论文:arxiv.org/abs/2307.0195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:模型参数的增加主要是由于使用了更多的注意block和一个更大的交叉注意上下文,SDXL使用了第二个文本编码器。本文设计了多种新颖的调节方案,并在多个宽高比上训练SDXL
论文链接:High-Resolution Image Synthesis with Latent Diffusion Models官方实现:CompVis/latent-diffusion、CompVis/stable-diffusion 这一篇文章的内容是 Latent Diffusion Models(LDM),也就是大名鼎鼎的 Stable Diffusion。先前的扩散模型一直面临的比较大的问题是采样空间太大,学习的噪声维度和图像的维度是相同的。当...
VQ正则化:在解码器中使用向量量化层 3.2:Latent Diffusion Models Diffusion Models 扩散模型:通过逐步对正态分布变量去噪,学习数据分布 p(x),对应学习固定长度为 T 的马尔可夫链的反向过程 图像合成模型,依赖于变分下界的重新加权变体 目标函数: Generative Modeling of Latent Representations 通过训练的感知压缩模型(...
这在机器学习中被称为流形假设。 二.隐式扩散模型 Latent Diffusion Models 与DDPM的过程类似,前向与反向扩散都是在潜空间里完成的,只不过Zt是Latent Feature,Z0是AE的Encoder推理出的原始特征,ZT是纯噪声特征。所以在训练时,不再是生成一张噪点图,而是在潜空间里生成一个随机张量(Tensor),并且在给图片每一步增...
Latent Diffusion Model, High-Resolution Image Synthesis with Latent Diffusion Models 时间:21.12 机构:runway TL;DR 这篇文章介绍了一种名为潜在扩散模型(Latent Diffusion Models, LDMs)的新型高分辨率图像合成方法。LDMs通过在预训练的自编码器的潜在空间中应用扩散模型,实现了在有限计算资源下训练高质量图像合成模...
[2]: Song, Jiaming, Chenlin Meng, and Stefano Ermon. "Denoising diffusion implicit models." arXiv preprint arXiv:2010.02502 (2020). [3]: Rombach, Robin, et al. "High-resolution image synthesis with latent diffusion models." Proceedings of the IEEE/CVF Conference on Computer Vision and Pat...
Meanwhile, diffusion models have shown significant progress towards general domain image generation. In this paper, we propose to leverage the pre-trained latent diffusion model to perform the neural ISP for enhancing extremely low-light images. Specifically, to tailor the pre-trained latent diffusion...
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 first...
In this paper, we propose a novel framework for solving high-definition video inverse problems using latent image diffusion models. Building on recent advancements in spatio-temporal optimization for video inverse problems using image diffusion models, our approach leverages latent-space diffusion models ...
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows to apply them to image modification tasks such as inpainting directly ...