Stable Diffusion pipelines Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and compute complexity. This specific type of diffusion model was proposed in High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. Stable Diffusion is trained on 512x512 images from a subset of the LAION-5B dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With its 860M UNet and 123M text encoder, the model is relatively lightweight and can run on consumer GPUs. For more details about how Stable Diffusion works and how it differs from the base latent diffusion model, take a look at the Stability AI announcement and our own blog post for more technical details. You can find the original codebase for Stable Diffusion v1.0 at CompVis/stable-diffusion and Stable Diffusion v2.0 at Stability-AI/stablediffusion as well as their original scripts for various tasks. Additional official checkpoints for the different Stable Diffusion versions and tasks can be found on the CompVis, Runway, and Stability AI Hub organizations. Explore these organizations to find the best checkpoint for your use-case! The table below summarizes the available Stable Diffusion pipelines, their supported tasks, and an interactive demo: Pipeline Supported tasks 🤗 Space StableDiffusion text-to-image  StableDiffusionImg2Img image-to-image  StableDiffusionInpaint inpainting  StableDiffusionDepth2Img depth-to-image  StableDiffusionImageVariation image variation  StableDiffusionPipelineSafe filtered text-to-image  StableDiffusion2 text-to-image, inpainting, depth-to-image, super-resolution  StableDiffusionXL text-to-image, image-to-image  StableDiffusionLatentUpscale super-resolution  StableDiffusionUpscale super-resolution StableDiffusionLDM3D text-to-rgb, text-to-depth, text-to-pano  StableDiffusionUpscaleLDM3D ldm3d super-resolution  Tips To help you get the most out of the Stable Diffusion pipelines, here are a few tips for improving performance and usability. These tips are applicable to all Stable Diffusion pipelines.  Explore tradeoff between speed and quality StableDiffusionPipeline uses the PNDMScheduler by default, but 🤗 Diffusers provides many other schedulers (some of which are faster or output better quality) that are compatible. For example, if you want to use the EulerDiscreteScheduler instead of the default:   Copied from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler