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 4951–4975 of 6689 papers

TitleStatusHype
Panoptic Segmentation of Mammograms with Text-To-Image Diffusion Model—0
ParaGAN: A Scalable Distributed Training Framework for Generative Adversarial Networks—0
Why Compress What You Can Generate? When GPT-4o Generation Ushers in Image Compression Fields—0
Parallel Multiscale Autoregressive Density Estimation—0
Parallel Optimal Transport GAN—0
Parallel Recurrent Data Augmentation for GAN training with Limited and Diverse Data—0
Unified Editing of Panorama, 3D Scenes, and Videos Through Disentangled Self-Attention Injection—0
Parallel Scheduled Sampling—0
Parallel Sequence Modeling via Generalized Spatial Propagation Network—0
Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity—0
Parametric Implicit Face Representation for Audio-Driven Facial Reenactment—0
Parasite: A Steganography-based Backdoor Attack Framework for Diffusion Models—0
PARASOL: Parametric Style Control for Diffusion Image Synthesis—0
PaReNeRF: Toward Fast Large-scale Dynamic NeRF with Patch-based Reference—0
PAROAttention: Pattern-Aware ReOrdering for Efficient Sparse and Quantized Attention in Visual Generation Models—0
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation—0
PartDiff: Image Super-resolution with Partial Diffusion Models—0
Partially Conditioned Generative Adversarial Networks—0
Partially Conditioned Patch Parallelism for Accelerated Diffusion Model Inference—0
Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics—0
Unified High-binding Watermark for Unconditional Image Generation Models—0
PassFlow: Guessing Passwords with Generative Flows—0
PASTA-GAN++: A Versatile Framework for High-Resolution Unpaired Virtual Try-on—0
Paste, Inpaint and Harmonize via Denoising: Subject-Driven Image Editing with Pre-Trained Diffusion Model—0
Patch Correspondences for Interpreting Pixel-level CNNs—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