From 04fa7baea8fc573aed07b675e8c1b35ff0f583a0 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Thu, 21 Jul 2022 16:54:55 +0200 Subject: [PATCH] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 974bc0a20a..ed94e3c554 100644 --- a/README.md +++ b/README.md @@ -29,9 +29,9 @@ More precisely, 🤗 Diffusers offers: In order to get started, we recommend taking a look at two notebooks: -- The [Diffusers](https://github.com/patrickvonplaten/notebooks/blob/master/Diffusers.ipynb) notebook, which showcases an end-to-end example of usage for diffusion models, schedulers and pipelines. +- The [Getting started with Diffusers](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb) notebook, which showcases an end-to-end example of usage for diffusion models, schedulers and pipelines. Take a look at this notebook to learn how to use the pipeline abstraction, which takes care of everything (model, scheduler, noise handling) for you, but also to get an understanding of each independent building blocks in the library. -- The [Training diffusers](https://colab.research.google.com/gist/anton-l/cde0c3643e991ad7dbc01939865acaf4/diffusers_training_example.ipynb) notebook, which summarizes diffuser model training methods. This notebook takes a step-by-step approach to training your +- The [Training a diffusers model](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/training_example.ipynb) notebook, which summarizes diffuser model training methods. This notebook takes a step-by-step approach to training your diffuser model on an image dataset, with explanatory graphics. ## Examples