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 64266450 of 6689 papers

TitleStatusHype
Lemotif: An Affective Visual Journal Using Deep Neural NetworksCode0
Offline Evaluation of Set-Based Text-to-Image GenerationCode0
Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-ResolutionCode0
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented InterventionCode0
DCL: Differential Contrastive Learning for Geometry-Aware Depth SynthesisCode0
Anomaly Detection with Adversarial Dual AutoencodersCode0
Controllable Text-to-Image GenerationCode0
On Investigating the Conservative Property of Score-Based Generative ModelsCode0
Adversarially Learned InferenceCode0
Annotated Hands for Generative ModelsCode0
Leveraging GAN Priors for Few-Shot Part SegmentationCode0
Graph Self-Supervised Learning for Endoscopic Image MatchingCode0
On the Wasserstein Convergence and Straightness of Rectified FlowCode0
Omni-Directional Image Generation from Single Snapshot ImageCode0
GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image GenerationCode0
Align Beyond Prompts: Evaluating World Knowledge Alignment in Text-to-Image GenerationCode0
GR-GAN: Gradual Refinement Text-to-image GenerationCode0
ANNA: Abstractive Text-to-Image Synthesis with Filtered News CaptionsCode0
Which Contrast Does Matter? Towards a Deep Understanding of MR Contrast using Collaborative GANCode0
Grid Partitioned Attention: Efficient TransformerApproximation with Inductive Bias for High Resolution Detail GenerationCode0
Class-Splitting Generative Adversarial NetworksCode0
Robust deep learning for eye fundus images: Bridging real and synthetic data for enhancing generalizationCode0
Controllable Textual Inversion for Personalized Text-to-Image GenerationCode0
Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal ModelsCode0
SEEDS: Exponential SDE Solvers for Fast High-Quality Sampling from Diffusion ModelsCode0
Show:102550
← PrevPage 258 of 268Next →

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