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 61266150 of 6689 papers

TitleStatusHype
Stable Attribute Group Editing for Reliable Few-shot Image GenerationCode0
2S-ODIS: Two-Stage Omni-Directional Image Synthesis by Geometric Distortion CorrectionCode0
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial NetworksCode0
PrefGen: Preference Guided Image Generation with Relative AttributesCode0
Multiscale structural similarity for image quality assessmentCode0
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion ModelsCode0
Multi-Scale Texture Loss for CT denoising with GANsCode0
From Trojan Horses to Castle Walls: Unveiling Bilateral Data Poisoning Effects in Diffusion ModelsCode0
SAGE: Exploring the Boundaries of Unsafe Concept Domain with Semantic-Augment ErasingCode0
fruit-SALAD: A Style Aligned Artwork Dataset to reveal similarity perception in image embeddingsCode0
Cycle-Consistent Generative Rendering for 2D-3D Modality TranslationCode0
Premonition: Using Generative Models to Preempt Future Data Changes in Continual LearningCode0
ODE_t (ODE_l ): Shortcutting the Time and Length in Diffusion and Flow Models for Faster SamplingCode0
Multi-Stage Variational Auto-Encoders for Coarse-to-Fine Image GenerationCode0
BRUNO: A Deep Recurrent Model for Exchangeable DataCode0
Deferred Neural Rendering: Image Synthesis using Neural TexturesCode0
Tell, Draw, and Repeat: Generating and Modifying Images Based on Continual Linguistic InstructionCode0
Kernel Mean Matching for Content Addressability of GANsCode0
Consistency Regularization for Variational Auto-EncodersCode0
Text to artistic image generationCode0
DEff-GAN: Diverse Attribute Transfer for Few-Shot Image SynthesisCode0
Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRICode0
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network AcceleratorsCode0
Cross-Modality Neuroimage Synthesis: A SurveyCode0
Deformable GANs for Pose-based Human Image GenerationCode0
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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