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

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Showing 926–950 of 6689 papers

TitleStatusHype
SAN: Inducing Metrizability of GAN with Discriminative Normalized Linear LayerCode1
Generative Occupancy Fields for 3D Surface-Aware Image SynthesisCode1
On the Multi-modal Vulnerability of Diffusion ModelsCode1
Edge Guided GANs with Multi-Scale Contrastive Learning for Semantic Image SynthesisCode1
EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion ModelsCode1
Benchmarking Robustness of Multimodal Image-Text Models under Distribution ShiftCode1
Edge-preserving noise for diffusion modelsCode1
Diffusion Self-Guidance for Controllable Image GenerationCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
ARINAR: Bi-Level Autoregressive Feature-by-Feature Generative ModelsCode1
Accelerating Diffusion Sampling with Optimized Time StepsCode1
DiffX: Guide Your Layout to Cross-Modal Generative ModelingCode1
DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual ExplanationsCode1
Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in Discriminative ModelsCode1
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel SynthesisCode1
Generator Knows What Discriminator Should Learn in Unconditional GANsCode1
Efficient Content-Based Sparse Attention with Routing TransformersCode1
Accelerating Diffusion Transformer via Increment-Calibrated Caching with Channel-Aware Singular Value DecompositionCode1
Efficient High-Resolution Image-to-Image Translation using Multi-Scale Gradient U-NetCode1
High-Fidelity Neural Human Motion Transfer from Monocular VideoCode1
Efficient Feature Extraction for High-resolution Video Frame InterpolationCode1
Grid: Omni Visual GenerationCode1
Efficient Image-to-Image Diffusion Classifier for Adversarial RobustnessCode1
Diffusion Models With Learned Adaptive NoiseCode1
CACTI: A Framework for Scalable Multi-Task Multi-Scene Visual Imitation LearningCode1
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All datasetsImageNet 256x256CIFAR-10ImageNet 64x64ImageNet 512x512FFHQ 256 x 256CelebA 64x64ImageNet 32x32LSUN Bedroom 256 x 256STL-10LSUN Churches 256 x 256ImageNet 128x128FFHQ 1024 x 1024

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3—Unverified
2ADMFID11.84—Unverified
3BigGAN-deepFID8.1—Unverified
4Polarity-BigGANFID6.82—Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59—Unverified
6MaskGITFID6.18—Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2—Unverified
8CDMFID4.88—Unverified
9ADM-GFID4.59—Unverified
10RINFID4.51—Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2—Unverified
2RESFLOWFID48.29—Unverified
3Residual FlowFID46.37—Unverified
4GLF+perceptual loss (ours)FID44.6—Unverified
5ProdPoly no activation functionsFID40.45—Unverified
6ProdPoly no activation functionsFID36.77—Unverified
7ACGANFID35.47—Unverified
8DenseFlow-74-10FID34.9—Unverified
9NVAE w/ flowFID32.53—Unverified
10QSNGANFID31.97—Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87—Unverified
2GLIDE + CLIPFID30.46—Unverified
3GLIDE + CLS-FREEFID29.22—Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18—Unverified
5PGMGANFID21.73—Unverified
6CLR-GANFID20.27—Unverified
7FMFID14.45—Unverified
8CT (Direct Generation, NFE=1)FID13—Unverified
9CT (Direct Generation, NFE=2)FID11.1—Unverified
10GLIDE +CLSKID7.95—Unverified