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 2151–2175 of 6689 papers

TitleStatusHype
ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining—0
Dimba: Transformer-Mamba Diffusion Models—0
CE-VAE: Capsule Enhanced Variational AutoEncoder for Underwater Image EnhancementCode1
Differentially Private Fine-Tuning of Diffusion Models—0
Anomaly Anything: Promptable Unseen Visual Anomaly Generation—0
AutoStudio: Crafting Consistent Subjects in Multi-turn Interactive Image GenerationCode3
Layout Agnostic Scene Text Image Synthesis with Diffusion Models—0
Text-guided Controllable Mesh Refinement for Interactive 3D Modeling—0
Long and Short Guidance in Score identity Distillation for One-Step Text-to-Image GenerationCode2
ParallelEdits: Efficient Multi-object Image Editing—0
Visual Car Brand Classification by Implementing a Synthetic Image Dataset Creation Pipeline—0
fruit-SALAD: A Style Aligned Artwork Dataset to reveal similarity perception in image embeddingsCode0
Δ-DiT: A Training-Free Acceleration Method Tailored for Diffusion TransformersCode0
Invisible Backdoor Attacks on Diffusion ModelsCode1
Diffusion Features to Bridge Domain Gap for Semantic SegmentationCode1
Deciphering Oracle Bone Language with Diffusion ModelsCode3
Diffusion-based Image Generation for In-distribution Data Augmentation in Surface Defect DetectionCode1
Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion SamplingCode0
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based CustomizationCode1
Diffusion Models Are Innate One-Step GeneratorsCode1
Cyclic image generation using chaotic dynamicsCode0
Amortizing intractable inference in diffusion models for vision, language, and controlCode1
Hybrid Fourier Score Distillation for Efficient One Image to 3D Object GenerationCode2
You Only Scan Once: Efficient Multi-dimension Sequential Modeling with LightNet—0
Information Theoretic Text-to-Image Alignment—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