3 ms·
>We present our 1B-parameter GigaGAN, achieving lower FID than Stable Diffusion v1.5, DALL·E 2, and Parti-750M. It generates 512px outputs at 0.13s, orders of m
by Vt71fcAqt7 4y ago
>We present our 1B-parameter GigaGAN, achieving lower FID than Stable Diffusion v1.5, DALL·E 2, and Parti-750M. It generates 512px outputs at 0.13s, orders of magnitude faster than diffusion and autoregressive models, and inherits the disentangled, continuous, and controllable latent space of GANs.
Nice to see GANs making a resurgence, as they are much faster and more controllable. Hands seem to still be messed up though. Are hands just a dataset issue or is there something really hard about it? My first thought is that faces would seem to be more difficult and that has already been mostly solved.
- GaggiX 4y agoHands compare to faces have a lot of limbs, they can take many different poses that makes it difficult for the model to learn them, also these models are usually not good with "counting", an hand has 5 fingers but you can easily expect that a model will create an hand with 4, 6, 7 fingers.
- ilkke 4y agoI heard it once explained as "most fingers have a finger either side" issue. That said, there certainly are SD models that are quite solid in this regard, like deliberate. I don't know much about GANs but if the problem is shared w diffusion, perhaps the solution is as well and we just need better training.