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 13011325 of 6689 papers

TitleStatusHype
FlexDiT: Dynamic Token Density Control for Diffusion TransformerCode1
Generate Your Own Scotland: Satellite Image Generation Conditioned on MapsCode1
Flow Contrastive Estimation of Energy-Based ModelsCode1
Generating Person Images with Appearance-aware Pose StylizerCode1
First Creating Backgrounds Then Rendering Texts: A New Paradigm for Visual Text BlendingCode1
Generative Adversarial Networks in Computer Vision: A Survey and TaxonomyCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
Counterfactual Generative NetworksCode1
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilitiesCode1
A Complete Recipe for Diffusion Generative ModelsCode1
Counterfactual contrastive learning: robust representations via causal image synthesisCode1
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual ExamplesCode1
BAGM: A Backdoor Attack for Manipulating Text-to-Image Generative ModelsCode1
Automatic Jailbreaking of the Text-to-Image Generative AI SystemsCode1
Finetuning CLIP to Reason about Pairwise DifferencesCode1
Generative Modelling With Inverse Heat DissipationCode1
Few-shot Semantic Image Synthesis Using StyleGAN PriorCode1
Generative Occupancy Fields for 3D Surface-Aware Image SynthesisCode1
Few-shot Image Generation via Cross-domain CorrespondenceCode1
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of ExpertsCode1
Batch-efficient EigenDecomposition for Small and Medium MatricesCode1
CVAE-GAN: Fine-Grained Image Generation through Asymmetric TrainingCode1
Few-Shot Human Motion Transfer by Personalized Geometry and Texture ModelingCode1
GeNIe: Generative Hard Negative Images Through DiffusionCode1
Few-shot Image Generation via Adaptation-Aware Kernel ModulationCode1
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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