The advent of text-to-image models that can produce sophisticated digital images in a matter of seconds has raised alarm bells within the artistic community. Human artists working with more traditional media have warned about the effects of generative models on the market for their works, and the future of human creativity. While artists whose works were involuntarily used to train generative models have legitimate cause for complaint (and reasonable demands for compensation and attribution), the emergence of generative models is not as apocalyptic as framed. By providing amateur creators with powerful tools for creative expression, generative models help to democratize cultural participation, and diversify public discourse. Accordingly, those who view human creativity as a relational and dialogic practice should embrace generative AI’s capacity to extend this practice to previously excluded communities. The puzzle, then, for copyright scholars, is how to balance the expressive interests of users of generative models with the expressive and economic interests of owners of copyrighted training data, where those interests diverge. Must the expressive interests of users be subordinated to the economic interests of existing artists? How will the social value of different forms of creativity inform the distribution of legal burdens, and the construction of preferred liability regimes? This presentation focuses on preserving user access to generative AI as a form of creative expression that contributes to a participatory and democratic culture. It surveys a range of options for achieving this goal, including a statutory non-commercial use provision, a compulsory licensing regime, an intermediary safe harbor, and user authorship of model generations.