Stable Diffusion

High-Resolution Image Synthesis with Latent Diffusion Models

Stable Diffusion v2 refers to a specific configuration of the model architecture that uses a downsampling-factor 8 autoencoder with an 865M UNet and OpenCLIP ViT-H/14 text encoder for the diffusion model. The SD 2-v model produces 768x768 px outputs.

This stable-diffusion-2 model is resumed from stable-diffusion-2-base (512-base-ema.ckpt) and trained for 150k steps using a v-objective on the same dataset. Resumed for another 140k steps on 768x768 images.

• Use it with the stablediffusion repository: download the 768-v-ema.ckpt here.
• Use it with 🧨 diffusers

Model Details

• Developed by: Robin Rombach, Patrick Esser
• Model type: Diffusion-based text-to-image generation model
• Language(s): English
• License: CreativeML Open RAIL++-M License
• Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (OpenCLIP-ViT/H).
• Resources for more information: GitHub Repository.

Examples

Using the 🤗’s Diffusers library to run Stable Diffusion 2 in a simple and efficient manner.