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 901925 of 6689 papers

TitleStatusHype
Censored Sampling of Diffusion Models Using 3 Minutes of Human FeedbackCode1
CFG++: Manifold-constrained Classifier Free Guidance for Diffusion ModelsCode1
Diffusion-NPO: Negative Preference Optimization for Better Preference Aligned Generation of Diffusion ModelsCode1
Discriminator-Cooperated Feature Map Distillation for GAN CompressionCode1
Generative Occupancy Fields for 3D Surface-Aware Image SynthesisCode1
Can MLLMs Perform Text-to-Image In-Context Learning?Code1
DiMSUM: Diffusion Mamba -- A Scalable and Unified Spatial-Frequency Method for Image GenerationCode1
Adversarial Audio SynthesisCode1
Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative ModelsCode1
Edge Guided GANs with Contrastive Learning for Semantic Image SynthesisCode1
A Complete Recipe for Diffusion Generative ModelsCode1
DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Diffusion Self-Guidance for Controllable Image GenerationCode1
Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative ModelsCode1
DPImageBench: A Unified Benchmark for Differentially Private Image SynthesisCode1
Diffusion Models With Learned Adaptive NoiseCode1
Characteristic Guidance: Non-linear Correction for Diffusion Model at Large Guidance ScaleCode1
Dual Pyramid Generative Adversarial Networks for Semantic Image SynthesisCode1
Are Diffusion Models Vision-And-Language Reasoners?Code1
ChatHouseDiffusion: Prompt-Guided Generation and Editing of Floor PlansCode1
Diffusion Normalizing FlowCode1
Are Diffusion Models Vulnerable to Membership Inference Attacks?Code1
Diffusion Probabilistic Modeling for Video GenerationCode1
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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