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 751775 of 6689 papers

TitleStatusHype
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability VolumesCode1
Diffusion in Diffusion: Cyclic One-Way Diffusion for Text-Vision-Conditioned GenerationCode1
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
A Scene-Text Synthesis Engine Achieved Through Learning from Decomposed Real-World DataCode1
AdvPaint: Protecting Images from Inpainting Manipulation via Adversarial Attention DisruptionCode1
Diffusion Models Are Innate One-Step GeneratorsCode1
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion ModelsCode1
FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic DataCode1
DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery AnalysisCode1
Accelerating Parallel Sampling of Diffusion ModelsCode1
Focal Frequency Loss for Image Reconstruction and SynthesisCode1
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion modelsCode1
ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face SynthesisCode1
Artistic Glyph Image Synthesis via One-Stage Few-Shot LearningCode1
Diffusion Features to Bridge Domain Gap for Semantic SegmentationCode1
FooDI-ML: a large multi-language dataset of food, drinks and groceries images and descriptionsCode1
Diffusion Cross-domain RecommendationCode1
Diffusion Cocktail: Mixing Domain-Specific Diffusion Models for Diversified Image GenerationsCode1
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image ManipulationCode1
Accelerating Markov Chain Monte Carlo sampling with diffusion modelsCode1
Diffusion Curriculum: Synthetic-to-Real Generative Curriculum Learning via Image-Guided DiffusionCode1
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image DetectionCode1
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-TrainingCode1
Diffusion Deformable Model for 4D Temporal Medical Image GenerationCode1
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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