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 3851–3875 of 6689 papers

TitleStatusHype
Human Image Generation: A Comprehensive Survey—0
Human Imperceptible Attacks and Applications to Improve Fairness—0
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes—0
A Unified Conditional Framework for Diffusion-based Image Restoration—0
Human Silhouette and Skeleton Video Synthesis through Wi-Fi signals—0
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment—0
Augmenting medical image classifiers with synthetic data from latent diffusion models—0
Hunyuan-Game: Industrial-grade Intelligent Game Creation Model—0
Zippo: Zipping Color and Transparency Distributions into a Single Diffusion Model—0
Accuracy and Fidelity Comparison of Luna and DALL-E 2 Diffusion-Based Image Generation Systems—0
HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation—0
ACCORD: Alleviating Concept Coupling through Dependence Regularization for Text-to-Image Diffusion Personalization—0
Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models—0
HYPE-C: Evaluating Image Completion Models Through Standardized Crowdsourcing—0
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models—0
HYPE: Human-eYe Perceptual Evaluation of Generative Models—0
Hyperbolic Generative Adversarial Network—0
This Intestine Does Not Exist: Multiscale Residual Variational Autoencoder for Realistic Wireless Capsule Endoscopy Image Generation—0
HyperCGAN: Text-to-Image Synthesis with HyperNet-Modulated Conditional Generative Adversarial Networks—0
Augmenting Images for ASR and TTS through Single-loop and Dual-loop Multimodal Chain Framework—0
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion—0
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories—0
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis—0
Hyperspectral Image Generation with Unmixing Guided Diffusion Model—0
I2AM: Interpreting Image-to-Image Latent Diffusion Models via Attribution Maps—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