Efficient high-resolution image synthesis with linear diffusion transformer

  > surely cherry-picked

As someone who works in generative vision, this is one of the most frustrating aspects (especially for those with less GPU resources). There's been a silent competition for picking the best images and not showing random results (even when there are random results they may be a selected batch). So it is hard to judge actual quality until you can play around.

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...

The GeForce RTX 3080 Mobile and GeForce RTX 3080 Ti Mobile also have 16 GB versions as noted directly above the linked section on [0].

Thanks! I forgot about that (usually mobile cards have less VRAM, not more lol). I don't necessarily doubt the paper's generation claim, but there are of course many factors that could help clarify what that number actually represents

0.37s is only 11x away from realtime 30fps. I wonder if that will enable some cool new popular application for it besides batch image generation.

You can do much much better with GANs at that resolution. I'm sure you could combine the two for upsampling

Truthfully, I've had astonishing results from Stable Diffusion 1.4 on an M1 Mac, given the right inputs ...enough to throw my hands up and declare it a sort of magic (except for the presence of Getty Images watermarks randomly scattered around my results).

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.

Maybe add 'watermark' to the negative prompt?

Magic in what way? They sure are impressive tools but like all AI, they do not have an eye for finer detail. I'm really unsure most ML researchers have an eye for this oddly. But then again, most people I know that work in generative vision have no artistic hobby so I'm not sure how they can feel they can properly evaluate works. It's the subtle details that matter.

I should've been clearer, really. What made me feel the "magic" was not prompting Stable Diffusion. It was letting it iterate on art I had already done.

I did a lot of 3D-rendered illustration back in the 1990s and early 2000s, necessarily low-polygon stuff, but things that were supposed to be life-like scenes, with tons of textures, that took a very long time to render. Including what may have been the first and only children's book illustrated with Infini-D on a Mac IIsi.

So, feeding these old renderings into StableDif with 75% bias toward the original image and a couple of basic prompts, produced results that blew my mind. It was like seeing what my illustrations could have been if I'd had a team at Pixar Studios refining them. In the sense that it was still my character art and my creation, totally recognizable, but polished and refined to a level that would have been unimaginable in 1997.

I prefer to know if I've stolen someone's art...? Taking the un-watermarked parts of a stolen image is kinda sticking your head in the sand. It's recycled, stolen art. I'd never use it in production, even if only to spare my clients potential copyright liability issues down the road. But it's still fantastic as a visualization tool.