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 5101–5125 of 6689 papers

TitleStatusHype
Pose-guided Generative Adversarial Net for Novel View Action Synthesis—0
Pose Guided Human Image Synthesis with Partially Decoupled GAN—0
Pose Guided Image Generation from Misaligned Sources via Residual Flow Based Correction—0
Pose Guided Person Image Generation with Hidden p-Norm Regression—0
A Modular Open Source Framework for Genomic Variant Calling—0
Pose with Style: Detail-Preserving Pose-Guided Image Synthesis with Conditional StyleGAN—0
Position and Orientation-Aware One-Shot Learning for Medical Action Recognition from Signal Data—0
Posterior Mean Matching: Generative Modeling through Online Bayesian Inference—0
Posterior Promoted GAN With Distribution Discriminator for Unsupervised Image Synthesis—0
PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering—0
Post-Training Quantization for Diffusion Transformer via Hierarchical Timestep Grouping—0
Potential Auto-driving Threat: Universal Rain-removal Attack—0
Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy Image Data—0
PQD: Post-training Quantization for Efficient Diffusion Models—0
3D-Aware Facial Landmark Detection via Multi-View Consistent Training on Synthetic Data—0
Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy—0
PreciseCam: Precise Camera Control for Text-to-Image Generation—0
Unlabeled Data Guided Semi-supervised Histopathology Image Segmentation—0
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation—0
Predicated Diffusion: Predicate Logic-Based Attention Guidance for Text-to-Image Diffusion Models—0
Prediction with Action: Visual Policy Learning via Joint Denoising Process—0
Preference Alignment on Diffusion Model: A Comprehensive Survey for Image Generation and Editing—0
Zero-shot spatial layout conditioning for text-to-image diffusion models—0
Preliminary Explorations with GPT-4o(mni) Native Image Generation—0
Zero-Shot Styled Text Image Generation, but Make It Autoregressive—0
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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