Text to image models feels inefficient to me. I wonder if it would be possible and better to do it in separate steps, like text to scene graph, scene graph to semantically segmented image, segmented image to final image. That way each step could be trained separately and be modular, and the image would be easier to edit instead of completely replace it with the output of a new prompt. That way it should be much easier to generate stuff like "object x next to object y, with the text foo on it", and the art style or level of realism would depend on the final rendering model which would be separate from the prompt adherence.
Kind of like those video2video (or img2img on each frame I guess) models where they enhance the image outputs from video games:
Kind of like those video2video (or img2img on each frame I guess) models where they enhance the image outputs from video games:
https://www.theverge.com/2021/5/12/22432945/intel-gta-v-real... https://www.reddit.com/r/aivideo/comments/1fx6zdr/gta_iv_wit...