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

Showing 101125 of 6689 papers

TitleStatusHype
MAMBO: High-Resolution Generative Approach for Mammography Images0
Autoregressive Semantic Visual Reconstruction Helps VLMs Understand BetterCode2
Re-Thinking the Automatic Evaluation of Image-Text Alignment in Text-to-Image Models0
SakugaFlow: A Stagewise Illustration Framework Emulating the Human Drawing Process and Providing Interactive Tutoring for Novice Drawing Skills0
SkipVAR: Accelerating Visual Autoregressive Modeling via Adaptive Frequency-Aware SkippingCode0
APTOS-2024 challenge report: Generation of synthetic 3D OCT images from fundus photographs0
Diffusion Counterfactual Generation with Semantic AbductionCode0
VIVAT: Virtuous Improving VAE Training through Artifact Mitigation0
Diffuse Everything: Multimodal Diffusion Models on Arbitrary State SpacesCode1
MADFormer: Mixed Autoregressive and Diffusion Transformers for Continuous Image Generation0
Explore the vulnerability of black-box models via diffusion models0
Highly Compressed Tokenizer Can Generate Without TrainingCode3
A Comprehensive Study of Decoder-Only LLMs for Text-to-Image Generation0
OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image GenerationCode2
AR-RAG: Autoregressive Retrieval Augmentation for Image GenerationCode0
Training-Free Identity Preservation in Stylized Image Generation Using Diffusion Models0
ResPF: Residual Poisson Flow for Efficient and Physically Consistent Sparse-View CT Reconstruction0
Noise Consistency Regularization for Improved Subject-Driven Image Synthesis0
Peer-Ranked Precision: Creating a Foundational Dataset for Fine-Tuning Vision Models from DataSeeds' Annotated ImageryCode0
Aligning Latent Spaces with Flow Priors0
PixCell: A generative foundation model for digital histopathology images0
AliTok: Towards Sequence Modeling Alignment between Tokenizer and Autoregressive ModelCode2
ContentV: Efficient Training of Video Generation Models with Limited Compute0
Invisible Backdoor Triggers in Image Editing Model via Deep WatermarkingCode0
Improving AI-generated music with user-guided training0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified