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 776800 of 6689 papers

TitleStatusHype
Diffusion Models for Constrained DomainsCode1
Diffusion Self-Guidance for Controllable Image GenerationCode1
Diffusion Models for Interferometric Satellite Aperture RadarCode1
Diffusion Normalizing FlowCode1
Discrete Contrastive Diffusion for Cross-Modal Music and Image GenerationCode1
From Face to Natural Image: Learning Real Degradation for Blind Image Super-ResolutionCode1
Fully automatic computer-aided mass detection and segmentation via pseudo-color mammograms and Mask R-CNNCode1
Adversarial score matching and improved sampling for image generationCode1
Accelerating Guided Diffusion Sampling with Splitting Numerical MethodsCode1
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
Frame Interpolation with Consecutive Brownian Bridge DiffusionCode1
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned GenerationCode1
FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded SamplingCode1
Accelerating Diffusion Transformer via Increment-Calibrated Caching with Channel-Aware Singular Value DecompositionCode1
Diffusion Models Are Innate One-Step GeneratorsCode1
FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image GenerationCode1
Diffusion Features to Bridge Domain Gap for Semantic SegmentationCode1
Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image GenerationCode1
ARINAR: Bi-Level Autoregressive Feature-by-Feature Generative ModelsCode1
Accelerating Diffusion Sampling with Optimized Time StepsCode1
Diffusion Models Beat GANs on Image ClassificationCode1
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
Benchmarking Robustness of Multimodal Image-Text Models under Distribution ShiftCode1
DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery AnalysisCode1
Freestyle Layout-to-Image SynthesisCode1
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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