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 46764700 of 6689 papers

TitleStatusHype
Skip-and-Play: Depth-Driven Pose-Preserved Image Generation for Any Objects0
Skip-Thought GAN: Generating Text through Adversarial Training using Skip-Thought Vectors0
SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis0
Skrr: Skip and Re-use Text Encoder Layers for Memory Efficient Text-to-Image Generation0
Advancing Generative Model Evaluation: A Novel Algorithm for Realistic Image Synthesis and Comparison in OCR System0
DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning0
Advancing Diffusion Models: Alias-Free Resampling and Enhanced Rotational Equivariance0
Advancing Deep Learning through Probability Engineering: A Pragmatic Paradigm for Modern AI0
You Don't Have to Be Perfect to Be Amazing: Unveil the Utility of Synthetic Images0
SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models0
Slot-Guided Adaptation of Pre-trained Diffusion Models for Object-Centric Learning and Compositional Generation0
Small-GAN: Speeding Up GAN Training Using Core-sets0
VGAN-Based Image Representation Learning for Privacy-Preserving Facial Expression Recognition0
SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control0
SmartSpatial: Enhancing the 3D Spatial Arrangement Capabilities of Stable Diffusion Models and Introducing a Novel 3D Spatial Evaluation Framework0
VGNC: Reducing the Overfitting of Sparse-view 3DGS via Validation-guided Gaussian Number Control0
VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning0
SMPL-GPTexture: Dual-View 3D Human Texture Estimation using Text-to-Image Generation Models0
SnapGen-V: Generating a Five-Second Video within Five Seconds on a Mobile Device0
Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis0
SNOOPI: Supercharged One-step Diffusion Distillation with Proper Guidance0
Social Biases through the Text-to-Image Generation Lens0
Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges0
ViCTr: Vital Consistency Transfer for Pathology Aware Image Synthesis0
Soft Curriculum for Learning Conditional GANs with Noisy-Labeled and Uncurated Unlabeled Data0
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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