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 4451–4475 of 6689 papers

TitleStatusHype
MaGRITTe: Manipulative and Generative 3D Realization from Image, Topview and Text—0
Training-free Subject-Enhanced Attention Guidance for Compositional Text-to-image Generation—0
MAISY: Motion-Aware Image SYnthesis for Medical Image Motion Correction—0
Make-A-Character: High Quality Text-to-3D Character Generation within Minutes—0
Make Autoregressive Great Again: Diffusion-Free Graph Generation with Next-Scale Prediction—0
Make-A-Volume: Leveraging Latent Diffusion Models for Cross-Modality 3D Brain MRI Synthesis—0
Art3D: Training-Free 3D Generation from Flat-Colored Illustration—0
Make Me Happier: Evoking Emotions Through Image Diffusion Models—0
Make-Your-Video: Customized Video Generation Using Textual and Structural Guidance—0
A4A: Adapter for Adapter Transfer via All-for-All Mapping for Cross-Architecture Models—0
MAMBO: High-Resolution Generative Approach for Mammography Images—0
Unbiased Image Synthesis via Manifold Guidance in Diffusion Models—0
Manifold Preserving Guided Diffusion—0
Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets—0
Training Object Detectors on Synthetic Images Containing Reflecting Materials—0
ManiTrend: Bridging Future Generation and Action Prediction with 3D Flow for Robotic Manipulation—0
Many-to-many Image Generation with Auto-regressive Diffusion Models—0
Training \& Quality Assessment of an Optical Character Recognition Model for Northern Haida—0
Marginal Contrastive Correspondence for Guided Image Generation—0
What does CLIP know about a red circle? Visual prompt engineering for VLMs—0
Marmot: Multi-Agent Reasoning for Multi-Object Self-Correcting in Improving Image-Text Alignment—0
ArrowGAN : Learning to Generate Videos by Learning Arrow of Time—0
Mask2Lesion: Mask-Constrained Adversarial Skin Lesion Image Synthesis—0
A Robust Pose Transformational GAN for Pose Guided Person Image Synthesis—0
Trajectory-aware Principal Manifold Framework for Data Augmentation and Image Generation—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