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 51–75 of 6689 papers

TitleStatusHype
Video Perception Models for 3D Scene Synthesis—0
EAR: Erasing Concepts from Unified Autoregressive ModelsCode0
Angio-Diff: Learning a Self-Supervised Adversarial Diffusion Model for Angiographic Geometry GenerationCode0
SoK: Can Synthetic Images Replace Real Data? A Survey of Utility and Privacy of Synthetic Image Generation—0
Morse: Dual-Sampling for Lossless Acceleration of Diffusion ModelsCode1
OmniGen2: Exploration to Advanced Multimodal GenerationCode7
ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image GenerationCode3
Programmable-Room: Interactive Textured 3D Room Meshes Generation Empowered by Large Language Models—0
DreamCube: 3D Panorama Generation via Multi-plane Synchronization—0
The Hidden Cost of an Image: Quantifying the Energy Consumption of AI Image Generation—0
AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario—0
Beyond Blur: A Fluid Perspective on Generative Diffusion Models—0
Machine Mental Imagery: Empower Multimodal Reasoning with Latent Visual TokensCode3
Category-based Galaxy Image Generation via Diffusion Models—0
PAROAttention: Pattern-Aware ReOrdering for Efficient Sparse and Quantized Attention in Visual Generation Models—0
Watermarking Autoregressive Image GenerationCode2
Evolutionary Caching to Accelerate Your Off-the-Shelf Diffusion ModelCode1
Align Your Flow: Scaling Continuous-Time Flow Map Distillation—0
DiffusionBlocks: Blockwise Training for Generative Models via Score-Based Diffusion—0
Risk Estimation of Knee Osteoarthritis Progression via Predictive Multi-task Modelling from Efficient Diffusion Model using X-ray Images—0
Cost-Aware Routing for Efficient Text-To-Image Generation—0
VideoMAR: Autoregressive Video Generatio with Continuous Tokens—0
Decoupled Classifier-Free Guidance for Counterfactual Diffusion Models—0
Fair Generation without Unfair Distortions: Debiasing Text-to-Image Generation with Entanglement-Free Attention—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
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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