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 64516475 of 6689 papers

TitleStatusHype
SPG-Net: Segmentation Prediction and Guidance Network for Image InpaintingCode0
Training Class-Imbalanced Diffusion Model Via Overlap OptimizationCode0
Δ-DiT: A Training-Free Acceleration Method Tailored for Diffusion TransformersCode0
DiffuMask: Synthesizing Images with Pixel-level Annotations for Semantic Segmentation Using Diffusion ModelsCode0
Automatic Generation of Semantic Parts for Face Image SynthesisCode0
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited DataCode0
CAT: Contrastive Adapter Training for Personalized Image GenerationCode0
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion ModelsCode0
Sphere Generative Adversarial Network Based on Geometric Moment MatchingCode0
Guided and Variance-Corrected Fusion with One-shot Style Alignment for Large-Content Image GenerationCode0
Eliminating Contextual Prior Bias for Semantic Image Editing via Dual-Cycle DiffusionCode0
Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit AssignmentCode0
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANsCode0
RAISE: Realness Assessment for Image Synthesis and EvaluationCode0
An Infinite Parade of Giraffes: Expressive Augmentation and Complexity Layers for Cartoon DrawingCode0
Guided Image Synthesis via Initial Image Editing in Diffusion ModelCode0
Understanding and Mitigating Compositional Issues in Text-to-Image Generative ModelsCode0
ControlThinker: Unveiling Latent Semantics for Controllable Image Generation through Visual ReasoningCode0
DR-GAN: Distribution Regularization for Text-to-Image GenerationCode0
Spherical Manifold Guided Diffusion Model for Panoramic Image GenerationCode0
Understanding and Stabilizing GANs' Training Dynamics with Control TheoryCode0
Dealing with Synthetic Data Contamination in Online Continual LearningCode0
Guiding InfoGAN with Semi-SupervisionCode0
LiteVAR: Compressing Visual Autoregressive Modelling with Efficient Attention and QuantizationCode0
Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) AssessmentCode0
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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