Quicktour Diffusion models are trained to denoise random Gaussian noise step-by-step to generate a sample of interest, such as an image or audio. This has sparked a tremendous amount of interest in generative AI, and you have probably seen examples of diffusion generated images on the internet. 🧨 Diffusers is a library aimed at making diffusion models widely accessible to everyone. Whether you’re a developer or an everyday user, this quicktour will introduce you to 🧨 Diffusers and help you get up and generating quickly! There are three main components of the library to know about: The DiffusionPipeline is a high-level end-to-end class designed to rapidly generate samples from pretrained diffusion models for inference. Popular pretrained model architectures and modules that can be used as building blocks for creating diffusion systems. Many different schedulers - algorithms that control how noise is added for training, and how to generate denoised images during inference. The quicktour will show you how to use the DiffusionPipeline for inference, and then walk you through how to combine a model and scheduler to replicate what’s happening inside the DiffusionPipeline. The quicktour is a simplified version of the introductory 🧨 Diffusers notebook to help you get started quickly. If you want to learn more about 🧨 Diffusers’ goal, design philosophy, and additional details about its core API, check out the notebook! Before you begin, make sure you have all the necessary libraries installed:   Copied # uncomment to install the necessary libraries in Colab