> For the complete documentation index, see [llms.txt](https://stablediffusion.gitbook.io/overview/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://stablediffusion.gitbook.io/overview/stable-diffusion-overview/technology.md).

# Technology

Stable Diffusion is a deep learning, text-to-image model released in 2022.

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**Key Note:** Stable Diffusion is primarily used to generate detailed images conditioned on text descriptions, though it can also be applied to other tasks such as inpainting, outpainting, and generating image-to-image translations guided by a text prompt.
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Stable Diffusion is a latent diffusion model, a kind of deep generative neural network developed by the CompVis group at LMU Munich.\[4] The model has been released by a collaboration of Stability AI, CompVis LMU, and Runway with support from EleutherAI and LAION.\[5]\[1]\[6] In October 2022, Stability AI raised US$101 million in a round led by Lightspeed Venture Partners and Coatue Management.\[7]

Stable Diffusion's code and model weights have been released publicly,\[8] and it can run on most consumer hardware equipped with a modest GPU with at least 8 GB VRAM. This marked a departure from previous proprietary text-to-image models such as DALL-E and Midjourney which were accessible only via cloud services.\[9]\[10]