Tutorials
Each tutorial is a Jupyter notebook in the repository’s tutorials/ folder, and each page here shows the notebook with the outputs from its last complete run. Open one in Colab to run it yourself: the first cell installs Dew from GitHub, and the notebook says at the top what hardware it needs.
Notebooks 01 to 04 are about image diffusion and build on each other: 04 samples from the checkpoint that 02 trains. The others stand alone. If you have not used Dew before, read the quickstart first; it trains a model in a few seconds on a CPU and introduces the pieces every notebook uses.
Basics of Diffusion based generative models from ScratchPlease bear with me at times and read through the entire notebook thoroughly.
Train a diffusion model with DewThis is part 2 of the series, and so I would strongly recommend going through Notebook 01 first.Text to image with classifier-free guidanceThis is part 3 of the series, and it builds directly on notebook 02, so I would recommend going through that one first.
Samplers and schedulesA trained diffusion model only tells us, for a noisy image, what it thinks the clean image is.
Train a language modelSo far we have only generated images. But the same Trainer trains language models too, and in this notebook we shall train a small decoder-only transformer on Tiny Shakespeare, 40,000 lines from…Learn image representations with I-JEPAThe earlier notebooks trained models that produce something, an image or some text.Scale one training run across many devicesWhen I trained text-to-image models from scratch with FlaxDiff, Dew's predecessor, it was on 128 TPU v4 chips.Load a pretrained decoder and keep training itIn notebook 05 we trained a language model from random weights. But most of the time, you would start from a model someone else already trained, on far more data than we have.
Train a diffusion model with DewThis is part 2 of the series, and so I would strongly recommend going through Notebook 01 first.Text to image with classifier-free guidanceThis is part 3 of the series, and it builds directly on notebook 02, so I would recommend going through that one first.Samplers and schedulesA trained diffusion model only tells us, for a noisy image, what it thinks the clean image is.Train a language modelSo far we have only generated images. But the same Trainer trains language models too, and in this notebook we shall train a small decoder-only transformer on Tiny Shakespeare, 40,000 lines from…Learn image representations with I-JEPAThe earlier notebooks trained models that produce something, an image or some text.Scale one training run across many devicesWhen I trained text-to-image models from scratch with FlaxDiff, Dew's predecessor, it was on 128 TPU v4 chips.Load a pretrained decoder and keep training itIn notebook 05 we trained a language model from random weights. But most of the time, you would start from a model someone else already trained, on far more data than we have.