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 10261050 of 6689 papers

TitleStatusHype
Diverse Image Synthesis from Semantic Layouts via Conditional IMLECode1
High Fidelity Image Synthesis With Deep VAEs In Latent SpaceCode1
Harnessing Frequency Spectrum Insights for Image Copyright Protection Against Diffusion ModelsCode1
Harnessing the Spatial-Temporal Attention of Diffusion Models for High-Fidelity Text-to-Image SynthesisCode1
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
Distribution-Aware Data Expansion with Diffusion ModelsCode1
GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial NetworksCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-EncoderCode1
Handwriting TransformersCode1
HealthiVert-GAN: A Novel Framework of Pseudo-Healthy Vertebral Image Synthesis for Interpretable Compression Fracture GradingCode1
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based CustomizationCode1
Dissecting and Mitigating Diffusion Bias via Mechanistic InterpretabilityCode1
Dissolving Is Amplifying: Towards Fine-Grained Anomaly DetectionCode1
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion ModelsCode1
Guided Image Generation with Conditional Invertible Neural NetworksCode1
Towards Disentangling Latent Space for Unsupervised Semantic Face EditingCode1
DMM: Building a Versatile Image Generation Model via Distillation-Based Model MergingCode1
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image SynthesisCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
High-Fidelity Neural Human Motion Transfer from Monocular VideoCode1
GRAF: Generative Radiance Fields for 3D-Aware Image SynthesisCode1
DisenBooth: Identity-Preserving Disentangled Tuning for Subject-Driven Text-to-Image GenerationCode1
Graph Image Prior for Unsupervised Dynamic Cardiac Cine MRI ReconstructionCode1
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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