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 2751–2775 of 6689 papers

TitleStatusHype
BodyGAN: General-Purpose Controllable Neural Human Body Generation—0
Shackled Dancing: A Bit-Locked Diffusion Algorithm for Lossless and Controllable Image Steganography—0
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis—0
DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis—0
Depth Structure Preserving Scene Image Generation—0
Blur, Noise, and Compression Robust Generative Adversarial Networks—0
Depth-SIMS: Semi-Parametric Image and Depth Synthesis—0
DepthFake: a depth-based strategy for detecting Deepfake videos—0
Anime Style Space Exploration Using Metric Learning and Generative Adversarial Networks—0
Hyperspectral Image Generation with Unmixing Guided Diffusion Model—0
DepthART: Monocular Depth Estimation as Autoregressive Refinement Task—0
Dependability Evaluation of Stable Diffusion with Soft Errors on the Model Parameters—0
Unpriortized Autoencoder For Image Generation—0
Block-wise LoRA: Revisiting Fine-grained LoRA for Effective Personalization and Stylization in Text-to-Image Generation—0
DensePANet: An improved generative adversarial network for photoacoustic tomography image reconstruction from sparse data—0
Block and Detail: Scaffolding Sketch-to-Image Generation—0
AniMer: Animal Pose and Shape Estimation Using Family Aware Transformer—0
I2AM: Interpreting Image-to-Image Latent Diffusion Models via Attribution Maps—0
Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction—0
Denoising with a Joint-Embedding Predictive Architecture—0
BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes—0
Denoising Task Difficulty-based Curriculum for Training Diffusion Models—0
BlobCtrl: A Unified and Flexible Framework for Element-level Image Generation and Editing—0
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era—0
Denoising Diffusion Probabilistic Models for Image Inpainting of Cell Distributions in the Human Brain—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