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 13511375 of 6689 papers

TitleStatusHype
Physics Informed Distillation for Diffusion ModelsCode2
Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Models0
A Survey on Vision Autoregressive Model0
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation0
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing0
TIPO: Text to Image with Text Presampling for Prompt OptimizationCode2
Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution0
Emotion Classification of Children Expressions0
Evaluating the Generation of Spatial Relations in Text and Image Generative Models0
Leveraging Previous Steps: A Training-free Fast Solver for Flow Diffusion0
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study0
Token Merging for Training-Free Semantic Binding in Text-to-Image SynthesisCode2
ENAT: Rethinking Spatial-temporal Interactions in Token-based Image SynthesisCode1
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models0
Layout Control and Semantic Guidance with Attention Loss Backward for T2I Diffusion Model0
More Expressive Attention with Negative WeightsCode0
Region-Aware Text-to-Image Generation via Hard Binding and Soft RefinementCode4
DDIM-Driven Coverless Steganography Scheme with Real Key0
Scalable, Tokenization-Free Diffusion Model Architectures with Efficient Initial Convolution and Fixed-Size Reusable Structures for On-Device Image Generation0
PointCG: Self-supervised Point Cloud Learning via Joint Completion and Generation0
Improving image synthesis with diffusion-negative sampling0
Autoregressive Models in Vision: A SurveyCode4
Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models0
Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation ModelCode0
Conditional Diffusion Model for Longitudinal Medical Image Generation0
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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