SOTAVerified

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

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Showing 4501–4525 of 6689 papers

TitleStatusHype
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data—0
Score Neural Operator: A Generative Model for Learning and Generalizing Across Multiple Probability Distributions—0
SCott: Accelerating Diffusion Models with Stochastic Consistency Distillation—0
SCP-Diff: Spatial-Categorical Joint Prior for Diffusion Based Semantic Image Synthesis—0
SD-DiT: Unleashing the Power of Self-supervised Discrimination in Diffusion Transformer—0
SDDM: Score-Decomposed Diffusion Models on Manifolds for Unpaired Image-to-Image Translation—0
Adversarial Pixel-Level Generation of Semantic Images—0
SDiT: Spiking Diffusion Model with Transformer—0
Adversarial nets with perceptual losses for text-to-image synthesis—0
SDXL-Lightning: Progressive Adversarial Diffusion Distillation—0
SEAL: Entangled White-box Watermarks on Low-Rank Adaptation—0
SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps—0
Seamless Optical Cloud Computing across Edge-Metro Network for Generative AI—0
VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors—0
Adversarially robust segmentation models learn perceptually-aligned gradients—0
Secure Multiparty Generative AI—0
Secure Traffic Sign Recognition: An Attention-Enabled Universal Image Inpainting Mechanism against Light Patch Attacks—0
SEEAvatar: Photorealistic Text-to-3D Avatar Generation with Constrained Geometry and Appearance—0
Adversarially Perturbed Wavelet-based Morphed Face Generation—0
Seeding Diversity into AI Art—0
SEE-DPO: Self Entropy Enhanced Direct Preference Optimization—0
Adversarially Approximated Autoencoder for Image Generation and Manipulation—0
Seedream 3.0 Technical Report—0
Adversarial Learning of Semantic Relevance in Text to Image Synthesis—0
Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis—0
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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

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified