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 2251–2300 of 6689 papers

TitleStatusHype
Computational Tradeoffs in Image Synthesis: Diffusion, Masked-Token, and Next-Token Prediction—0
An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual RepresentationCode0
CustomText: Customized Textual Image Generation using Diffusion Models—0
Personalized Residuals for Concept-Driven Text-to-Image Generation—0
Diffusion for World Modeling: Visual Details Matter in AtariCode5
Slicedit: Zero-Shot Video Editing With Text-to-Image Diffusion Models Using Spatio-Temporal SlicesCode2
TriLoRA: Integrating SVD for Advanced Style Personalization in Text-to-Image Generation—0
UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers—0
On the Trajectory Regularity of ODE-based Diffusion SamplingCode3
Lean Attention: Hardware-Aware Scalable Attention Mechanism for the Decode-Phase of Transformers—0
Improving face generation quality and prompt following with synthetic captions—0
UniRAG: Universal Retrieval Augmentation for Large Vision Language ModelsCode1
Chameleon: Mixed-Modal Early-Fusion Foundation ModelsCode7
MediSyn: Text-Guided Diffusion Models for Broad Medical 2D and 3D Image Synthesis—0
VirtualModel: Generating Object-ID-retentive Human-object Interaction Image by Diffusion Model for E-commerce Marketing—0
KPNDepth: Depth Estimation of Lane Images under Complex Rainy Environment—0
VisioBlend: Sketch and Stroke-Guided Denoising Diffusion Probabilistic Model for Realistic Image Generation—0
DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual ExplanationsCode0
Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images—0
Similarity and Quality Metrics for MR Image-To-Image Translation—0
Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese UnderstandingCode7
Compositional Text-to-Image Generation with Dense Blob Representations—0
RATLIP: Generative Adversarial CLIP Text-to-Image Synthesis Based on Recurrent Affine TransformationsCode0
SAR Image Synthesis with Diffusion Models—0
Quality Assessment for AI Generated Images with Instruction TuningCode1
Training-free Subject-Enhanced Attention Guidance for Compositional Text-to-image Generation—0
Deep MMD Gradient Flow without adversarial training—0
Controllable Image Generation With Composed Parallel Token Prediction—0
MasterWeaver: Taming Editability and Face Identity for Personalized Text-to-Image GenerationCode2
An Inversion-based Measure of Memorization for Diffusion ModelsCode0
Exploring Text-Guided Single Image Editing for Remote Sensing ImagesCode0
Frame Interpolation with Consecutive Brownian Bridge DiffusionCode1
A Survey on Personalized Content Synthesis with Diffusion Models—0
VM-DDPM: Vision Mamba Diffusion for Medical Image Synthesis—0
Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte CarloCode0
Variational Schrödinger Diffusion Models—0
FlexEControl: Flexible and Efficient Multimodal Control for Text-to-Image Generation—0
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI—0
HAGAN: Hybrid Augmented Generative Adversarial Network for Medical Image Synthesis—0
TexControl: Sketch-Based Two-Stage Fashion Image Generation Using Diffusion Model—0
Diffusion-driven GAN Inversion for Multi-Modal Face Image GenerationCode0
Inf-DiT: Upsampling Any-Resolution Image with Memory-Efficient Diffusion TransformerCode3
Simple Drop-in LoRA Conditioning on Attention Layers Will Improve Your Diffusion Model—0
ResNCT: A Deep Learning Model for the Synthesis of Nephrographic Phase Images in CT Urography—0
CCDM: Continuous Conditional Diffusion Models for Image GenerationCode1
Generated Contents Enrichment—0
ImageInWords: Unlocking Hyper-Detailed Image DescriptionsCode3
Data-Efficient Molecular Generation with Hierarchical Textual InversionCode0
U-DiTs: Downsample Tokens in U-Shaped Diffusion TransformersCode3
Functional Imaging Constrained Diffusion for Brain PET Synthesis from Structural MRICode0
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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