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…
Diffusion 模型现有问题: 训练和评估模型需要在 RGB 图像这样的高维空间中进行重复的函数评估(和梯度计算),例如,训练最强大的 Diffusion 模型通常需要数百个 GPU 天(例如,在论文 Diffusion Models Beat GANs on Image Synthesis 中为150-1000 个 V100 天)。在输入空间的噪声版本上进行重复评估也使推理变得昂贵,因此...
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 ...
Stable Diffusion 原论文。由本人翻译,不保证准确。见原文:https://arxiv.org/abs/2112.10752。 项目地址:https://github.com/CompVis/latent-diffusion。 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。
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...