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 551575 of 6689 papers

TitleStatusHype
GenAI Arena: An Open Evaluation Platform for Generative ModelsCode2
Generative Modeling by Estimating Gradients of the Data DistributionCode2
GALIP: Generative Adversarial CLIPs for Text-to-Image SynthesisCode2
From Text to Pose to Image: Improving Diffusion Model Control and QualityCode2
GAN Compression: Efficient Architectures for Interactive Conditional GANsCode2
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in VideosCode2
Attention Calibration for Disentangled Text-to-Image PersonalizationCode2
FouriScale: A Frequency Perspective on Training-Free High-Resolution Image SynthesisCode2
FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept CompositionCode2
GAN Prior Embedded Network for Blind Face Restoration in the WildCode2
Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image SynthesisCode2
FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity RefinerCode2
From Parts to Whole: A Unified Reference Framework for Controllable Human Image GenerationCode2
Attention Mechanisms in Computer Vision: A SurveyCode2
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous TokensCode2
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory MatchingCode2
GANSpace: Discovering Interpretable GAN ControlsCode2
BiGR: Harnessing Binary Latent Codes for Image Generation and Improved Visual Representation CapabilitiesCode2
Gaussian Mixture Flow Matching ModelsCode2
Flow Matching in Latent SpaceCode2
Generating Images with Multimodal Language ModelsCode2
Generative AI for Character Animation: A Comprehensive Survey of Techniques, Applications, and Future DirectionsCode2
Generative Diffusion Models on Graphs: Methods and ApplicationsCode2
Flux Already Knows -- Activating Subject-Driven Image Generation without TrainingCode2
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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