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 701750 of 6689 papers

TitleStatusHype
Diffusion Facial Forgery DetectionCode2
Beyond Self-attention: External Attention using Two Linear Layers for Visual TasksCode2
BoxDiff: Text-to-Image Synthesis with Training-Free Box-Constrained DiffusionCode2
A Style-Based Generator Architecture for Generative Adversarial NetworksCode2
Fréchet Video Motion Distance: A Metric for Evaluating Motion Consistency in VideosCode2
Flux Already Knows -- Activating Subject-Driven Image Generation without TrainingCode2
Diffusion-GAN: Training GANs with DiffusionCode2
Diffusion Enhancement for Cloud Removal in Ultra-Resolution Remote Sensing ImageryCode2
Be Yourself: Bounded Attention for Multi-Subject Text-to-Image GenerationCode2
Planting a SEED of Vision in Large Language ModelCode2
Diffusion Explainer: Visual Explanation for Text-to-image Stable DiffusionCode2
Hybrid Fourier Score Distillation for Efficient One Image to 3D Object GenerationCode2
Cross-view Masked Diffusion Transformers for Person Image SynthesisCode2
Pose-Normalized Image Generation for Person Re-identificationCode2
FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept CompositionCode2
Progressive Distillation for Fast Sampling of Diffusion ModelsCode2
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory MatchingCode2
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and QualityCode2
HiFlow: Training-free High-Resolution Image Generation with Flow-Aligned GuidanceCode2
Q-DiT: Accurate Post-Training Quantization for Diffusion TransformersCode2
Flow Matching in Latent SpaceCode2
FlowTurbo: Towards Real-time Flow-Based Image Generation with Velocity RefinerCode2
RealCompo: Balancing Realism and Compositionality Improves Text-to-Image Diffusion ModelsCode2
CTR-Driven Advertising Image Generation with Multimodal Large Language ModelsCode2
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow MatchingCode2
CapHuman: Capture Your Moments in Parallel UniversesCode2
Boosting Latent Diffusion with Flow MatchingCode2
Diffusion-Enhanced Test-time Adaptation with Text and Image AugmentationCode2
Flow-Guided Diffusion for Video InpaintingCode2
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous TokensCode2
Enhancing Detail Preservation for Customized Text-to-Image Generation: A Regularization-Free ApproachCode2
RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and GenerationCode2
From Parts to Whole: A Unified Reference Framework for Controllable Human Image GenerationCode2
Geometry-Complete Diffusion for 3D Molecule Generation and OptimizationCode2
MDTv2: Masked Diffusion Transformer is a Strong Image SynthesizerCode2
Semantic Image Synthesis via Diffusion ModelsCode2
DiffusionCLIP: Text-Guided Diffusion Models for Robust Image ManipulationCode1
Diffusion Cocktail: Mixing Domain-Specific Diffusion Models for Diversified Image GenerationsCode1
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion ModelsCode1
FLAME Diffuser: Wildfire Image Synthesis using Mask Guided DiffusionCode1
Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect DetectionCode1
Diffusion-based Data Augmentation for Nuclei Image SegmentationCode1
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-TrainingCode1
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision TransformersCode1
A Simple and Effective Baseline for Attentional Generative Adversarial NetworksCode1
FlexDiT: Dynamic Token Density Control for Diffusion TransformerCode1
Diffusion Autoencoders: Toward a Meaningful and Decodable RepresentationCode1
A Shared Representation for Photorealistic Driving SimulatorsCode1
Diffusion-based Blind Text Image Super-ResolutionCode1
Show:102550
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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