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 56765700 of 6689 papers

TitleStatusHype
SKE-Layout: Spatial Knowledge Enhanced Layout Generation with LLMs0
Sketch2Human: Deep Human Generation with Disentangled Geometry and Appearance Control0
Sketch and Text Guided Diffusion Model for Colored Point Cloud Generation0
Advancing Text-Driven Chest X-Ray Generation with Policy-Based Reinforcement Learning0
Enhancing Diffusion-Based Image Synthesis with Robust Classifier GuidanceCode0
Enhancing Diffusion Models Efficiency by Disentangling Total-Variance and Signal-to-Noise RatioCode0
TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report GenerationCode0
Relational Diffusion Distillation for Efficient Image GenerationCode0
Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute ManipulationsCode0
Object-Centric Relational Representations for Image GenerationCode0
CPGAN: Full-Spectrum Content-Parsing Generative Adversarial Networks for Text-to-Image SynthesisCode0
Enhancing GAN Performance through Neural Architecture Search and Tensor DecompositionCode0
Image Content Generation with Causal ReasoningCode0
Enhancing GANs with MMD Neural Architecture Search, PMish Activation Function, and Adaptive Rank DecompositionCode0
Enhancing Image Generation Fidelity via Progressive PromptsCode0
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large ScaleCode0
Image Difficulty Curriculum for Generative Adversarial Networks (CuGAN)Code0
Image Embedding for Denoising Generative ModelsCode0
Diffusion Counterfactual Generation with Semantic AbductionCode0
Semi-supervised Multimodal Representation Learning through a Global WorkspaceCode0
Enhancing Object Coherence in Layout-to-Image SynthesisCode0
BLADERUNNER: Rapid Countermeasure for Synthetic (AI-Generated) StyleGAN FacesCode0
Medical Image Synthesis with Deep Convolutional Adversarial NetworksCode0
Enhancing Quantitative Image Synthesis through Pretraining and Resolution Scaling for Bone Mineral Density Estimation from a Plain X-ray ImageCode0
Medical Imaging Complexity and its Effects on GAN PerformanceCode0
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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