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 3251–3300 of 6689 papers

TitleStatusHype
ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking SamplingCode1
A Pytorch Reproduction of Masked Generative Image TransformerCode2
Diffusion-based Data Augmentation for Nuclei Image SegmentationCode1
Improved Techniques for Training Consistency ModelsCode1
Adversarial Image Generation by Spatial Transformation in Perceptual ColorspacesCode1
DPM-Solver-v3: Improved Diffusion ODE Solver with Empirical Model StatisticsCode1
Localizing and Editing Knowledge in Text-to-Image Generative Models—0
Bridging the Gap between Synthetic and Authentic Images for Multimodal Machine TranslationCode0
CycleNet: Rethinking Cycle Consistency in Text-Guided Diffusion for Image ManipulationCode1
PrivImage: Differentially Private Synthetic Image Generation using Diffusion Models with Semantic-Aware PretrainingCode1
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion ModelsCode1
Conditional Generative Modeling for Images, 3D Animations, and Video—0
Diverse Diffusion: Enhancing Image Diversity in Text-to-Image Generation—0
PrivacyGAN: robust generative image privacy—0
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and GenerationCode1
STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models—0
DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion ModelCode1
An Image is Worth Multiple Words: Discovering Object Level Concepts using Multi-Concept Prompt LearningCode1
Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder ApproachCode1
Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven OptimizationCode1
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian InferenceCode1
Elucidating The Design Space of Classifier-Guided Diffusion GenerationCode1
LAMP: Learn A Motion Pattern for Few-Shot-Based Video GenerationCode2
BiomedJourney: Counterfactual Biomedical Image Generation by Instruction-Learning from Multimodal Patient Journeys—0
LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed PromptsCode1
Scene Graph Conditioning in Latent DiffusionCode1
ConsistNet: Enforcing 3D Consistency for Multi-view Images DiffusionCode1
A Survey on Video Diffusion ModelsCode4
ViPE: Visualise Pretty-much EverythingCode0
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation—0
Unified High-binding Watermark for Unconditional Image Generation Models—0
CopyScope: Model-level Copyright Infringement Quantification in the Diffusion Workflow—0
R&B: Region and Boundary Aware Zero-shot Grounded Text-to-image Generation—0
Hypernymy Understanding Evaluation of Text-to-Image Models via WordNet HierarchyCode0
Discovery and Expansion of New Domains within Diffusion Models—0
Feature Proliferation -- the "Cancer" in StyleGAN and its TreatmentsCode1
EasyGen: Easing Multimodal Generation with BiDiffuser and LLMsCode1
SingleInsert: Inserting New Concepts from a Single Image into Text-to-Image Models for Flexible Editing—0
HyperHuman: Hyper-Realistic Human Generation with Latent Structural Diffusion—0
Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt RewritingCode1
Neural Diffusion Models—0
PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training ParadigmCode2
Interpretable Diffusion via Information DecompositionCode1
Unmasking Bias in Diffusion Model TrainingCode0
DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion modelCode2
Generative Modeling with Phase Stochastic BridgesCode1
ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion ModelsCode2
Distance Weighted Trans Network for Image Completion—0
Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion ModelCode1
Mini-DALLE3: Interactive Text to Image by Prompting Large Language ModelsCode2
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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