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 2551–2575 of 6689 papers

TitleStatusHype
A psychophysical evaluation of techniques for Mooney image generation—0
Generic 3D Diffusion Adapter Using Controlled Multi-View EditingCode3
LayerDiff: Exploring Text-guided Multi-layered Composable Image Synthesis via Layer-Collaborative Diffusion Model—0
Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory—0
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data—0
Fast High-Resolution Image Synthesis with Latent Adversarial Diffusion Distillation—0
A Survey on Quality Metrics for Text-to-Image Generation—0
QEAN: Quaternion-Enhanced Attention Network for Visual Dance GenerationCode0
Binary Noise for Binary Tasks: Masked Bernoulli Diffusion for Unsupervised Anomaly DetectionCode1
CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion ModelCode2
Urban Scene Diffusion through Semantic Occupancy Map—0
Generative modeling of seismic data using score-based generative modelsCode0
Understanding Diffusion Models by Feynman's Path Integral—0
Zippo: Zipping Color and Transparency Distributions into a Single Diffusion Model—0
CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient InversionCode0
StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining—0
Source Prompt Disentangled Inversion for Boosting Image Editability with Diffusion ModelsCode1
Fast Personalized Text-to-Image Syntheses With Attention Injection—0
GazeFusion: Saliency-Guided Image Generation—0
OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion ModelsCode4
StableGarment: Garment-Centric Generation via Stable Diffusion—0
Reward Guided Latent Consistency Distillation—0
Boosting Flow-based Generative Super-Resolution Models via Learned PriorCode2
LightIt: Illumination Modeling and Control for Diffusion Models—0
Giving a Hand to Diffusion Models: a Two-Stage Approach to Improving Conditional Human Image GenerationCode1
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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