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 4001–4025 of 6689 papers

TitleStatusHype
Person image generation with semantic attention network for person re-identification—0
Anatomy and Physiology of Artificial Intelligence in PET Imaging—0
UniFL: Improve Latent Diffusion Model via Unified Feedback Learning—0
Zero-Shot Object Segmentation through Concept Distillation from Generative Image Foundation Models—0
An Analysis on Quantizing Diffusion Transformers—0
An Analysis of Human Alignment of Latent Diffusion Models—0
PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics—0
Photographic Image Synthesis with Cascaded Refinement Networks—0
Analyzing the Latent Space of GAN through Local Dimension Estimation—0
Photorealistic Image Synthesis for Object Instance Detection—0
Photo-realistic Neural Domain Randomization—0
Unifying conditional and unconditional semantic image synthesis with OCO-GAN—0
PhyBench: A Physical Commonsense Benchmark for Evaluating Text-to-Image Models—0
Physically-Based Face Rendering for NIR-VIS Face Recognition—0
Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models—0
Physics-Inspired Generative Models in Medical Imaging: A Review—0
Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations—0
PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion—0
Analyzing CLIP's Performance Limitations in Multi-Object Scenarios: A Controlled High-Resolution Study—0
Picture that Sketch: Photorealistic Image Generation from Abstract Sketches—0
PIDiff: Image Customization for Personalized Identities with Diffusion Models—0
Analyzing and Improving Model Collapse in Rectified Flow Models—0
3D-Aware Indoor Scene Synthesis with Depth Priors—0
Pipeline Enabling Zero-shot Classification for Bangla Handwritten Grapheme—0
Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes—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