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 1951–1975 of 6689 papers

TitleStatusHype
End-to-End Diffusion Latent Optimization Improves Classifier GuidanceCode1
End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure ImagesCode1
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion ModelsCode1
Norm-guided latent space exploration for text-to-image generationCode1
A Survey on Deep Generative 3D-aware Image SynthesisCode1
Conceptrol: Concept Control of Zero-shot Personalized Image GenerationCode1
ENAT: Rethinking Spatial-temporal Interactions in Token-based Image SynthesisCode1
Training-Free Watermarking for Autoregressive Image GenerationCode1
Artistic Glyph Image Synthesis via One-Stage Few-Shot LearningCode1
Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar ReconstructionCode1
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image GenerationCode1
Magnitude Invariant Parametrizations Improve Hypernetwork LearningCode1
Not All Diffusion Model Activations Have Been Evaluated as Discriminative FeaturesCode1
ConceptPrune: Concept Editing in Diffusion Models via Skilled Neuron PruningCode1
Multi-marginal Wasserstein GANCode1
E2NeRF: Event Enhanced Neural Radiance Fields from Blurry ImagesCode1
DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion ModelCode1
Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value OptimizationCode1
Multimodal LLM-Guided Semantic Correction in Text-to-Image DiffusionCode1
Enhanced Balancing GAN: Minority-class Image GenerationCode1
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion modelsCode1
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood EstimationCode1
Elucidating the design space of language models for image generationCode1
Elucidating the Exposure Bias in Diffusion ModelsCode1
Concept Conductor: Orchestrating Multiple Personalized Concepts in Text-to-Image SynthesisCode1
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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