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
Stellar: Systematic Evaluation of Human-Centric Personalized Text-to-Image Methods—0
Compensation Sampling for Improved Convergence in Diffusion ModelsCode0
Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning—0
Disentangled Representation Learning for Controllable Person Image Generation—0
AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion ModelCode2
Correcting Diffusion Generation through ResamplingCode1
Multi-Energy Guided Image Translation with Stochastic Differential Equations for Near-Infrared Facial Expression Recognition—0
Conditional Stochastic Interpolation for Generative Learning—0
Efficient Quantization Strategies for Latent Diffusion Models—0
NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models—0
UDiffText: A Unified Framework for High-quality Text Synthesis in Arbitrary Images via Character-aware Diffusion ModelsCode2
RS-Corrector: Correcting the Racial Stereotypes in Latent Diffusion Models—0
MVDD: Multi-View Depth Diffusion Models—0
Membership Inference Attacks on Diffusion Models via Quantile Regression—0
Reality's Canvas, Language's Brush: Crafting 3D Avatars from Monocular Video—0
Image Synthesis-based Late Stage Cancer Augmentation and Semi-Supervised Segmentation for MRI Rectal Cancer Staging—0
Synthesizing Traffic Datasets using Graph Neural NetworksCode0
SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control—0
LSegDiff: A Latent Diffusion Model for Medical Image Segmentation—0
Resolution Chromatography of Diffusion Models—0
DreamVideo: Composing Your Dream Videos with Customized Subject and Motion—0
Approximate Caching for Efficiently Serving Diffusion Models—0
Investigating the Design Space of Diffusion Models for Speech EnhancementCode1
PhotoMaker: Customizing Realistic Human Photos via Stacked ID EmbeddingCode6
KOALA: Empirical Lessons Toward Memory-Efficient and Fast Diffusion Models for Text-to-Image Synthesis—0
Stable Diffusion for Data Augmentation in COCO and Weed Datasets—0
Multi-View Unsupervised Image Generation with Cross Attention Guidance—0
Generating Illustrated InstructionsCode0
Gen2Det: Generate to Detect—0
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design—0
Forensic Iris Image SynthesisCode0
Autoencoding Labeled Interpolator, Inferring Parameters From Image, And Image From Parameters—0
Understanding (Un)Intended Memorization in Text-to-Image Generative Models—0
Memory Triggers: Unveiling Memorization in Text-To-Image Generative Models through Word-Level Duplication—0
DiffusionSat: A Generative Foundation Model for Satellite ImageryCode2
Cache Me if You Can: Accelerating Diffusion Models through Block Caching—0
Return of Unconditional Generation: A Self-supervised Representation Generation MethodCode2
TokenCompose: Text-to-Image Diffusion with Token-level SupervisionCode1
Kandinsky 3.0 Technical ReportCode2
Data-driven Crop Growth Simulation on Time-varying Generated Images using Multi-conditional Generative Adversarial NetworksCode0
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models—0
FoodFusion: A Latent Diffusion Model for Realistic Food Image Generation—0
WarpDiffusion: Efficient Diffusion Model for High-Fidelity Virtual Try-on—0
Context Diffusion: In-Context Aware Image Generation—0
Exploring Social Bias in Downstream Applications of Text-to-Image Foundation Models—0
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Stable Diffusion Exposed: Gender Bias from Prompt to Image—0
Customization Assistant for Text-to-image GenerationCode2
FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression ReactionCode0
ViscoNet: Bridging and Harmonizing Visual and Textual Conditioning for ControlNetCode1
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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