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 876–900 of 6689 papers

TitleStatusHype
Disentangled Image Generation Through Structured Noise InjectionCode1
Disentanglement in a GAN for Unconditional Speech SynthesisCode1
Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2Code1
Generative Adversarial NetworksCode1
Can MLLMs Perform Text-to-Image In-Context Learning?Code1
Causal Diffusion Autoencoders: Toward Counterfactual Generation via Diffusion Probabilistic ModelsCode1
Adversarial Audio SynthesisCode1
A Preliminary Study for GPT-4o on Image RestorationCode1
Diffusion Models With Learned Adaptive NoiseCode1
Generative Modeling with Bayesian Sample InferenceCode1
Generative Modeling with Optimal Transport MapsCode1
Adversarial Image Generation by Spatial Transformation in Perceptual ColorspacesCode1
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
CCDM: Continuous Conditional Diffusion Models for Image GenerationCode1
Diffusion Normalizing FlowCode1
Diverse Image Synthesis from Semantic Layouts via Conditional IMLECode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Diverse Image Generation via Self-Conditioned GANsCode1
Diffusion Models for Interferometric Satellite Aperture RadarCode1
Diffusion Models for Constrained DomainsCode1
Diffusion Probabilistic Modeling for Video GenerationCode1
GenCo: Generative Co-training for Generative Adversarial Networks with Limited DataCode1
General Image-to-Image Translation with One-Shot Image GuidanceCode1
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic SegmenterCode1
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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