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

Showing 25762600 of 6689 papers

TitleStatusHype
Medical Imaging Complexity and its Effects on GAN PerformanceCode0
Adaptive Consensus Optimization Method for GANsCode0
Medical Image Synthesis with Deep Convolutional Adversarial NetworksCode0
Measurement Score-Based Diffusion ModelCode0
M-EBM: Towards Understanding the Manifolds of Energy-Based ModelsCode0
MC-GAN: Multi-conditional Generative Adversarial Network for Image SynthesisCode0
CusConcept: Customized Visual Concept Decomposition with Diffusion ModelsCode0
Curriculum Direct Preference Optimization for Diffusion and Consistency ModelsCode0
MC^2: Multi-concept Guidance for Customized Multi-concept GenerationCode0
Background Image Generation Using Boolean OperationsCode0
Backdoor Attack is a Devil in Federated GAN-based Medical Image SynthesisCode0
Mask Embedding in conditional GAN for Guided Synthesis of High Resolution ImagesCode0
Mask and Restore: Blind Backdoor Defense at Test Time with Masked AutoencoderCode0
ManiTrans: Entity-Level Text-Guided Image Manipulation via Token-wise Semantic Alignment and GenerationCode0
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for Image-to-Image TransformationCode0
Mapping Instructions to Actions in 3D Environments with Visual Goal PredictionCode0
Adapting Diffusion Models for Improved Prompt Compliance and Controllable Image SynthesisCode0
CS^2: A Controllable and Simultaneous Synthesizer of Images and Annotations with Minimal Human InterventionCode0
Axial Attention in Multidimensional TransformersCode0
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial NetworksCode0
Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast AsiaCode0
Fusion Embedding for Pose-Guided Person Image Synthesis with Diffusion ModelCode0
MAGIC: Mask-Guided Image Synthesis by Inverting a Quasi-Robust ClassifierCode0
MAGAN: Margin Adaptation for Generative Adversarial NetworksCode0
MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic AlignmentCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified