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

Showing 21262150 of 6689 papers

TitleStatusHype
Color Agnostic Cross-Spectral Disparity EstimationCode0
Trade-offs in Fine-tuned Diffusion Models Between Accuracy and InterpretabilityCode0
Personalized Image Generation for Color Vision Deficiency PopulationCode0
A Siamese-based Verification System for Open-set Architecture Attribution of Synthetic ImagesCode0
Spatially Controllable Image Synthesis with Internal Representation CollagingCode0
Affect-Conditioned Image GenerationCode0
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing FlowsCode0
PasteGAN: A Semi-Parametric Method to Generate Image from Scene GraphCode0
CollaFuse: Collaborative Diffusion ModelsCode0
PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative ModelsCode0
Paired 3D Model Generation with Conditional Generative Adversarial NetworksCode0
Editing Text in the WildCode0
Collaborative Interactive Evolution of Art in the Latent Space of Deep Generative ModelsCode0
Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I ModelsCode0
Progressive Augmentation of GANsCode0
Collaborative Sampling in Generative Adversarial NetworksCode0
P^2-GAN: Efficient Style Transfer Using Single Style ImageCode0
PAGER: Progressive Attribute-Guided Extendable Robust Image GenerationCode0
Overcoming the Disentanglement vs Reconstruction Trade-off via Jacobian SupervisionCode0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
Adversarial Out-domain Examples for Generative ModelsCode0
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network AcceleratorsCode0
Generalized Compressed Sensing for Image Reconstruction with Diffusion Probabilistic ModelsCode0
Open-Source Acceleration of Stable-Diffusion.cpp Deployable on All DevicesCode0
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified