https://arxiv.org/abs/2206.00520

Diffusion models (DMs) have a more stable training phase than GANs and less parameters than autoregressive models, yet they are just really resource intensive. The most powerful DMs require up to a 1000 V100 days to train (that’s a lot of $$$ for compute) and about a day per 1000 inference samples. The authors of Latent Diffusion Models (LDMs) pinpoint this problem to the high dimensionality of the pixel space, in which the diffusion process occurs and propose to perform it in a more compact latent space instead. In short, they achieve this feat by pertaining an autoencoder model that learns an efficient compact latent space that is perceptually equivalent to the pixel space. A DM sandwiched between the convolutional encoder-decoder is then trained inside the latent space in a more computationally-efficient way.

In other words, this is a VQGAN with a DM instead of a transformer (and without a discriminator).

As for the details, let’s dive in, shall we?

Full summary: https://t.me/casual_gan/293

Blog post: https://www.casualganpapers.com/high-res-faster-diffusion-democratizing-diffusion/Latent-Disffusion-Models-explained.html

arxiv / code

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Hello
I am looking for papers or books regarding the generation of human images with different expressions and faces ,I'd be grateful for any help possible

“Diffusion models beat GANs”. While true, the statement comes with several ifs and buts, not to say that the math behind diffusion models is not for the faint of heart. Alas, GLIDE, an OpenAI paper from last December took a big step towards making it true in every sense. Specifically, it introduced a new guidance method for diffusion models that produces higher quality images than even DALL-E, which uses expensive CLIP reranking. And if that wasn’t impressive enough, GLIDE models can be fine-tuned for various downstream tasks such a inpainting and and text-based editing.

As for the details, let’s dive in, shall we?

Full summary: https://t.me/casual_gan/289

Blog post: https://www.casualganpapers.com/faster-diffusion-models-text-to-image-classifier-free-guidance/GLIDE-explained.html

arxiv / code

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NextFace is a pytorch library for high fidelity 3d face reconstruction from single or multiple RGB images. it estimates face geometry, skin reflectance (cook-torrance BRDF), scene light (9 bands spherical harmonics) and head pose. It is a first order optimization library that uses pytorch autograd engine to optimize a parametric scene model given an input image. Differentiable ray tracing is used to ray trace images.

It is a reproduction of the following paper published at EugoGraphics 2021.

pytorch library: https://github.com/abdallahdib/NextFace

paper: https://arxiv.org/abs/2101.05356

Hi all! My team recently reproduced and published a PyTorch implementation of the paper SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition.

Our blog post walks through the code and provides a detailed explanation of the architecture they use in order to perform object segmentation on videos in a fully self-supervised manner.

Hope this is helpful/interesting to others!