Train a diffusion model Unconditional image generation is a popular application of diffusion models that generates images that look like those in the dataset used for training. Typically, the best results are obtained from finetuning a pretrained model on a specific dataset. You can find many of these checkpoints on the Hub, but if you can’t find one you like, you can always train your own! This tutorial will teach you how to train a UNet2DModel from scratch on a subset of the Smithsonian Butterflies dataset to generate your own 🦋 butterflies 🦋. 💡 This training tutorial is based on the Training with 🧨 Diffusers notebook. For additional details and context about diffusion models like how they work, check out the notebook! Before you begin, make sure you have 🤗 Datasets installed to load and preprocess image datasets, and 🤗 Accelerate, to simplify training on any number of GPUs. The following command will also install TensorBoard to visualize training metrics (you can also use Weights & Biases to track your training).   Copied # uncomment to install the necessary libraries in Colab