  Blind and low vision (BLV) creators use images to communicate with sighted
audiences. However, creating or retrieving images is challenging for BLV
creators as it is difficult to use authoring tools or assess image search
results. Thus, creators limit the types of images they create or recruit
sighted collaborators. While text-to-image generation models let creators
generate high-fidelity images based on a text description (i.e. prompt), it is
difficult to assess the content and quality of generated images. We present
GenAssist, a system to make text-to-image generation accessible. Using our
interface, creators can verify whether generated image candidates followed the
prompt, access additional details in the image not specified in the prompt, and
skim a summary of similarities and differences between image candidates. To
power the interface, GenAssist uses a large language model to generate visual
questions, vision-language models to extract answers, and a large language
model to summarize the results. Our study with 12 BLV creators demonstrated
that GenAssist enables and simplifies the process of image selection and
generation, making visual authoring more accessible to all.
