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 6451–6475 of 6689 papers

TitleStatusHype
Identity Encoder for Personalized Diffusion—0
Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos—0
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design—0
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling—0
Three-dimensional Bone Image Synthesis with Generative Adversarial Networks—0
IE-Bench: Advancing the Measurement of Text-Driven Image Editing for Human Perception Alignment—0
IFaceUV: Intuitive Motion Facial Image Generation by Identity Preservation via UV map—0
IFAdapter: Instance Feature Control for Grounded Text-to-Image Generation—0
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation—0
Improving GANs with a Feature Cycling Generator—0
IGNOR: Image-guided Neural Object Rendering—0
Illiterate DALLE Learns to Compose—0
Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks—0
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset—0
ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance—0
Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification—0
Illustrious: an Open Advanced Illustration Model—0
VQ4DiT: Efficient Post-Training Vector Quantization for Diffusion Transformers—0
Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation—0
Image2Text2Image: A Novel Framework for Label-Free Evaluation of Image-to-Text Generation with Text-to-Image Diffusion Models—0
Image Aesthetic Reasoning: A New Benchmark for Medical Image Screening with MLLMs—0
Audio Latent Space Cartography—0
Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation—0
Image Augmentations for GAN Training—0
Synthetic dual image generation for reduction of labeling efforts in semantic segmentation of micrographs with a customized metric function—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