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 1901–1925 of 6689 papers

TitleStatusHype
Network Bending of Diffusion Models for Audio-Visual GenerationCode1
Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value OptimizationCode1
ENAT: Rethinking Spatial-temporal Interactions in Token-based Image SynthesisCode1
NeRF-GAN Distillation for Efficient 3D-Aware Generation with ConvolutionsCode1
ClimateGAN: Raising Climate Change Awareness by Generating Images of FloodsCode1
Meta ControlNet: Enhancing Task Adaptation via Meta LearningCode1
Accelerating Guided Diffusion Sampling with Splitting Numerical MethodsCode1
Meta-Learning via Classifier(-free) Diffusion GuidanceCode1
Elucidating the solution space of extended reverse-time SDE for diffusion modelsCode1
METR: Image Watermarking with Large Number of Unique MessagesCode1
A Cheaper and Better Diffusion Language Model with Soft-Masked NoiseCode1
Nested Diffusion Processes for Anytime Image GenerationCode1
CLIP-Diffusion-LM: Apply Diffusion Model on Image CaptioningCode1
Network-to-Network Translation with Conditional Invertible Neural NetworksCode1
Conditional Diffusion Models for Weakly Supervised Medical Image SegmentationCode1
Nested Hierarchical Transformer: Towards Accurate, Data-Efficient and Interpretable Visual UnderstandingCode1
Elucidating The Design Space of Classifier-Guided Diffusion GenerationCode1
CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIPCode1
Elucidating the design space of language models for image generationCode1
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image GenerationCode1
A Characteristic Function Approach to Deep Implicit Generative ModelingCode1
A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and RestorationCode1
Elucidating the Exposure Bias in Diffusion ModelsCode1
End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure ImagesCode1
NeoBabel: A Multilingual Open Tower for Visual GenerationCode1
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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