Stable Diffusion image generator

Stable Diffusion is a free, open source AI-based system that generates images from text descriptions or prompts.  

Trained on pairs of images and captions taken from LAION-5B, Stable Diffusion was initially restricted to researchers as Stable Diffusion 1. Stable Diffusion 2 was publicly released in August 2022.

Reaction

Stable Diffusion quickly became popular largely on account of its free availability, minimal usage restrictions, and the fact that it can run on ordinary consumer hardware.

However, it is seen to suffer from a number of technical limitations, and to pose several actual and potential challenges and risks to organisations and individuals operating and using it:

• Accuracy/reliability: The quality of images produced by the system degrade above 512×512-pixel resolution; it also has trouble generating images with human limbs and faces.
  
  

• Bias/discrimination: Stable Diffusion was primarily trained on images with English descriptions, resulting in images that tend to reinforce western stereotypes and biases. A Bloomberg test discovered that the tool produces content that is full of gender and racial stereotypes when it renders people in 'high-paying' and 'low-paying jobs.'
  
  

• Copyright: Stable Diffusion draws on copyrighted works of artists, without the owner’s consent or any form of attribution, prompting concerns about plagiarism and the misuse, abuse and loss of IP. Furthermore, users are given full rights to images they create on Stable Diffusion, and is free to use them commercially, resulting in questions about fair use. Responding to pressure, Stability AI gave artists the ability to opt out of having their artworks included in data training and stopped users being able to emulate the style of specific artists, including Greg Rutkowski, leading users to complain the model had been 'nerfed'.
  
  In January 2023, a group of lawyers filed a US class-action lawsuit against Sta­bil­ity AI, DeviantArt, and Mid­jour­ney on the basis that their use of Sta­ble Dif­fu­sion illegally remixes the copy­righted works of mil­lions of artists whose work was used as train­ing data. Stability AI was also accused in UK and US filed by Getty Images of infringing the copyright protections of over 12 million photographs from its stock image collection when training Stable Diffusion.

Furthermore, Google researchers have shown that Stable Diffusion is able to memorise and recreate specific copyrighted images.

• Dual/multi-use: The relative accessibility of the model, including the release of its model weights, together with its relative paucity of filters, means Stable Diffusion can be used for more or less any purpose, including nefarious ones such as spreading spam, propaganda, deepfakes, misinformation and disinformation.
  
  

• Employment: Artists' loss of copyright and the ability of users to create images in their unique style has prompted increasing  concern that artists, photographers, illustrators, cinematographers and others stand to lose their commercial viability and careers.
  
  A French game developer's release of a Stable Diffusion-based tool to generate images in the style of late Korean artist Kim Jung Gi with a simple text prompt sparked a furious response from artists who saw the tool as theft, and accused the developer of cultural appropriation.
  
  

• Safety: As TechCrunch notes, Stability AI bans lewd or sexual material, hateful or violent imagery, prompts containing copyrighted or trademarked material, and personal information such as phone numbers and Social Security numbers. But this has not stopped users generating abusive, violent, sexual and pornographic images.

Transparency

Stability AI initially made Stable Diffusion accessible courtesy of a public demo, a software beta called DreamStudio, a development notebook, and weights, model card, and code.

Research, advocacy

• Carlini N., Hayes J., Nasr M., Jagielski M., Sehwag V., Tramèr F., Balle B., Ippolito D., Wallace E. (2023). Extracting Training Data from Diffusion Models

• Luccioni A.S., Akiki C., Mitchell M., Jernite Y. (2023). Stable Bias: Analyzing Societal Representations in Diffusion Models

• Somepalli G., Singla V., Goldblum M., Geiping J., Goldstein T. (2022). Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models