The expansion of generative artificial intelligence into the domain of music production has introduced new possibilities and risks for cultural representation, particularly in its depiction of marginalized groups. In this paper we study how LGBTQIA identities are represented in text-prompt-based end-to-end generated songs by the black-box models of Suno and Mureka. To do so we use a template-based approach to generate prompts containing identity terms like “gay”, “lesbian” or “genderqueer” that we pass to the examined systems, generating a dataset of 9600 songs. Using qualitative and quantitative content analysis of the generated song lyrics and musicological analysis of the generated music, we find that while songs generally convey high regard and positive sentiment towards queer individuals, they often reproduce stereotypes. We uncover representational harms specifically in the representation of aromantic, asexual, lesbian and intersex people. Musically, the models produce songs that strongly align with the requested genre, but show only minor differences based on the identity terms used in the prompts. We release the created Sounds Queer dataset and all associated code under open Creative Commons licensing for further research.