted: arXiv 2023 paper: https://arxiv.org/abs/2307.01952 co
[AS]《NaturalSpeech 2: Latent Diffusion Models are Natural and Zero-Shot Speech and Singing Synthesizers》K Shen, Z Ju, X Tan, Y Liu, Y Leng, L He, T Qin, S Zhao, J Bian [Microsoft Research Asia] (2023) O网页链接 #机器学习##人工智能##论文# û收藏 14 评论...
Latent diffusion models are a class of probabilistic models used in machine learning and natural language processing (NLP). These models are particularly useful for tasks such as image generation, language modeling, and representation learning. In this article, we will provide a comprehensive overview...
两者之间的关系主要在于Stable Diffusion是基于Latent Diffusion Models的原理开发的,它继承了LDMs在生成图像方面的一些优点,例如能够在潜在空间中有效地模拟复杂的数据分布。同时,Stable Diffusion通过特定的改进和优化,进一步提高了图像生成的质量和效率。 简而言之,Latent Diffusion提供了一种框架,而Stable Diffusion是在这个...
motivation 由于扩散模型(Diffusion Models,DM)通常直接在像素空间中操作,优化功能强大的DM通常会消耗数百个GPU天,而且由于顺序计算,扩散模型的...
此仓库是为了提升国内下载速度的镜像仓库,每日同步一次。 原始仓库:https://github.com/CompVis/latent-diffusion main 克隆/下载 git config --global user.name userName git config --global user.email userEmail 分支2 标签0 Robin Rombachformatting and info513f0092年前 ...
This paper explores the use of state-of-the-art latent diffusion models, specifically stable diffusion, to generate synthetic images for improving the robustness of visual defect segmentation in manufacturing components. Given the scarcity and imbalance of real-world defect data, synthetic data generatio...
Latent Diffusion Models (LDMs) enable high-quality image synthesis while avoiding excessive compute demands by training a diffusion model in a compressed lower-dimensional latent space. Here, we apply the LDM paradigm to high-resolution video generation, a particularly resource-intensive task. We ...