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 801–825 of 6689 papers

TitleStatusHype
G2PDiffusion: Genotype-to-Phenotype Prediction with Diffusion Models—0
Bridging Scales in Map Generation: A scale-aware cascaded generative mapping framework for seamless and consistent multi-scale cartographic representation—0
DICE: Distilling Classifier-Free Guidance into Text Embeddings—0
FairT2I: Mitigating Social Bias in Text-to-Image Generation via Large Language Model-Assisted Detection and Attribute Rebalancing—0
Decoder-Only LLMs are Better Controllers for Diffusion Models—0
Generative Adversarial Networks Bridging Art and Machine Intelligence—0
Augmented Conditioning Is Enough For Effective Training Image Generation—0
Towards Fair and Robust Face Parsing for Generative AI: A Multi-Objective Approach—0
HOG-Diff: Higher-Order Guided Diffusion for Graph GenerationCode1
Masked Autoencoders Are Effective Tokenizers for Diffusion Models—0
TruePose: Human-Parsing-guided Attention Diffusion for Full-ID Preserving Pose Transfer—0
On Fairness of Unified Multimodal Large Language Model for Image Generation—0
CTR-Driven Advertising Image Generation with Multimodal Large Language ModelsCode2
Poisson Flow Joint Model for Multiphase contrast-enhanced CT—0
CoRPA: Adversarial Image Generation for Chest X-rays Using Concept Vector Perturbations and Generative Models—0
Diffusion Instruction Tuning—0
Towards Consistent and Controllable Image Synthesis for Face Editing—0
When are Diffusion Priors Helpful in Sparse Reconstruction? A Study with Sparse-view CT—0
AAD-DCE: An Aggregated Multimodal Attention Mechanism for Early and Late Dynamic Contrast Enhanced Prostate MRI SynthesisCode0
Layer Separation: Adjustable Joint Space Width Images Synthesis in Conventional Radiography—0
On the Guidance of Flow MatchingCode2
Unpaired Deblurring via Decoupled Diffusion Model—0
Texture Image Synthesis Using Spatial GAN Based on Vision Transformers—0
Compressed Image Generation with Denoising Diffusion Codebook ModelsCode2
RealRAG: Retrieval-augmented Realistic Image Generation via Self-reflective Contrastive Learning—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