I dunno... This sounds like making diffusion backwards compatible with ESRGAN. Technically they are both upscaling denoisers (with finetunes for specific tasks

I dunno... This sounds like making diffusion backwards compatible with ESRGAN. Technically they are both upscaling denoisers (with finetunes for specific tasks), and you can set up objective tests compatible with both, but actual way they are used is so different that its not even a good performance measurement. The same thing applies to recent LLMs, and the structural changes are only going to get more drastic and fundamental. For instance, what about LLMs with seperate instruction and data context? Or multimodal LLMs with multiple inputs/outputs? Or LLMs that finetune themselves during inference? That is just scratching the surface.