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 3001–3050 of 6689 papers

TitleStatusHype
DiffSensei: Bridging Multi-Modal LLMs and Diffusion Models for Customized Manga Generation—0
UniReal: Universal Image Generation and Editing via Learning Real-world Dynamics—0
Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion ModelCode0
FiVA: Fine-grained Visual Attribute Dataset for Text-to-Image Diffusion Models—0
Fine-grained Text to Image Synthesis—0
Preference Adaptive and Sequential Text-to-Image Generation—0
A Generative Victim Model for Segmentation—0
Moderating the Generalization of Score-based Generative Model—0
Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
Boosting Alignment for Post-Unlearning Text-to-Image Generative ModelsCode0
ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance—0
ContRail: A Framework for Realistic Railway Image Synthesis using ControlNet—0
Visual Lexicon: Rich Image Features in Language Space—0
Self-Guidance: Boosting Flow and Diffusion Generation on Their Own—0
BudgetFusion: Perceptually-Guided Adaptive Diffusion Models—0
Open-Source Acceleration of Stable-Diffusion.cpp Deployable on All DevicesCode0
GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis—0
CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation—0
Language-Guided Image Tokenization for Generation—0
Nested Diffusion Models Using Hierarchical Latent Priors—0
SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation—0
Not Just Text: Uncovering Vision Modality Typographic Threats in Image Generation Models—0
A Tiered GAN Approach for Monet-Style Image Generation—0
Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors—0
The Silent Prompt: Initial Noise as Implicit Guidance for Goal-Driven Image Generation—0
Continuous Video Process: Modeling Videos as Continuous Multi-Dimensional Processes for Video Prediction—0
Structure-Aware Stylized Image Synthesis for Robust Medical Image SegmentationCode0
One Communication Round is All It Needs for Federated Fine-Tuning Foundation Models—0
AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models—0
Local Curvature Smoothing with Stein's Identity for Efficient Score Matching—0
A Noise is Worth Diffusion Guidance—0
BodyMetric: Evaluating the Realism of Human Bodies in Text-to-Image Generation—0
Multi-Subject Image Synthesis as a Generative Prior for Single-Subject PET Image Reconstruction—0
The Hyperfitting Phenomenon: Sharpening and Stabilizing LLMs for Open-Ended Text Generation—0
Safeguarding Text-to-Image Generation via Inference-Time Prompt-Noise OptimizationCode0
LayerFusion: Harmonized Multi-Layer Text-to-Image Generation with Generative Priors—0
CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation—0
T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts—0
A Framework For Image Synthesis Using Supervised Contrastive Learning—0
Seamless Optical Cloud Computing across Edge-Metro Network for Generative AI—0
DynamicControl: Adaptive Condition Selection for Improved Text-to-Image Generation—0
DIVE: Taming DINO for Subject-Driven Video Editing—0
Partially Conditioned Patch Parallelism for Accelerated Diffusion Model Inference—0
Appearance Matching Adapter for Exemplar-based Semantic Image Synthesis—0
Panoptic Diffusion Models: co-generation of images and segmentation maps—0
Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis—0
Implicit Priors Editing in Stable Diffusion via Targeted Token Adjustment—0
Generalized Diffusion Model with Adjusted Offset Noise—0
Flow Matching with General Discrete Paths: A Kinetic-Optimal Perspective—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