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 27262750 of 6689 papers

TitleStatusHype
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer0
On Fairness of Unified Multimodal Large Language Model for Image Generation0
Poisson Flow Joint Model for Multiphase contrast-enhanced CT0
Masked Autoencoders Are Effective Tokenizers for Diffusion Models0
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT0
Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography0
Diffusion Instruction Tuning0
AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI SynthesisCode0
Towards Consistent and Controllable Image Synthesis for Face Editing0
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models0
Unpaired Deblurring via Decoupled Diffusion Model0
Texture Image Synthesis Using Spatial GAN Based on Vision Transformers0
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning0
Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target GenerationCode0
CAT Pruning: Cluster-Aware Token Pruning For Text-to-Image Diffusion ModelsCode0
Video Latent Flow Matching: Optimal Polynomial Projections for Video Interpolation and Extrapolation0
Fast Solvers for Discrete Diffusion Models: Theory and Applications of High-Order Algorithms0
Distorting Embedding Space for Safety: A Defense Mechanism for Adversarially Robust Diffusion ModelsCode0
BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamics0
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data0
REG: Rectified Gradient Guidance for Conditional Diffusion Models0
Diffusion Autoencoders are Scalable Image Tokenizers0
Segmentation-Aware Generative Reinforcement Network (GRN) for Tissue Layer Segmentation in 3-D Ultrasound Images for Chronic Low-back Pain (cLBP) AssessmentCode0
Generative AI for Vision: A Comprehensive Study of Frameworks and Applications0
DebiasPI: Inference-time Debiasing by Prompt Iteration of a Text-to-Image Generative Model0
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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