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 15011525 of 6689 papers

TitleStatusHype
Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image SynthesisCode1
Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion ModelsCode1
SinDDM: A Single Image Denoising Diffusion ModelCode1
Refining Generative Process with Discriminator Guidance in Score-based Diffusion ModelsCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Efficient Feature Extraction for High-resolution Video Frame InterpolationCode1
Shifted Diffusion for Text-to-image GenerationCode1
TetraDiffusion: Tetrahedral Diffusion Models for 3D Shape GenerationCode1
The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image GenerationCode1
Exploring Discrete Diffusion Models for Image CaptioningCode1
Diffusion-Based Scene Graph to Image Generation with Masked Contrastive Pre-TrainingCode1
Conffusion: Confidence Intervals for Diffusion ModelsCode1
GLAMI-1M: A Multilingual Image-Text Fashion DatasetCode1
Bipartite Graph Reasoning GANs for Person Pose and Facial Image SynthesisCode1
StyleNAT: Giving Each Head a New PerspectiveCode1
Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion ModelsCode1
Rickrolling the Artist: Injecting Backdoors into Text Encoders for Text-to-Image SynthesisCode1
DC-cycleGAN: Bidirectional CT-to-MR Synthesis from Unpaired DataCode1
Few-shot Image Generation via Adaptation-Aware Kernel ModulationCode1
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?Code1
TPFNet: A Novel Text In-painting Transformer for Text RemovalCode1
Towards the Detection of Diffusion Model DeepfakesCode1
A Survey on Deep Generative 3D-aware Image SynthesisCode1
High-Resolution Image Editing via Multi-Stage Blended DiffusionCode1
Representation Learning with Diffusion ModelsCode1
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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