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 2101–2150 of 6689 papers

TitleStatusHype
Generative Adversarial Text to Image SynthesisCode1
Semantic Style Transfer and Turning Two-Bit Doodles into Fine ArtworksCode1
Iterative Gaussianization: from ICA to Random RotationsCode1
Pixel Recurrent Neural NetworksCode1
Combining Markov Random Fields and Convolutional Neural Networks for Image SynthesisCode1
DRAW: A Recurrent Neural Network For Image GenerationCode1
MADE: Masked Autoencoder for Distribution EstimationCode1
NICE: Non-linear Independent Components EstimationCode1
Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection—0
FashionPose: Text to Pose to Relight Image Generation for Personalized Fashion Visualization—0
A Distributed Generative AI Approach for Heterogeneous Multi-Domain Environments under Data Sharing constraintsCode0
fastWDM3D: Fast and Accurate 3D Healthy Tissue InpaintingCode0
Pixel Perfect MegaMed: A Megapixel-Scale Vision-Language Foundation Model for Generating High Resolution Medical Images—0
FADE: Adversarial Concept Erasure in Flow Models—0
CATVis: Context-Aware Thought Visualization—0
Implementing Adaptations for Vision AutoRegressive ModelCode0
Latent Space Consistency for Sparse-View CT Reconstruction—0
MFGDiffusion: Mask-Guided Smoke Synthesis for Enhanced Forest Fire DetectionCode0
Vision Foundation Models as Effective Visual Tokenizers for Autoregressive Image Generation—0
Evaluating Attribute Confusion in Fashion Text-to-Image Generation—0
Model-free Optical Processors using In Situ Reinforcement Learning with Proximal Policy Optimization—0
AdaptaGen: Domain-Specific Image Generation through Hierarchical Semantic Optimization Framework—0
TextPixs: Glyph-Conditioned Diffusion with Character-Aware Attention and OCR-Guided Supervision—0
Trojan Horse Prompting: Jailbreaking Conversational Multimodal Models by Forging Assistant Message—0
Estimating Object Physical Properties from RGB-D Vision and Depth Robot Sensors Using Deep LearningCode0
LoomNet: Enhancing Multi-View Image Generation via Latent Space Weaving—0
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer—0
CorrDetail: Visual Detail Enhanced Self-Correction for Face Forgery Detection—0
AI-Driven Cytomorphology Image Synthesis for Medical DiagnosticsCode0
A Training-Free Style-Personalization via Scale-wise Autoregressive Model—0
Hita: Holistic Tokenizer for Autoregressive Image GenerationCode0
UniMC: Taming Diffusion Transformer for Unified Keypoint-Guided Multi-Class Image Generation—0
Rethinking Discrete Tokens: Treating Them as Conditions for Continuous Autoregressive Image Synthesis—0
Locality-aware Parallel Decoding for Efficient Autoregressive Image Generation—0
UniGlyph: Unified Segmentation-Conditioned Diffusion for Precise Visual Text Synthesis—0
Process-aware and high-fidelity microstructure generation using stable diffusion—0
Prompt Mechanisms in Medical Imaging: A Comprehensive Survey—0
GenFlow: Interactive Modular System for Image Generation—0
Instella-T2I: Pushing the Limits of 1D Discrete Latent Space Image Generation—0
Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling—0
ODE_t (ODE_l ): Shortcutting the Time and Length in Diffusion and Flow Models for Faster SamplingCode0
BitMark for Infinity: Watermarking Bitwise Autoregressive Image Generative Models—0
HybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation—0
Rethink Sparse Signals for Pose-guided Text-to-image GenerationCode0
EAR: Erasing Concepts from Unified Autoregressive ModelsCode0
Diffusion Tree Sampling: Scalable inference-time alignment of diffusion models—0
MultiHuman-Testbench: Benchmarking Image Generation for Multiple Humans—0
Med-Art: Diffusion Transformer for 2D Medical Text-to-Image Generation—0
3DGH: 3D Head Generation with Composable Hair and Face—0
Video Perception Models for 3D Scene Synthesis—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