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 10511075 of 6689 papers

TitleStatusHype
How to train your neural ODE: the world of Jacobian and kinetic regularizationCode1
Diverse Image Synthesis from Semantic Layouts via Conditional IMLECode1
High-Fidelity Synthesis with Disentangled RepresentationCode1
High-Frequency Space Diffusion Models for Accelerated MRICode1
Diverse Image Generation via Self-Conditioned GANsCode1
Diverse Semantic Image Synthesis via Probability Distribution ModelingCode1
High-fidelity Person-centric Subject-to-Image SynthesisCode1
DiTAS: Quantizing Diffusion Transformers via Enhanced Activation SmoothingCode1
DIVA: A Dirichlet Process Mixtures Based Incremental Deep Clustering Algorithm via Variational Auto-EncoderCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus DecodingCode1
Distribution-Aware Data Expansion with Diffusion ModelsCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Hierarchical Transformers Are More Efficient Language ModelsCode1
Dissolving Is Amplifying: Towards Fine-Grained Anomaly DetectionCode1
Dissecting and Mitigating Diffusion Bias via Mechanistic InterpretabilityCode1
High Fidelity Image Synthesis With Deep VAEs In Latent SpaceCode1
Hierarchical Clustering for Conditional Diffusion in Image GenerationCode1
Hierarchical Amortized Training for Memory-efficient High Resolution 3D GANCode1
HieraFashDiff: Hierarchical Fashion Design with Multi-stage Diffusion ModelsCode1
Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based CustomizationCode1
BootPIG: Bootstrapping Zero-shot Personalized Image Generation Capabilities in Pretrained Diffusion ModelsCode1
Hierarchical Masked Autoregressive Models with Low-Resolution Token PivotsCode1
High-Fidelity Neural Human Motion Transfer from Monocular VideoCode1
High-Resolution Image Editing via Multi-Stage Blended DiffusionCode1
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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