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 951975 of 6689 papers

TitleStatusHype
MemBench: Memorized Image Trigger Prompt Dataset for Diffusion ModelsCode1
DStruct2Design: Data and Benchmarks for Data Structure Driven Generative Floor Plan DesignCode1
SpotDiffusion: A Fast Approach For Seamless Panorama Generation Over TimeCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image ModelsCode1
Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and EvaluationsCode1
Training-free Composite Scene Generation for Layout-to-Image SynthesisCode1
Latent Diffusion for Medical Image Segmentation: End to end learning for fast sampling and accuracyCode1
InsertDiffusion: Identity Preserving Visualization of Objects through a Training-Free Diffusion ArchitectureCode1
PSC: Posterior Sampling-Based CompressionCode1
LAPT: Label-driven Automated Prompt Tuning for OOD Detection with Vision-Language ModelsCode1
Region Attention Transformer for Medical Image RestorationCode1
DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model TrainingCode1
GAURA: Generalizable Approach for Unified Restoration and Rendering of Arbitrary ViewsCode1
Deformation-Recovery Diffusion Model (DRDM): Instance Deformation for Image Manipulation and SynthesisCode1
Trainable Highly-expressive Activation FunctionsCode1
3D Vessel Graph Generation Using Denoising DiffusionCode1
Diffusion as Sound Propagation: Physics-inspired Model for Ultrasound Image GenerationCode1
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?Code1
Leveraging Latent Diffusion Models for Training-Free In-Distribution Data Augmentation for Surface Defect DetectionCode1
An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image SynthesisCode1
Stochastic Solutions for Simultaneous Seismic Data Denoising and Reconstruction via Score-based Generative ModelsCode1
LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image GenerationCode1
MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose GuidanceCode1
Network Bending of Diffusion Models for Audio-Visual 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