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Stable Diffusion
Stable Diffusion | |
---|---|
type | text-to-image |
sdlogo.png | |
Official quote | Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input, cultivates autonomous freedom to produce incredible imagery, empowers billions of people to create stunning art within seconds. |
Links | |
License | Open RAIL++-M |
Description | Standard general-purpose model. Great for generating common things (such as cat pics) and training. |
NSFW | no |
Important tags |
Stable Diffusion is an open-source text-to-image diffusion model capable of generating photo-realistic images given any text input in English. It is developed by engineers from Stability AI, CompVis, and LAION. Stable Diffusion is an open source implementation of the Latent Diffusion architecture, trained to denoise random Gaussian noise, in a lower dimensional latent space, to get a sample of interest. Then it tries to denoise the sample to get a desired result. Diffusion models have already been applied to a variety of generation tasks, such as image, speech, 3D shape, and graph synthesis.
Versions[edit | edit source]
1.5[edit | edit source]
Uses CLIP model for text interpretation.
2.0+[edit | edit source]
Unlike older versions OpenCLIP for text interpretations.
Legality[edit | edit source]
See License.
Long story short, using SD and its derivatives is legal. Unlike DALL-E, Stable Diffusion makes its source code available, along with pretrained weights. Its license prohibits certain use cases, including crime, libel, harassment, doxing, "exploiting ... minors", giving medical advice, automatically creating legal obligations, producing legal evidence, and "discriminating against or harming individuals or groups based on ... social behavior or ... personal or personality characteristics ... [or] legally protected characteristics or categories". The user owns the rights to their generated output images, and is free to use them commercially.
Societal impact[edit | edit source]
Forks[edit | edit source]
Due to its open nature, users have produced a number of derivative models based on Stable Diffusion. See List_of_text-to-image_models.