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 5351–5375 of 6689 papers

TitleStatusHype
Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later—0
Diffusion Models in NLP: A Survey—0
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives—0
Diffusion Models Need Visual Priors for Image Generation—0
Diffusion Models with Double Guidance: Generate with aggregated datasets—0
Surgical Text-to-Image Generation—0
Diffusion Models Without Attention—0
Brain Image Synthesis with Unsupervised Multivariate Canonical CSC_4Net—0
Diffusion Model with Clustering-based Conditioning for Food Image Generation—0
Diffusion Motion: Generate Text-Guided 3D Human Motion by Diffusion Model—0
SurGrID: Controllable Surgical Simulation via Scene Graph to Image Diffusion—0
Diffusion on the Probability Simplex—0
CT Reconstruction using Diffusion Posterior Sampling conditioned on a Nonlinear Measurement Model—0
Diffusion Prism: Enhancing Diversity and Morphology Consistency in Mask-to-Image Diffusion—0
Diffusion Probabilistic Model Made Slim—0
BrainDreamer: Reasoning-Coherent and Controllable Image Generation from EEG Brain Signals via Language Guidance—0
Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models—0
BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?—0
Boundary Attention Constrained Zero-Shot Layout-To-Image Generation—0
Diffusion Schrödinger Bridge Models for High-Quality MR-to-CT Synthesis for Head and Neck Proton Treatment Planning—0
Diffusion-SDF: Conditional Generative Modeling of Signed Distance Functions—0
Diffusion Self-Distillation for Zero-Shot Customized Image Generation—0
Surrogate Modeling of Car Drag Coefficient with Depth and Normal Renderings—0
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models—0
Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection—0
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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