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 3926–3950 of 6689 papers

TitleStatusHype
UniDiff: Advancing Vision-Language Models with Generative and Discriminative Learning—0
PAID: A Framework of Product-Centric Advertising Image Design—0
PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud—0
Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin—0
Unified Autoregressive Visual Generation and Understanding with Continuous Tokens—0
An Ensemble Approach for Brain Tumor Segmentation and Synthesis—0
Painter: Teaching Auto-regressive Language Models to Draw Sketches—0
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask—0
Paint it Black: Generating paintings from text descriptions—0
Pal-GAN: Palette-conditioned Generative Adversarial Networks—0
PanGu-Draw: Advancing Resource-Efficient Text-to-Image Synthesis with Time-Decoupled Training and Reusable Coop-Diffusion—0
3DGH: 3D Head Generation with Composable Hair and Face—0
Panoptic-based Image Synthesis—0
Panoptic-aware Image-to-Image Translation—0
Panoptic Diffusion Models: co-generation of images and segmentation maps—0
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
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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