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 4351–4400 of 6689 papers

TitleStatusHype
Density-aware Haze Image Synthesis by Self-Supervised Content-Style Disentanglement—0
Leveraging Generative AI Models to Explore Human Identity—0
Weakly Supervised Keypoint Discovery—0
Leveraging Previous Steps: A Training-free Fast Solver for Flow Diffusion—0
Weakly-Supervised Photo-realistic Texture Generation for 3D Face Reconstruction—0
Leveraging Semantic Attribute Binding for Free-Lunch Color Control in Diffusion Models—0
Leveraging Text-to-Image Generation for Handling Spurious Correlation—0
Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency—0
Leveraging Vision-Language Foundation Models to Reveal Hidden Image-Attribute Relationships in Medical Imaging—0
Leveraging Visual Question Answering to Improve Text-to-Image Synthesis—0
LeX-Art: Rethinking Text Generation via Scalable High-Quality Data Synthesis—0
Lformer: Text-to-Image Generation with L-shape Block Parallel Decoding—0
LG-VQ: Language-Guided Codebook Learning—0
LIDA: A Tool for Automatic Generation of Grammar-Agnostic Visualizations and Infographics using Large Language Models—0
Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation—0
Lifelong GAN: Continual Learning for Conditional Image Generation—0
A self-attention-based differentially private tabular GAN with high data utility—0
LightIt: Illumination Modeling and Control for Diffusion Models—0
Lightweight Long-Range Generative Adversarial Networks—0
Training Data Protection with Compositional Diffusion Models—0
LIME: Localized Image Editing via Attention Regularization in Diffusion Models—0
LineArt: A Knowledge-guided Training-free High-quality Appearance Transfer for Design Drawing with Diffusion Model—0
Line Artist: A Multiple Style Sketch to Painting Synthesis Scheme—0
DiffAug: A Diffuse-and-Denoise Augmentation for Training Robust Classifiers—0
LinkGAN: Linking GAN Latents to Pixels for Controllable Image Synthesis—0
AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation—0
LiT: Delving into a Simplified Linear Diffusion Transformer for Image Generation—0
LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models—0
LMFusion: Adapting Pretrained Language Models for Multimodal Generation—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
Training Diffusion Models with Federated Learning—0
LLM as an Art Director (LaDi): Using LLMs to improve Text-to-Media Generators—0
Training End-to-end Single Image Generators without GANs—0
Artworks Reimagined: Exploring Human-AI Co-Creation through Body Prompting—0
Training Energy-Based Models with Diffusion Contrastive Divergences—0
ARTIST: Improving the Generation of Text-rich Images with Disentangled Diffusion Models and Large Language Models—0
Training face verification models from generated face identity data—0
Training-free Color-Style Disentanglement for Constrained Text-to-Image Synthesis—0
LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks—0
Load Balanced GANs for Multi-view Face Image Synthesis—0
Abnormal Colon Polyp Image Synthesis Using Conditional Adversarial Networks for Improved Detection Performance—0
Local Attention Pyramid for Scene Image Generation—0
Local Conditional Controlling for Text-to-Image Diffusion Models—0
Local Curvature Smoothing with Stein's Identity for Efficient Score Matching—0
Local Differential Privacy Image Generation Using Flow-based Deep Generative Models—0
Locality-Aware Generalizable Implicit Neural Representation—0
Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation—0
Training-free Diffusion Acceleration with Bottleneck Sampling—0
Localized Text-to-Image Generation for Free via Cross Attention Control—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