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 1851–1875 of 6689 papers

TitleStatusHype
Elucidating the Exposure Bias in Diffusion ModelsCode1
Elucidating the solution space of extended reverse-time SDE for diffusion modelsCode1
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image SynthesisCode1
DMM: Building a Versatile Image Generation Model via Distillation-Based Model MergingCode1
Conditional Image Synthesis With Auxiliary Classifier GANsCode1
Conditional Image Generation with Score-Based Diffusion ModelsCode1
On the Multi-modal Vulnerability of Diffusion ModelsCode1
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
Mask Conditional Synthetic Satellite ImageryCode1
MADE: Masked Autoencoder for Distribution EstimationCode1
Elucidating The Design Space of Classifier-Guided Diffusion GenerationCode1
ECG-Image-Kit: A Synthetic Image Generation Toolbox to Facilitate Deep Learning-Based Electrocardiogram DigitizationCode1
Benchmarking Robustness of Multimodal Image-Text Models under Distribution ShiftCode1
Domain-Adaptive 3D Medical Image Synthesis: An Efficient Unsupervised ApproachCode1
MAGVLT: Masked Generative Vision-and-Language TransformerCode1
Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value OptimizationCode1
Multi-Spectral Image Synthesis for Crop/Weed Segmentation in Precision FarmingCode1
Make-A-Video: Text-to-Video Generation without Text-Video DataCode1
Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 BenchmarkCode1
CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image SynthesisCode1
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image SynthesisCode1
Network-to-Network Translation with Conditional Invertible Neural NetworksCode1
Accelerating Diffusion Sampling with Optimized Time StepsCode1
Manipulating Embeddings of Stable Diffusion PromptsCode1
Not All Diffusion Model Activations Have Been Evaluated as Discriminative FeaturesCode1
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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