AI Art “In the Style of” & Contributory Liability

By: Jacob Alhadeff

Greg Rutkowski illustrates fantastical images for games such as Dungeons & Dragons and Magic the Gathering. Rutkowski’s name has been used thousands of times in generative art platforms, such as Stable Diffusion and Dall-E, flooding the internet with thousands of works in his style. For example, type in “Wizard with sword and a glowing orb of magic fire fights a fierce dragon Greg Rutkowski,” and Stable Diffusion will output something similar to Rutkowski’s actual work. Rutkowski is now reasonably concerned that his work will be drowned out by these hundreds of thousands of emulations, ultimately preventing customers from being able to find his work online. 

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Examples of images generated by Dream Studio (Stable Diffusion) in Rutkowski’s style.

These machine learning algorithms are trained using freely available information, which is largely a good thing. However, it may feel unfair that an artist’s copyrighted images are freely copied to train their potential replacement. Ultimately, nothing these algorithms or their owners are doing is copyright infringement, and there are many good reasons for this. However, in certain exceptional circumstances, like Rutkowski’s, it may seem like copyright laws insufficiently protect human creation and unreasonably prioritizes computer generation.
A primary reason why Rutkowski has no legal recourse is because an entity that trains its AI on Rutkowski’s copyrighted work is not the person generating the emulating art. Instead, thousands of end-users are collectively causing Rutkowski harm. Since distinct entities cause aggregate harm, there is no infringement. By contrast, if Stable Diffusion verbatim copied Rutkowski’s work to train their AI before generating hundreds of thousands of look-a-likes, this would likely be an unfair infringement. Understanding the importance of this separation is best seen through understanding the process of text-to-art generation and analyzing each person’s role in the process. 

Text-to-Image Copyright AnalysisDiagram, text

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To give a brief summary of this process, billions of original human artists throughout history have created art that has been posted online. Then a group like Common Crawl scrapes those billions of images and their textual pairs from billions of web pages for public use. Later, a non-profit such as LAION creates a massive dataset that includes internet indexes and similarity scores between text and images. Subsequently, a company such as Stable Diffusion trains its text-to-art AI generator on these text-image pairs. Notably, when a text-to-art generator uses the LAION database, they are not necessarily downloading the images themselves to train their AI. Finally, when the end user goes to Dream Studio and types in the phrase “a mouse in the style of Walt Disney,” the AI generates unique images of Mickey Mouse. 

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Examples of images generated by Dream Studio (Stable Diffusion) using the phrase “a mouse in the style of Walt Disney”

These several distributed roles complicate our copyright analysis, but for now, we will limit our discussion of copyright liability to three primary entities: (1) the original artist, (2) the Text-to-Image AI Company, and (3) the end-user. 

The Text-to-Image Company likely has copied Rutkowski’s work. If the Text-to-Image company actually downloads the images from the dataset to train its AI, then there is verbatim intermediate copying of potentially billions of copyrightable images. However, this is likely fair use because the generative AI provides what the court would consider a public benefit and has transformed the purpose and character of the original art. This reasoning is demonstrated by Kelly v. Arriba, where an image search’s use of thumbnail images was determined to be transformative and fair partly because of the public benefit provided by the ability to search images and the transformed purpose for that art, searching versus viewing. Here, the purpose of the original art was to be viewed by humans, and the Text-to-Image AI Company has transformatively used the art to be “read” by machines to train an AI. The public benefit of text-to-art AI is the ability to create complex and novel art by simply typing a few words into a prompt. It is more likely that the Generative AI’s use is fair because the public does not see these downloaded images, which means that they have not directly impacted the market for the copyrighted originals. 

The individual end-user is any person that prompts the AI to generate hundreds of thousands of works “in the style of Greg Rutkowski.” However, the end-user has not copied Rutkowski’s art because copyright’s idea-expression distinction means that Rutkowski’s style is not copyrightable. The end-user simply typed 10 words into Stable Diffusion’s UI. While the images of wizards fighting dragons may seem similar to Rutkowski’s work, they may not be substantially similar enough to be deemed infringing copies. Therefore, the end-user similarly didn’t unfairly infringe on Rutkowski’s copyright.

Secondary Liability & AI Copyright

Generative AI portends dramatic social and economic change for many, and copyright will necessarily respond to these changes. Copyright could change to protect Rutkowski in different ways, but many of these potential changes would result in either a complete overhaul of copyright law or the functional elimination of generative art, neither of which is desirable. One minor alteration that could give Rutkowski, and other artists like him, slightly more protection is a creative expansion of contributory liability in copyright. One infringes contributorily by intentionally inducing or encouraging direct infringement.

Dall-E has actively encouraged end-users to generate art “in the style of” artists. So not only are these text-to-art AI companies verbatim copying artists’ works, but they are then also encouraging users to emulate the artists’ work. At present, this is not considered contributory liability and is frequently innocuous. Style is not copyrightable because ideas are not copyrightable, which is a good thing for artistic freedom and creation. So, while the work of these artists is not being directly copied by end-users when Dall-E encourages users to flood the internet with AI art in Rutkowski’s style, it feels like copyright law should offer Rutkowski slightly more protection.

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An astronaut riding a horse in the style of Andy Warhol.
A painting of a fox in the style of Claude Monet.

Contributory liability could offer this modicum of protection if, and only if, it expanded to include circumstances where the copying fairly occurred by the contributor, but not the thousands of end-users. As previously stated, the end-users are not directly infringing Rutkowski’s copyright, so under current law, Dall-E has not contributorily copied. However, there has never been a contributory copyright case such as this one, where the contributing entity themselves verbatim copied the copyrighted work, albeit fairly, but the end user did not. As such, copyright’s flexibility and policy-oriented nature could permit a unique carveout for such protection.

Analyzing the potential contributory liability of Dall-E is more complicated than it sounds, particularly because of the quintessential modern contributory liability case, MGM v. Grokster, which involved intentionally instructing users on how to file-share millions of songs. Moreover, Sony v. Universal would rightfully protect Dall-E generally as due to many similarities between the two situations. In that case, the court found Sony not liable for copyright infringement for the sale of VHS recorders which facilitated direct copying of TV programming because the technology had “commercially significant non-infringing uses.” Finally, regardless of Rutkowski’s theoretical likelihood of success, if contributory liability were expanded in this way, then it would at least stop companies such as Dall-E from advertising the fact that their generations are a great way to emulate, or copy, an artist’s work that they themselves initially copied. 

This article has been premised on the idea that the end-users aren’t copying, but what if they are? It is clear that Rutkowski’s work was not directly infringed by the wizard fighting the dragon, but what about “a mouse in the style of Walt Disney?” How about “a yellow cartoon bear with a red shirt” or “a yellow bear in the style of A. A. Milne?” How similar does an end-user’s generation need to be for Disney to sue over an end-user’s direct infringement? What if there were hundreds of thousands of unique AI-generated Mickey Mouse emulations flooding the internet, and Twitter trolls were harassing Disney instead of Rutkowski? Of course, each individual generation would require an individual infringement analysis. Maybe the “yellow cartoon bear with a red shirt” is not substantially similar to Winnie the Pooh, but the “mouse in the style of Walt Disney” could be. These determinations would impact a generative AI’s potential contributory liability in such a claim. Whatever copyright judges and lawmakers decide, the law will need to find creative solutions that carefully balance the interests of artists and technological innovation. 

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