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 18761900 of 6689 papers

TitleStatusHype
Perm: A Parametric Representation for Multi-Style 3D Hair ModelingCode2
MVPbev: Multi-view Perspective Image Generation from BEV with Test-time Controllability and GeneralizabilityCode1
Temporal Feature Matters: A Framework for Diffusion Model QuantizationCode2
Artificial Immunofluorescence in a Flash: Rapid Synthetic Imaging from Brightfield Through Residual Diffusion0
Guided Latent Slot Diffusion for Object-Centric Learning0
ReCorD: Reasoning and Correcting Diffusion for HOI GenerationCode1
Utilizing Generative Adversarial Networks for Image Data Augmentation and Classification of Semiconductor Wafer Dicing Induced Defects0
MemBench: Memorized Image Trigger Prompt Dataset for Diffusion ModelsCode1
ViPer: Visual Personalization of Generative Models via Individual Preference Learning0
Adaptive Gradient Regularization: A Faster and Generalizable Optimization Technique for Deep Neural Networks0
Synthetic Trajectory Generation Through Convolutional Neural NetworksCode0
Artistic Intelligence: A Diffusion-Based Framework for High-Fidelity Landscape Painting Synthesis0
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion ModelsCode0
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in VideosCode2
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
DStruct2Design: Data and Benchmarks for Data Structure Driven Generative Floor Plan DesignCode1
SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over TimeCode1
CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion ModelsCode5
Distilling Vision-Language Foundation Models: A Data-Free Approach via Prompt Diversification0
BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image ModelsCode1
Variational Potential Flow: A Novel Probabilistic Framework for Energy-Based Generative Modelling0
MedEdit: Counterfactual Diffusion-based Image Editing on Brain MRI0
LSReGen: Large-Scale Regional Generator via Backward Guidance Framework0
Diffusion Models as Data Mining Tools0
-Brush: Controllable Large Image Synthesis with Diffusion Models in Infinite DimensionsCode0
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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