These models are trained on an aesthetic subset of the LAION-5B dataset created by the DeepFloyd team at Stability AI, which is then further filtered to remove adult content using LAION’s NSFW filter. For more details about how Stable Diffusion 2 works and how it differs from the original Stable Diffusion, please refer to the official announcement post. The architecture of Stable Diffusion 2 is more or less identical to the original Stable Diffusion model so check out it’s API documentation for how to use Stable Diffusion 2. We recommend using the DPMSolverMultistepScheduler as it gives a reasonable speed/quality trade-off and can be run with as little as 20 steps. Stable Diffusion 2 is available for tasks like text-to-image, inpainting, super-resolution, and depth-to-image: Task Repository text-to-image (512x512) stabilityai/stable-diffusion-2-base text-to-image (768x768) stabilityai/stable-diffusion-2 inpainting stabilityai/stable-diffusion-2-inpainting super-resolution stable-diffusion-x4-upscaler depth-to-image stabilityai/stable-diffusion-2-depth Here are some examples for how to use Stable Diffusion 2 for each task: Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! If you’re interested in using one of the official checkpoints for a task, explore the CompVis, Runway, and Stability AI Hub organizations!  Text-to-image   Copied from diffusers import DiffusionPipeline, DPMSolverMultistepScheduler