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 44764500 of 6689 papers

TitleStatusHype
Self-Guided Diffusion ModelsCode1
AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video AvatarsCode1
Controllable Radiance Fields for Dynamic Face Synthesis0
GENIE: Higher-Order Denoising Diffusion SolversCode1
Style-Guided Inference of Transformer for High-resolution Image Synthesis0
Markup-to-Image Diffusion Models with Scheduled SamplingCode1
Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and GuidanceCode2
f-DM: A Multi-stage Diffusion Model via Progressive Signal Transformation0
SCAM! Transferring humans between images with Semantic Cross Attention ModulationCode1
SiNeRF: Sinusoidal Neural Radiance Fields for Joint Pose Estimation and Scene ReconstructionCode1
CLIP-Diffusion-LM: Apply Diffusion Model on Image CaptioningCode1
What the DAAM: Interpreting Stable Diffusion Using Cross AttentionCode2
Bridging CLIP and StyleGAN through Latent Alignment for Image Editing0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis0
Dual Pyramid Generative Adversarial Networks for Semantic Image SynthesisCode1
Images as Weight Matrices: Sequential Image Generation Through Synaptic Learning RulesCode1
Can Artificial Intelligence Reconstruct Ancient Mosaics?0
Pose Guided Human Image Synthesis with Partially Decoupled GAN0
Visualize Before You Write: Imagination-Guided Open-Ended Text GenerationCode1
GENHOP: An Image Generation Method Based on Successive Subspace Learning0
On Distillation of Guided Diffusion ModelsCode3
Flow Matching for Generative ModelingCode3
LDEdit: Towards Generalized Text Guided Image Manipulation via Latent Diffusion Models0
Imagen Video: High Definition Video Generation with Diffusion Models0
Progressive Text-to-Image Generation0
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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