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 1901–1925 of 6689 papers

TitleStatusHype
Causal Inference via Style Transfer for Out-of-distribution GeneralisationCode1
FunkNN: Neural Interpolation for Functional GenerationCode1
Two-Stage Peer-Regularized Feature Recombination for Arbitrary Image Style TransferCode1
FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image GenerationCode1
Freestyle Layout-to-Image SynthesisCode1
Conditional Image Synthesis With Auxiliary Classifier GANsCode1
Conditional Image Generation with Score-Based Diffusion ModelsCode1
T2ISafety: Benchmark for Assessing Fairness, Toxicity, and Privacy in Image GenerationCode1
Tackling the Generative Learning Trilemma with Denoising Diffusion GANsCode1
Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt RewritingCode1
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram DigitizationCode1
Frame Interpolation with Consecutive Brownian Bridge DiffusionCode1
FreCaS: Efficient Higher-Resolution Image Generation via Frequency-aware Cascaded SamplingCode1
CLIP-Forge: Towards Zero-Shot Text-to-Shape GenerationCode1
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANsCode1
Dressing in Order: Recurrent Person Image Generation for Pose Transfer, Virtual Try-on and Outfit EditingCode1
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIPCode1
FuseDream: Training-Free Text-to-Image Generation with Improved CLIP+GAN Space OptimizationCode1
General Image-to-Image Translation with One-Shot Image GuidanceCode1
GENIE: Higher-Order Denoising Diffusion SolversCode1
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion ModelsCode1
Conditional Image Generation and Manipulation for User-Specified ContentCode1
ForkGAN: Seeing into the Rainy NightCode1
Foreground-Background Separation through Concept Distillation from Generative Image Foundation ModelsCode1
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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