Text-to-image            When you think of diffusion models, text-to-image is usually one of the first things that come to mind. Text-to-image generates an image from a text description (for example, “Astronaut in a jungle, cold color palette, muted colors, detailed, 8k”) which is also known as a prompt. From a very high level, a diffusion model takes a prompt and some random initial noise, and iteratively removes the noise to construct an image. The denoising process is guided by the prompt, and once the denoising process ends after a predetermined number of time steps, the image representation is decoded into an image. Read the How does Stable Diffusion work? blog post to learn more about how a latent diffusion model works. You can generate images from a prompt in 🤗 Diffusers in two steps: Load a checkpoint into the AutoPipelineForText2Image class, which automatically detects the appropriate pipeline class to use based on the checkpoint:   Copied from diffusers import AutoPipelineForText2Image