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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 6689 papers

TitleStatusHype
Latent Flow TransformerCode0
Adaptive Cyclic Diffusion for Inference Scaling—0
Emerging Properties in Unified Multimodal PretrainingCode9
"Haet Bhasha aur Diskrimineshun": Phonetic Perturbations in Code-Mixed Hinglish to Red-Team LLMs—0
Instructing Text-to-Image Diffusion Models via Classifier-Guided Semantic OptimizationCode0
Hunyuan-Game: Industrial-grade Intelligent Game Creation Model—0
UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation—0
Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modeling—0
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to RankCode2
Vision-Language Modeling Meets Remote Sensing: Models, Datasets and Perspectives—0
Training-Free Watermarking for Autoregressive Image GenerationCode1
Improving Compositional Generation with Diffusion Models Using Lift ScoresCode0
Accelerate TarFlow Sampling with GS-Jacobi IterationCode1
Swin DiT: Diffusion Transformer using Pseudo Shifted WindowsCode0
Diffusion Models with Double Guidance: Generate with aggregated datasets—0
FRAbench and GenEval: Scaling Fine-Grained Aspect Evaluation across Tasks, Modalities—0
SounDiT: Geo-Contextual Soundscape-to-Landscape Generation—0
Safe-Sora: Safe Text-to-Video Generation via Graphical Watermarking—0
Higher fidelity perceptual image and video compression with a latent conditioned residual denoising diffusion modelCode0
Diff-MM: Exploring Pre-trained Text-to-Image Generation Model for Unified Multi-modal Object Tracking—0
MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPOCode0
VTBench: Evaluating Visual Tokenizers for Autoregressive Image GenerationCode1
A Physics-Inspired Optimizer: Velocity Regularized Adam—0
Exploring Sparsity for Parameter Efficient Fine Tuning Using WaveletsCode0
Context-Aware Autoregressive Models for Multi-Conditional Image Generation—0
Show:102550
← PrevPage 10 of 268Next →
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