完毕结语终于翻译完成了,边读边复现边翻译,一边又一边,用了一个多星期, 后面可能还会解读每幅图,每个表格,每个数学公式,先看看吧,大佬们的工作是真的严谨和丰富,这篇论文算是视频生成的奠基性工作之一吧…
Latent 经常被翻译作“潜在”,这里根据本人习惯一律翻译作“隐”或者“隐式”。 封面图来自https://www.bilibili.com/opus/842962566786318355。 摘要 通过将图像形成过程分解为顺序的去噪自编码器,扩散模型(Diffusion Model, DM)在图像数据等方面达到了最先进的合成结果。此外,其公式允许使用一种指导机制对图像生成过程...
LDM可以通过聚合低分辨率的图像有效训练出超分辨率生成模型,基于之前提到的条件建模机制。在第一个实验中,论文依照SR3论文中数据处理方法,采用双三次插值将训练图像进行4倍的下采样。在OpenImage数据集上训练出模型LDM−4LDM−4(VQ-reg正则化),直接将低分辨率图像输入给UNet网络结构即ττ是恒等变换。定性...
In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn dif...
摘要原文 Diffusion Probabilistic models have been shown to generate state-of-the-artresults on several competitive image synthesis benchmarks but lack alow-dimensional, interpretable latent space, and are slow at generation. On theother hand, Variational Autoencoders (VAEs) typically have access to...
semantics and style, while varying the non-essential details absent from the image representation. We use diffusion models for the decoder and experiment with both autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality ...
Code URL:https://github.com/CompVis/latent-diffusion TL;DR 2021 年 runway 和慕尼黑路德维希·马克西米利安大学出品的文章,开源社区大名顶顶的文生图模型 stable diffusion 背后的论文。提出 Latent Diffusion Models,基于 latent space 进行 diffusion,降低计算量需求。
记录一个自己翻译的中文版本,可以反复观看。 论文: https://arxiv.org/abs/2307.01952摘要本文提出SDXL,一种用于文本到图像合成的潜在扩散模型。与先前版本Stable Diffusion相比,SDXL利用了三倍大的UNet主干:…
training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and...