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

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Showing 3801–3825 of 6689 papers

TitleStatusHype
High Quality Diffusion Distillation on a Single GPU with Relative and Absolute Position Matching—0
High-Quality Medical Image Generation from Free-hand Sketch—0
High-resolution efficient image generation from WiFi CSI using a pretrained latent diffusion model—0
ACE: Attentional Concept Erasure in Diffusion Models—0
The Right Losses for the Right Gains: Improving the Semantic Consistency of Deep Text-to-Image Generation with Distribution-Sensitive Losses—0
High-Resolution Image Synthesis via Next-Token Prediction—0
Manifold Constraint Regularization for Remote Sensing Image Generation—0
High-Resolution Mammogram Synthesis using Progressive Generative Adversarial Networks—0
High-resolution medical image synthesis using progressively grown generative adversarial networks—0
High Resolution Seismic Waveform Generation using Denoising Diffusion—0
High Resolution Solar Image Generation using Generative Adversarial Networks—0
High-Resolution UAV Image Generation for Sorghum Panicle Detection—0
Thermal to Visible Image Synthesis under Atmospheric Turbulence—0
Accurate Ground-Truth Depth Image Generation via Overfit Training of Point Cloud Registration using Local Frame Sets—0
HiPA: Enabling One-Step Text-to-Image Diffusion Models via High-Frequency-Promoting Adaptation—0
Thermoxels: a voxel-based method to generate simulation-ready 3D thermal models—0
HiREN: Towards Higher Supervision Quality for Better Scene Text Image Super-Resolution—0
HiScene: Creating Hierarchical 3D Scenes with Isometric View Generation—0
The Role of Generative AI in Facilitating Social Interactions: A Scoping Review—0
The Silent Prompt: Initial Noise as Implicit Guidance for Goal-Driven Image Generation—0
The Spectral Bias of Polynomial Neural Networks—0
HiWave: Training-Free High-Resolution Image Generation via Wavelet-Based Diffusion Sampling—0
HMAR: Efficient Hierarchical Masked Auto-Regressive Image Generation—0
Accurate Compression of Text-to-Image Diffusion Models via Vector Quantization—0
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data—0
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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