StyleGAN-T is a new GAN for tex2image generation.
This GAN produces good results and is even quite quick (0.1 sec for a 512×512 image).
The new architecture is based on StyleGAN-XL, but it reevaluates the generator and discriminator designs.
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You’ve surely noticed that GANs are no longer discussed when the topic of image generation comes up. After diffusion models like Stable Diffusion emerged, GANs somehow retreated into the background. This is because they are challenging to train and frequently trip over. The only benefit of GANs was that, unlike diffusion models, they produce an image in a single run (a “forward pass”) instead of many runs.
But now a new player from the GANs has entered the field: StyleGAN-T. This GAN for tex-to-image generation produces good results fast, as it only takes 0.1 sec for a 512×512 image. The new architecture is based on StyleGAN-XL, but it reevaluates the generator and discriminator designs and employs CLIP for text prompt alignment and generated graphics.
In general, StyleGAN-T now creates text-to-image faster and more accurately than other GANs. However, GAN is still awful and the quality of the full-size SD model is obviously out of the question. But that all will depend on ability to produce extremely high-quality images from text in less than a second in a year. Additionally, it will fall somewhere between GAN and the diffusion model.
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