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 31513175 of 6689 papers

TitleStatusHype
Test-time Conditional Text-to-Image Synthesis Using Diffusion Models0
CART: Compositional Auto-Regressive Transformer for Image Generation0
Safe Text-to-Image Generation: Simply Sanitize the Prompt Embedding0
Boundary Attention Constrained Zero-Shot Layout-To-Image Generation0
Adaptive Non-Uniform Timestep Sampling for Diffusion Model Training0
Visual question answering based evaluation metrics for text-to-image generation0
Content-Aware Preserving Image Generation0
Advancing Diffusion Models: Alias-Free Resampling and Enhanced Rotational Equivariance0
Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models0
A Survey on Vision Autoregressive Model0
Towards More Accurate Fake Detection on Images Generated from Advanced Generative and Neural Rendering Models0
Leveraging Previous Steps: A Training-free Fast Solver for Flow Diffusion0
Tracing the Roots: Leveraging Temporal Dynamics in Diffusion Trajectories for Origin Attribution0
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing0
Mediffusion: Joint Diffusion for Self-Explainable Semi-Supervised Classification and Medical Image Generation0
Evaluating the Generation of Spatial Relations in Text and Image Generative Models0
Emotion Classification of Children Expressions0
More Expressive Attention with Negative WeightsCode0
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
Exploring Variational Autoencoders for Medical Image Generation: A Comprehensive Study0
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
Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models0
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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