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 1001–1025 of 6689 papers

TitleStatusHype
One Image is Worth a Thousand Words: A Usability Preservable Text-Image Collaborative Erasing FrameworkCode1
Image Understanding Makes for A Good Tokenizer for Image GenerationCode1
BS-Diff: Effective Bone Suppression Using Conditional Diffusion Models from Chest X-Ray ImagesCode1
ImagineFSL: Self-Supervised Pretraining Matters on Imagined Base Set for VLM-based Few-shot LearningCode1
Adaptive Feature Interpolation for Low-Shot Image GenerationCode1
Implicit Rank-Minimizing AutoencoderCode1
Brush Your Text: Synthesize Any Scene Text on Images via Diffusion ModelCode1
AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image DetectorsCode1
A Simple and Robust Framework for Cross-Modality Medical Image Segmentation applied to Vision TransformersCode1
ColorSwap: A Color and Word Order Dataset for Multimodal EvaluationCode1
Frame Interpolation with Consecutive Brownian Bridge DiffusionCode1
Improved Techniques for Training Consistency ModelsCode1
Consistency Models as a Rich and Efficient Policy Class for Reinforcement LearningCode1
Improved Techniques for Training Single-Image GANsCode1
A Simple Early Exiting Framework for Accelerated Sampling in Diffusion ModelsCode1
Controlling Geometric Abstraction and Texture for Artistic ImagesCode1
Combining Markov Random Fields and Convolutional Neural Networks for Image SynthesisCode1
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
Improving GAN Training with Probability Ratio Clipping and Sample ReweightingCode1
Improving Generation and Evaluation of Visual Stories via Semantic ConsistencyCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Bridging the Gap Between f-GANs and Wasserstein GANsCode1
Flow Contrastive Estimation of Energy-Based ModelsCode1
FlexDiT: Dynamic Token Density Control for Diffusion TransformerCode1
FLAME Diffuser: Wildfire Image Synthesis using Mask Guided DiffusionCode1
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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