~25x faster performance than Flux-dev, while offering comparable quality in benchmarks. And visually the examples (surely cherry-picked, but still) look great!
Especially since with GenAI the best way to get good results is to just generate a large amount of them and pick the best (imo). Performance like this will make that much easier/faster/cheaper.
Code is unfortunately "(Coming soon)" for now. Can't wait to play with it!
> surely cherry-picked
Also, I'm not sure what laptop that is but they say 0.37s to generate a 1024x1024 image on a 4090. They also mention that it requires 16GB VRAM. But that laptop looks like a MSI Titan, which has a 4090, and correct me if I'm wrong, but I think the 4090 is the only mobile card with 16GB?[0] (I know desktop graphics have 16 for most cards). The laptop demo takes 4s to generate a 1024x1024 image. But they are chopped down quite a bit[1]
I wonder if that's with or without TensorRT
[0] https://en.wikipedia.org/wiki/List_of_Nvidia_graphics_proces...
[1] https://gpu.userbenchmark.com/Compare/Nvidia-RTX-4090-Laptop...
Nonetheless, as an art director, nothing I'd put into production. I guess that's because what I'm focused on is tickling the client base with something original.
Looking at their methodology, it seems like it's more of an accumulation of existing good ideas into the one model.
If it performs as well as they say, perhaps you can say the breakthrough is discovering just how much can be gained by combining recent advances.
It's sitting on just the edge of sounding too good to be true to me. I will certainly be pleased if it holds up to scrutiny.
I'd be curious to see how a vision model would go if it were finetuned to select the best image match to a given criteria.
It's possible that you could do O1 style training to build a final stage auto-cherrypicker.
You have to release your model in some fashion for it to be impressive.
Basically they compress/decompress the images more, which means they need less computation during generation. But on the flip side this should mean less variability.
Isn't this more of a design trade-off than an optimization?
It would decrease the workload by having fewer things to compare against balanced against workload per comparison. For normal N² that makes sense but the page says.
We introduce a new linear DiT, replacing vanilla quadratic attention and reducing complexity from O(N²) to O(N) Mix-FFN
So not sure what's up there.
Looking forward to it. This space just keeps getting more interesting.
3D models (sculpts, texture, retopo, etc.) are following a similar trend and trajectory.
Open video models are lagging behind by several years. While CogVideo and Pyramid are promising, video models are petabyte scale and so much more costly to build and train.
I'm hoping video becomes free and cheap, but it's looking like we might be waiting a while.
Major kudos to all of the teams building and training open source models!
That would be useful for e.g. book illustration, comic strips, icon sets. Otherwise, people would think you pick those images all over the internet and not from one source/theme.