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

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Showing 1001–1050 of 6689 papers

TitleStatusHype
AutoPresent: Designing Structured Visuals from ScratchCode2
Regression Guided Strategy to Automated Facial Beauty Optimization through Image Synthesis—0
Dual Diffusion for Unified Image Generation and UnderstandingCode2
Token Pruning for Caching Better: 9 Times Acceleration on Stable Diffusion for FreeCode0
MLLM-as-a-Judge for Image Safety without Human Labeling—0
PQD: Post-training Quantization for Efficient Diffusion Models—0
Text-to-Image GAN with Pretrained Representations—0
Quantum Diffusion Model for Quark and Gluon Jet GenerationCode0
Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis—0
VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing ControlCode2
Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation—0
Motion Transfer-Driven intra-class data augmentation for Finger Vein RecognitionCode0
Open-Sora: Democratizing Efficient Video Production for AllCode13
Zero-Shot Image Restoration Using Few-Step Guidance of Consistency Models (and Beyond)Code1
Diff4MMLiTS: Advanced Multimodal Liver Tumor Segmentation via Diffusion-Based Image Synthesis and Alignment—0
FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian PerturbationCode1
INFELM: In-depth Fairness Evaluation of Large Text-To-Image Models—0
Deep Generalized Schrödinger Bridges: From Image Generation to Solving Mean-Field Games—0
An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models—0
Focusing Image Generation to Mitigate Spurious Correlations—0
Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales—0
P3S-Diffusion:A Selective Subject-driven Generation Framework via Point Supervision—0
Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation—0
Protective Perturbations against Unauthorized Data Usage in Diffusion-based Image Generation—0
DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering—0
Elucidating Flow Matching ODE Dynamics with Respect to Data Geometries—0
UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation—0
DRDM: A Disentangled Representations Diffusion Model for Synthesizing Realistic Person Images—0
1.58-bit FLUX—0
Fashionability-Enhancing Outfit Image Editing with Conditional Diffusion Models—0
Ensuring Consistency for In-Image Translation—0
EvalMuse-40K: A Reliable and Fine-Grained Benchmark with Comprehensive Human Annotations for Text-to-Image Generation Model EvaluationCode2
Extract Free Dense Misalignment from CLIPCode1
Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction—0
RDPM: Solve Diffusion Probabilistic Models via Recurrent Token Prediction—0
Personalized Large Vision-Language Models—0
Discriminative Image Generation with Diffusion Models for Zero-Shot Learning—0
Diffusion-Based Approaches in Medical Image Generation and Analysis—0
RealisID: Scale-Robust and Fine-Controllable Identity Customization via Local and Global Complementation—0
Modular Conversational Agents for Surveys and Interviews—0
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories—0
Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers—0
Human-Guided Image Generation for Expanding Small-Scale Training Image DatasetsCode0
DreamOmni: Unified Image Generation and Editing—0
Self-Corrected Flow Distillation for Consistent One-Step and Few-Step Text-to-Image GenerationCode0
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow MatchingCode2
Adversarial Attack Against Images Classification based on Generative Adversarial Networks—0
When Worse is Better: Navigating the compression-generation tradeoff in visual tokenization—0
Stylish and Functional: Guided Interpolation Subject to Physical Constraints—0
CLEAR: Conv-Like Linearization Revs Pre-Trained Diffusion Transformers UpCode3
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified