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 1451–1475 of 6689 papers

TitleStatusHype
Longitudinal Causal Image SynthesisCode0
FreeVS: Generative View Synthesis on Free Driving Trajectory—0
Deep Generative Models for 3D Medical Image Synthesis—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
Scalable Ranked Preference Optimization for Text-to-Image Generation—0
Medical Imaging Complexity and its Effects on GAN PerformanceCode0
Offline Evaluation of Set-Based Text-to-Image GenerationCode0
DiP-GO: A Diffusion Pruner via Few-step Gradient Optimization—0
Hierarchical Clustering for Conditional Diffusion in Image GenerationCode1
Altogether: Image Captioning via Re-aligning Alt-text—0
IdenBAT: Disentangled Representation Learning for Identity-Preserved Brain Age TransformationCode0
Dual-Model Defense: Safeguarding Diffusion Models from Membership Inference Attacks through Disjoint Data Splitting—0
MPDS: A Movie Posters Dataset for Image Generation with Diffusion Model—0
Continuous Speech Synthesis using per-token Latent Diffusion—0
Elucidating the design space of language models for image generationCode1
MedDiff-FM: A Diffusion-based Foundation Model for Versatile Medical Image Applications—0
Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One StepCode2
On the Wasserstein Convergence and Straightness of Rectified FlowCode0
SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuningCode2
Improving Vector-Quantized Image Modeling with Latent Consistency-Matching Diffusion—0
SYNOSIS: Image synthesis pipeline for machine vision in metal surface inspection—0
Truncated Consistency Models—0
Personalized Image Generation with Large Multimodal ModelsCode1
Parallel Backpropagation for Inverse of a Convolution with Application to Normalizing FlowsCode0
HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image GenerationCode2
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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