Stable Diffusion

Stable Diffusion is a text-to-image diffusion model capable of generating photo-realistic images given any text input. It was developed by Stability AI and released in August 2022. Stable Diffusion is trained on a massive dataset of images and text and can generate images of any style or subject matter.

Initial release: August 22, 2022
License: Creative ML OpenRAIL-M
Operating system: Any that support CUDA kernels
Original author(s): Runway, CompVis, and Stability AI
Repository: github.com/Stability-AI/stablediffusion
Stable release: 2.1 (model) / December 7, 2022
Written in: Python

To use Stable Diffusion, you simply need to provide a text prompt. For example, you could say “a landscape painting of a forest” or “a portrait of a young woman.” Stable Diffusion will then generate an image that matches your description.

Stable Diffusion is a powerful tool that can be used to create stunning images. It is still under development, but it has the potential to revolutionize the way we create art.

Here are some of the benefits of using Stable Diffusion:

• It can generate photo-realistic images of any style or subject matter.
• It is easy to use. Simply provide a text prompt and Stable Diffusion will generate an image that matches your description.
• It is still under development, which means that it is constantly improving.

Here are some of the limitations of using Stable Diffusion:

• It can be slow to generate images, especially for complex prompts.
• It can sometimes generate images that are not exactly what you were expecting.
• It is not yet available for commercial use.

Overall, Stable Diffusion is a powerful tool that has the potential to revolutionize the way we create art. It is still under development, but it is already capable of generating stunning images.

Training data

The Stable Diffusion model was trained on a vast collection of image-caption pairs sourced from LAION-5B, a publicly available dataset extracted from the web using Common Crawl data. The dataset consisted of an impressive 5 billion image-text pairs, which were meticulously classified based on language and then filtered into separate datasets based on resolution, the likelihood of containing watermarks, and subjective aesthetic scores indicating visual quality.

The LAION-5B dataset was developed by LAION, a German non-profit organization that receives funding from Stability AI. LAION carefully curated and prepared the dataset to ensure its suitability for training the Stable Diffusion model. For this purpose, three specific subsets were used: laion2B-en, laion-high-resolution, and laion-aesthetics v2 5+.

An analysis conducted by a third-party examined the training data employed for the Stable Diffusion model. From a smaller subset of 12 million images selected from the wider original dataset, it was found that approximately 47% of the sampled images originated from 100 different domains. Notably, Pinterest accounted for 8.5% of this subset, while other websites such as WordPress, Blogspot, Flickr, DeviantArt, and Wikimedia Commons also contributed to the dataset.

Please note that the Stable Diffusion model focuses on generating high-quality text content and does not directly involve image processing or analysis.