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 33013325 of 6689 papers

TitleStatusHype
Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion ModelCode1
ConditionVideo: Training-Free Condition-Guided Text-to-Video GenerationCode1
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling0
Improving Compositional Text-to-image Generation with Large Vision-Language Models0
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion ModelsCode1
Learning Stackable and Skippable LEGO Bricks for Efficient, Reconfigurable, and Variable-Resolution Diffusion ModelingCode0
Efficient-VQGAN: Towards High-Resolution Image Generation with Efficient Vision Transformers0
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision TransformersCode1
A Bias-Variance-Covariance Decomposition of Kernel Scores for Generative ModelsCode0
Perceptual Artifacts Localization for Image Synthesis Tasks0
Language Model Beats Diffusion -- Tokenizer is Key to Visual GenerationCode4
Locality-Aware Generalizable Implicit Neural Representation0
The Emergence of Reproducibility and Generalizability in Diffusion ModelsCode1
Targeted Attack Improves Protection against Unauthorized Diffusion CustomizationCode1
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection0
Observation-Guided Diffusion Probabilistic ModelsCode0
BrainSCUBA: Fine-Grained Natural Language Captions of Visual Cortex Selectivity0
Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step InferenceCode4
Assessing Robustness via Score-Based Adversarial Image Generation0
Predictive microstructure image generation using denoising diffusion probabilistic modelsCode1
RL-based Stateful Neural Adaptive Sampling and Denoising for Real-Time Path TracingCode0
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency0
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT ImagesCode1
Kandinsky: an Improved Text-to-Image Synthesis with Image Prior and Latent DiffusionCode4
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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