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 251275 of 6689 papers

TitleStatusHype
Guiding Diffusion with Deep Geometric Moments: Balancing Fidelity and Variation0
Exploring Sparsity for Parameter Efficient Fine Tuning Using WaveletsCode0
Model alignment using inter-modal bridges0
Is Artificial Intelligence Generated Image Detection a Solved Problem?Code1
FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the EdgeCode1
Measurement Score-Based Diffusion ModelCode0
LoFT: LoRA-fused Training Dataset Generation with Few-shot GuidanceCode0
PSDiffusion: Harmonized Multi-Layer Image Generation via Layout and Appearance Alignment0
DRAGON: A Large-Scale Dataset of Realistic Images Generated by Diffusion Models0
Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion ModelsCode1
CompAlign: Improving Compositional Text-to-Image Generation with a Complex Benchmark and Fine-Grained Feedback0
One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing FrameworkCode1
HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-ResolutionCode0
DDAE++: Enhancing Diffusion Models Towards Unified Generative and Discriminative Learning0
Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges0
Shackled Dancing: A Bit-Locked Diffusion Algorithm for Lossless and Controllable Image Steganography0
End-to-End Vision Tokenizer Tuning0
IMAGE-ALCHEMY: Advancing subject fidelity in personalised text-to-image generation0
Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image SynthesisCode2
EnerVerse-AC: Envisioning Embodied Environments with Action Condition0
BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and DatasetCode5
Don't Forget your Inverse DDIM for Image Editing0
Mini Diffuser: Fast Multi-task Diffusion Policy Training Using Two-level Mini-batchesCode1
An Initial Exploration of Default Images in Text-to-Image Generation0
Skeleton-Guided Diffusion Model for Accurate Foot X-ray Synthesis in Hallux Valgus DiagnosisCode0
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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