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 5701–5750 of 6689 papers

TitleStatusHype
Your Image is Secretly the Last Frame of a Pseudo Video—0
Ensuring Consistency for In-Image Translation—0
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models—0
Entropy Rectifying Guidance for Diffusion and Flow Models—0
Entropy-regularized Optimal Transport Generative Models—0
Synthesizing Environment-Specific People in Photographs—0
ePBR: Extended PBR Materials in Image Synthesis—0
EqGAN: Feature Equalization Fusion for Few-shot Image Generation—0
LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models—0
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling—0
ERA-Solver: Error-Robust Adams Solver for Fast Sampling of Diffusion Probabilistic Models—0
AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation—0
ERUPT: Efficient Rendering with Unposed Patch Transformer—0
Estimating Fine-Grained Noise Model via Contrastive Learning—0
Estimating High Order Gradients of the Data Distribution by Denoising—0
Taming Stable Diffusion for Text to 360 Panorama Image Generation—0
ET3D: Efficient Text-to-3D Generation via Multi-View Distillation—0
AdaDiff: Adaptive Step Selection for Fast Diffusion Models—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
Black Hole-Driven Identity Absorbing in Diffusion Models—0
Step Saver: Predicting Minimum Denoising Steps for Diffusion Model Image Generation—0
Evaluating a Synthetic Image Dataset Generated with Stable Diffusion—0
Evaluating Attribute Confusion in Fashion Text-to-Image Generation—0
Evaluating GAN-Based Image Augmentation for Threat Detection in Large-Scale Xray Security Images—0
Precision-Recall Curves Using Information Divergence Frontiers—0
Evaluating Image Caption via Cycle-consistent Text-to-Image Generation—0
Diffusion Model Compression for Image-to-Image Translation—0
DiffuSyn Bench: Evaluating Vision-Language Models on Real-World Complexities with Diffusion-Generated Synthetic Benchmarks—0
Evaluating Procedures for Establishing Generative Adversarial Network-based Stochastic Image Models in Medical Imaging—0
A Survey on Quality Metrics for Text-to-Image Generation—0
Evaluating Text-to-Image Synthesis with a Conditional Fréchet Distance—0
Evaluating Textual Representations through Image Generation—0
Evaluating the Capabilities of Multi-modal Reasoning Models with Synthetic Task Data—0
TauPETGen: Text-Conditional Tau PET Image Synthesis Based on Latent Diffusion Models—0
Evaluating the Generation of Spatial Relations in Text and Image Generative Models—0
Evaluating Utility of Memory Efficient Medical Image Generation: A Study on Lung Nodule Segmentation—0
Evaluation Metric for Quality Control and Generative Models in Histopathology Images—0
Evaluation Metrics for Conditional Image Generation—0
Evaluation of Machine-generated Biomedical Images via A Tally-based Similarity Measure—0
Event-Customized Image Generation—0
Everything is There in Latent Space: Attribute Editing and Attribute Style Manipulation by StyleGAN Latent Space Exploration—0
Evolutionary perspective on model fine-tuning—0
Evolutionary Retrofitting—0
BizGen: Advancing Article-level Visual Text Rendering for Infographics Generation—0
Evolving GAN Formulations for Higher Quality Image Synthesis—0
Visual question answering based evaluation metrics for text-to-image generation—0
Visual-Relation Conscious Image Generation from Structured-Text—0
Example-Based Image Synthesis via Randomized Patch-Matching—0
Example-Based Sampling with Diffusion Models—0
Example-Guided Image Synthesis across Arbitrary Scenes using Masked Spatial-Channel Attention and Self-Supervision—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