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 12011225 of 6689 papers

TitleStatusHype
BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian InferenceCode1
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
Feature Proliferation -- the "Cancer" in StyleGAN and its TreatmentsCode1
EasyGen: Easing Multimodal Generation with BiDiffuser and LLMsCode1
Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt RewritingCode1
Interpretable Diffusion via Information DecompositionCode1
Generative Modeling with Phase Stochastic BridgesCode1
Uni-paint: A Unified Framework for Multimodal Image Inpainting with Pretrained Diffusion ModelCode1
ConditionVideo: Training-Free Condition-Guided Text-to-Video GenerationCode1
Advancing Pose-Guided Image Synthesis with Progressive Conditional Diffusion ModelsCode1
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision TransformersCode1
The Emergence of Reproducibility and Generalizability in Diffusion ModelsCode1
Targeted Attack Improves Protection against Unauthorized Diffusion CustomizationCode1
Predictive microstructure image generation using denoising diffusion probabilistic modelsCode1
MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT ImagesCode1
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance FieldsCode1
ImagenHub: Standardizing the evaluation of conditional image generation modelsCode1
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image GenerationCode1
Text-image Alignment for Diffusion-based PerceptionCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
CCEdit: Creative and Controllable Video Editing via Diffusion ModelsCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
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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