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 2926–2950 of 6689 papers

TitleStatusHype
Harnessing Caption Detailness for Data-Efficient Text-to-Image Generation—0
Gradpaint: Gradient-Guided Inpainting with Diffusion Models—0
Harnessing Global-Local Collaborative Adversarial Perturbation for Anti-Customization—0
Creating Image Datasets in Agricultural Environments using DALL.E: Generative AI-Powered Large Language Model—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Gradient-Informed Quality Diversity for the Illumination of Discrete Spaces—0
Gradient-Guided Conditional Diffusion Models for Private Image Reconstruction: Analyzing Adversarial Impacts of Differential Privacy and Denoising—0
Have we unified image generation and understanding yet? An empirical study of GPT-4o's image generation ability—0
Deep OCT Angiography Image Generation for Motion Artifact Suppression—0
Deep D-bar: Real time Electrical Impedance Tomography Imaging with Deep Neural Networks—0
HeadRouter: A Training-free Image Editing Framework for MM-DiTs by Adaptively Routing Attention Heads—0
Bias in Large Language Models Across Clinical Applications: A Systematic Review—0
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation—0
HepatoGEN: Generating Hepatobiliary Phase MRI with Perceptual and Adversarial Models—0
Heredity-aware Child Face Image Generation with Latent Space Disentanglement—0
HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization—0
Heuristics for Image Generation from Scene Graphs—0
Gradient-Free Textual Inversion—0
Gradient-Free Classifier Guidance for Diffusion Model Sampling—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—0
Deep Convolutional GANs for Car Image Generation—0
Gradient Domain Diffusion Models for Image Synthesis—0
HiDiffusion: Unlocking Higher-Resolution Creativity and Efficiency in Pretrained Diffusion Models—0
Bias-Free FedGAN: A Federated Approach to Generate Bias-Free Datasets—0
Improved Training with Curriculum GANs—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