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 2551–2575 of 6689 papers

TitleStatusHype
Diffusion Models Need Visual Priors for Image Generation—0
Anywhere: A Multi-Agent Framework for User-Guided, Reliable, and Diverse Foreground-Conditioned Image Generation—0
Diffusion Models Meet Remote Sensing: Principles, Methods, and Perspectives—0
Diffusion Models in NLP: A Survey—0
CanvasGAN: A simple baseline for text to image generation by incrementally patching a canvas—0
Adversarial Code Learning for Image Generation—0
Diffusion Models Generate Images Like Painters: an Analytical Theory of Outline First, Details Later—0
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
Can segmentation models be trained with fully synthetically generated data?—0
Temporal Evolution of Knee Osteoarthritis: A Diffusion-based Morphing Model for X-ray Medical Image Synthesis—0
Guide3D: Create 3D Avatars from Text and Image Guidance—0
GuidedStyle: Attribute Knowledge Guided Style Manipulation for Semantic Face Editing—0
Diffusion models for Gaussian distributions: Exact solutions and Wasserstein errors—0
Diffusion Models Enable Zero-Shot Pose Estimation for Lower-Limb Prosthetic Users—0
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models—0
Diffusion Models as Data Mining Tools—0
Can Generative Geospatial Diffusion Models Excel as Discriminative Geospatial Foundation Models?—0
Can Generative AI Replace Immunofluorescent Staining Processes? A Comparison Study of Synthetically Generated CellPainting Images from Brightfield—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
Can CLIP Count Stars? An Empirical Study on Quantity Bias in CLIP—0
Diffusion Lens: Interpreting Text Encoders in Text-to-Image Pipelines—0
Can Artificial Intelligence Reconstruct Ancient Mosaics?—0
AnySynth: Harnessing the Power of Image Synthetic Data Generation for Generalized Vision-Language Tasks—0
Diffusion Instruction Tuning—0
Causal Adversarial Network for Learning Conditional and Interventional Distributions—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