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 17261750 of 6689 papers

TitleStatusHype
Attentive Normalization for Conditional Image GenerationCode1
Elucidating the Exposure Bias in Diffusion ModelsCode1
METR: Image Watermarking with Large Number of Unique MessagesCode1
Meta ControlNet: Enhancing Task Adaptation via Meta LearningCode1
MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from FacesCode1
ConsistNet: Enforcing 3D Consistency for Multi-view Images DiffusionCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
ElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content SeparationCode1
DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Meta Internal LearningCode1
Adversarial Generation of Continuous ImagesCode1
EGC: Image Generation and Classification via a Diffusion Energy-Based ModelCode1
iPLAN: Interactive and Procedural Layout PlanningCode1
EHRDiff: Exploring Realistic EHR Synthesis with Diffusion ModelsCode1
AI Illustrator: Translating Raw Descriptions into Images by Prompt-based Cross-Modal GenerationCode1
iSeg: An Iterative Refinement-based Framework for Training-free SegmentationCode1
Efficient-VDVAE: Less is moreCode1
EigenGAN: Layer-Wise Eigen-Learning for GANsCode1
AccDiffusion v2: Towards More Accurate Higher-Resolution Diffusion ExtrapolationCode1
CAT-DM: Controllable Accelerated Virtual Try-on with Diffusion ModelCode1
Iterative Gaussianization: from ICA to Random RotationsCode1
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion ModelsCode1
Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood DiscrepancyCode1
AI-Generated Image Detection using a Cross-Attention Enhanced Dual-Stream NetworkCode1
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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