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 726750 of 6689 papers

TitleStatusHype
Flow-Guided Diffusion for Video InpaintingCode2
FlowAR: Scale-wise Autoregressive Image Generation Meets Flow MatchingCode2
Flow Matching for Medical Image Synthesis: Bridging the Gap Between Speed and QualityCode2
Hybrid Fourier Score Distillation for Efficient One Image to 3D Object GenerationCode2
GAN Prior Embedded Network for Blind Face Restoration in the WildCode2
Direct Inversion: Boosting Diffusion-based Editing with 3 Lines of CodeCode2
Direct Discriminative Optimization: Your Likelihood-Based Visual Generative Model is Secretly a GAN DiscriminatorCode2
DiSA: Diffusion Step Annealing in Autoregressive Image GenerationCode2
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete LatentsCode2
Reformer: The Efficient TransformerCode2
Geometry-Complete Diffusion for 3D Molecule Generation and OptimizationCode2
Seedream 2.0: A Native Chinese-English Bilingual Image Generation Foundation ModelCode2
FLAME Diffuser: Wildfire Image Synthesis using Mask Guided DiffusionCode1
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion ModelsCode1
First Creating Backgrounds Then Rendering Texts: A New Paradigm for Visual Text BlendingCode1
Finite Scalar Quantization: VQ-VAE Made SimpleCode1
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision TransformersCode1
A Simple and Effective Baseline for Attentional Generative Adversarial NetworksCode1
FlexDiT: Dynamic Token Density Control for Diffusion TransformerCode1
Finetuning CLIP to Reason about Pairwise DifferencesCode1
A Shared Representation for Photorealistic Driving SimulatorsCode1
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image SynthesisCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 ClassesCode1
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of ExpertsCode1
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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