Image Generation
Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
( Image credit: StyleGAN )
Papers
Showing 1–10 of 6689 papers
All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024
Benchmark Results
| # | Model | Metric | Claimed | Verified | Status |
|---|---|---|---|---|---|
| 1 | Projected-GAN (DINOv2) | FD | 592.26 | — | Unverified |
| 2 | StyleGAN2 + ADA (DINOv2) | FD | 514.78 | — | Unverified |
| 3 | Efficient-VDVAE (DINOv2) | FD | 514.16 | — | Unverified |
| 4 | InsGen (DINOv2) | FD | 436.26 | — | Unverified |
| 5 | Unleashing Transformers (DINOv2) | FD | 393.45 | — | Unverified |
| 6 | StyleSwin (DINOv2) | FD | 300.18 | — | Unverified |
| 7 | StyleGAN-XL (DINOv2) | FD | 240.07 | — | Unverified |
| 8 | StyleNAT (DINOv2) | FD | 229.72 | — | Unverified |
| 9 | LDM (DINOv2) | FD | 112.4 | — | Unverified |
| 10 | Efficient-VDVAE | FID | 34.88 | — | Unverified |