Search results
In order to use Stable Diffusion WebUI, you will need to download Stable Diffusion models. One option is to download a pre-trained model from Hugging Face. For example, you can download the "openjourney" model by visiting https://huggingface.co/prompthero/openjourney.
- Overview
- News
- Requirements
- General Disclaimer
- Stable Diffusion v2
- Shout-Outs
- License
This repository contains Stable Diffusion models trained from scratch and will be continuously updated with new checkpoints. The following list provides an overview of all currently available models. More coming soon.
March 24, 2023
Stable UnCLIP 2.1
•New stable diffusion finetune (Stable unCLIP 2.1, Hugging Face) at 768x768 resolution, based on SD2.1-768. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO. Comes in two variants: Stable unCLIP-L and Stable unCLIP-H, which are conditioned on CLIP ViT-L and ViT-H image embeddings, respectively. Instructions are available here.
•A public demo of SD-unCLIP is already available at clipdrop.co/stable-diffusion-reimagine
December 7, 2022
Version 2.1
You can update an existing latent diffusion environment by running
xformers efficient attention
For more efficiency and speed on GPUs, we highly recommended installing the xformers library.
Tested on A100 with CUDA 11.4. Installation needs a somewhat recent version of nvcc and gcc/g++, obtain those, e.g., via
Then, run the following (compiling takes up to 30 min).
Upon successful installation, the code will automatically default to memory efficient attention for the self- and cross-attention layers in the U-Net and autoencoder.
Stable Diffusion models are general text-to-image diffusion models and therefore mirror biases and (mis-)conceptions that are present in their training data. Although efforts were made to reduce the inclusion of explicit pornographic material, we do not recommend using the provided weights for services or products without additional safety mechanis...
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.
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 DDIM sampling steps show the relative improvements of the checkpoints:
•Thanks to Hugging Face and in particular Apolinário for support with our model releases!
•Stable Diffusion would not be possible without LAION and their efforts to create open, large-scale datasets.
•The DeepFloyd team at Stability AI, for creating the subset of LAION-5B dataset used to train the model.
•Stable Diffusion 2.0 uses OpenCLIP, trained by Romain Beaumont.
•Our codebase for the diffusion models builds heavily on OpenAI's ADM codebase and https://github.com/lucidrains/denoising-diffusion-pytorch. Thanks for open-sourcing!
•CompVis initial stable diffusion release
The code in this repository is released under the MIT License.
The weights are available via the StabilityAI organization at Hugging Face, and released under the CreativeML Open RAIL++-M License License.
May 16, 2024 · Learn how to find, download, and install various models or checkpoints in stable diffusion to generate stunning images. Understand model details and add custom variable autoencoders (VAEs) for improved results.
Dec 19, 2022 · 0:00 Introduction to the video. 0:38 Official page of Stability AI who released Stable Diffusion models. 1:14 How to download official Stable Diffusion version 2.1 with 768x768 pixels. 1:44 How to copy paste the downloaded version 2.1 model into the correct web UI folder.
Dec 21, 2022 · Download Stable diffusion 2.0 files. After installation, you will need to download two files to use Stable Diffusion 2.0. Download the model file (768-v-ema.ckpt) Download the config file, rename it to 768-v-ema.yaml; Put both of them in the model directory: stable-diffusion-webui/models/Stable-diffusion Google Colab
Mar 4, 2024 · Finally, we welcome the Automatic1111 Stable Diffusion WebUI v1.80. This update brings along several useful features. Let’s take a look at the key updates right away!
People also ask
How do I install a stable diffusion model?
What is orange's new Livebox 5?
What is stable diffusion V2?
Where can I find stable diffusion V2 weights?
Stable Diffusion Plugin. To use Stable Diffusion with this project, you will either need to download and install AUTOMATIC1111 - Stable Diffusion WebUI or, for a dockerized deployment, you can also use stable-diffusion-webui-docker.
