Stable Diffusion's VAE is a neural network that encodes and decodes images into a compressed "latent" format. The encoder performs 48x lossy compression, and the decoder generates new detail to fill in the gaps.
(Calling this model a "VAE" is sort of a misnomer - it's an encoder with some very slight KL regularization, and a conditional GAN decoder)
This document is a big pile of various links with more info.
So you want to generate images with neural networks. You're in luck! VAEs are here to save the day. They're simple to implement, they generate images in one inference step (unlike those awful slow autoregressive models) and (most importantly) VAEs are 🚀🎉🎂🥳 theoretically grounded 🚀🎉🎂🥳 (unlike those scary GANs - don't look at the GANs)!
The idea of VAE is so simple, even an AI chatbot could explain it:
torch.randn(1024)), interprets that noise as decisions about what to generate, and produces corresponding real-looking images. You want to train this network with nice simple image-space MSE loss against your dataset of real images.
Yoav Goldberg, April 2023.
With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much
A list of public training logs from neural network image generation models, since I think they're interesting.
| function svg2png(svgContent, width, height, callback) { | |
| // svgContent should be in base64 | |
| let svgData = svgContent; | |
| let canvas = document.createElement("canvas"); | |
| let context = canvas.getContext("2d"); | |
| canvas.width = width; | |
| canvas.height = height; | |
| let image = new Image(); |
These resources (articles, books, and videos) are useful when you're starting to learn the language, or when you're learning a specific part of the language. This an opinionated list, no doubt. I've compiled this list from writing and teaching Clojure over the last 10 years.
| # ------------------------------------------------------------------ | |
| # EDIT: I eventually found a faster way to run SD on macOS, via MPSGraph (~0.8s / step on M1 Pro): | |
| # https://github.com/madebyollin/maple-diffusion | |
| # The original CoreML-related code & discussion is preserved below :) | |
| # ------------------------------------------------------------------ | |
| # you too can run stable diffusion on the apple silicon GPU (no ANE sadly) | |
| # | |
| # quick test portraits (each took 50 steps x 2s / step ~= 100s on my M1 Pro): | |
| # * https://i.imgur.com/5ywISvm.png |
| ### Procesar datos SSalud para modelo compartimental | |
| using CSV, DataFrames, Dates | |
| ## Cargar datos | |
| @time begin | |
| fechaDate = Date(2022,6,16) | |
| archivo = "220616COVID19MEXICO.csv" |
Run 3 commands below when you prepare your PR
nbdev_build_lib
nbdev_build_docs —fname {path the to file}
nbdev_clean_nbs