Something I've wondered, maybe I should just do it if I can find some time, but... given DeepSeek's nice results on using rendered text as input, I'm wondering if anyone has given serious research efforts towards image-based diffusion methods for text.
As in, instead of all the complexities induced by discrete token generation, just generate the image of the text using standard image diffusion methods, then convert it to text.
If you used a single, monospace font, I bet this would be even pretty efficient, because the OCR problem becomes basically just direct template matching.
But I guess probably there is already a paper out there, I haven't searched. I'd be curious to know if it compares on par with token-based methods.
As in, instead of all the complexities induced by discrete token generation, just generate the image of the text using standard image diffusion methods, then convert it to text.
If you used a single, monospace font, I bet this would be even pretty efficient, because the OCR problem becomes basically just direct template matching.
But I guess probably there is already a paper out there, I haven't searched. I'd be curious to know if it compares on par with token-based methods.