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 30513075 of 6689 papers

TitleStatusHype
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective0
Panoptic Diffusion Models: co-generation of images and segmentation maps0
SNOOPI: Supercharged One-step Diffusion Distillation with Proper Guidance0
3D representation in 512-Byte:Variational tokenizer is the key for autoregressive 3D generation0
GIST: Towards Photorealistic Style Transfer via Multiscale Geometric Representations0
WEM-GAN: Wavelet transform based facial expression manipulation0
FoundHand: Large-Scale Domain-Specific Learning for Controllable Hand Image Generation0
ShapeWords: Guiding Text-to-Image Synthesis with 3D Shape-Aware Prompts0
ScImage: How Good Are Multimodal Large Language Models at Scientific Text-to-Image Generation?Code0
Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation0
SerialGen: Personalized Image Generation by First Standardization Then Personalization0
World-consistent Video Diffusion with Explicit 3D Modeling0
LoyalDiffusion: A Diffusion Model Guarding Against Data Replication0
Switti: Designing Scale-Wise Transformers for Text-to-Image Synthesis0
Hierarchical VAE with a Diffusion-based VampPriorCode0
MFTF: Mask-free Training-free Object Level Layout Control Diffusion ModelCode0
CopyrightShield: Spatial Similarity Guided Backdoor Defense against Copyright Infringement in Diffusion Models0
Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via Precision LocalizationCode0
Memories of Forgotten Concepts0
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer0
CtrlNeRF: The Generative Neural Radiation Fields for the Controllable Synthesis of High-fidelity 3D-Aware Images0
Learning on Less: Constraining Pre-trained Model Learning for Generalizable Diffusion-Generated Image Detection0
Energy-Based Prior Latent Space Diffusion model for Reconstruction of Lumbar Vertebrae from Thick Slice MRICode0
Opt-In Art: Learning Art Styles Only from Few Examples0
Retrieval-guided Cross-view Image Synthesis0
Show:102550
← PrevPage 123 of 268Next →

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