In this tutorial, we’ll talk about two popular deep-learning models for image generation, Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). First, we’ll briefly introduce these two approaches, and we’ll mainly focus on their differences. We’ll discuss their behalve...
Implementation My implementation of the VAE for image generation is based on the TensorFlow framework. I started with a naive implementation of a basic autoencoder for image reconstruction. I then extended this implementation to include the VAE architecture. The VAE consists of two parts: an encoder...
OD-VAE——时空双维度VAE突破界限，实现高效任意长视频重建 1. OD-VAE: An Omni-dimensional Video Compressor for Improving Latent Video Diffusion Model 变分自编码器（VAE）将视频压缩为潜在表示，是视频扩散模型（LVDM模型）的关键前处理组件。在相同的重建质量下，VAE对视频的压缩越充分，LVDM模型的效率就越高...
但是人工设计image的几个关键特征是很难的,我们需要一种自动的方法,来学习image的关键特征。 AE 针对上面的问题,AE(AutoEncoder)尝试用2个神经网络Encoder和Decoder拟合压缩和解压缩的过程,Encoder 将 image x 压缩为一个低维 feature z,Decoder 将 feature z 解压缩为原图 image x’。这个压缩的得到的中间feature ...
3.2 Image generation 作者设置噪声w为0,然后在混合高斯的不同成分中采样来生成手写数字,结果发现不同成分对应了不同的数字,这就证明GMVAE成功地学习到了不同的类别。 作者还固定混合高斯的成分,然后改变噪声w,发现噪声w实际上控制了数字的风格。 这两个实验的结果如下图所示: 4. Conclusion GMVAE改进了传统的VAE...
We present results on ImageNet, COCOStuff, and FFHQ datasets, and we compared the obtained images with results with VQGAN . The interpretability of the training process for the latent representation is significantly increased maintaining the structured bottleneck idea. This has practical benefits, for...
We explore the use of Vector Quantized Variational AutoEncoder (VQ-VAE) models for large scale image generation. To this end, we scale and enhance the autoregressive priors used in VQ-VAE to generate synthetic samples of much higher coherence and fidelity than possible before. We use simple fee...
不同的深度生成模型之间存在怎样的共性?近日,来自 CMU 和 Petuum 的四位研究者 Zhiting Hu、Zichao Yang、Ruslan Salakhutdinov 和邢波在 arXiv 上发表了一篇论文介绍了他们的研究成果,即构建了 GAN 和 VAE 深度生成建模方法之间的形式联系。机器之心对该研究论文进行了摘要介绍,更多详情请查阅原论文。
Stable diffusion: High-Resolution Image Synthesis with Latent Diffusion Models Robin DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation 揭秘EVA-CLIP-18B:官方论文深度解读,探索史上最强大 CLIP 模型 Stable Cascade 深度解析:从原理到实践,探索 StabilityAI 的最新文图生成技术...
DistVAE: A patch parallelism distributed VAE implement for high resolution generation By providing a set of adapter interfaces, this project allows users to quickly convert vae-related implementations in the diffusers library into parallel versions on multiple gpu's, enabling non-intrusive parallelisatio...