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 3276–3300 of 6689 papers

TitleStatusHype
DiffuseGAE: Controllable and High-fidelity Image Manipulation from Disentangled Representation—0
DDIM-Driven Coverless Steganography Scheme with Real Key—0
3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models—0
Cross Modality Medical Image Synthesis for Improving Liver Segmentation—0
Generative networks as inverse problems with fractional wavelet scattering networks—0
Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens—0
Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis—0
Adaptive Multiplane Image Generation from a Single Internet Picture—0
LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder—0
Latent Schrodinger Bridge: Prompting Latent Diffusion for Fast Unpaired Image-to-Image Translation—0
Latent Space Disentanglement in Diffusion Transformers Enables Precise Zero-shot Semantic Editing—0
LatexBlend: Scaling Multi-concept Customized Generation with Latent Textual Blending—0
Latent Diffusion Models for Structural Component Design—0
Latent Dirichlet Allocation in Generative Adversarial Networks—0
Generative models for visualising abstract social processes: Guiding streetview image synthesis of StyleGAN2 with indices of deprivation—0
DC-ControlNet: Decoupling Inter- and Intra-Element Conditions in Image Generation with Diffusion Models—0
Bespoke Non-Stationary Solvers for Fast Sampling of Diffusion and Flow Models—0
Latent Drifting in Diffusion Models for Counterfactual Medical Image Synthesis—0
In-Domain GAN Inversion for Faithful Reconstruction and Editability—0
Generative Modelling with High-Order Langevin Dynamics—0
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer—0
Benefiting Deep Latent Variable Models via Learning the Prior and Removing Latent Regularization—0
Inference-time Scaling of Diffusion Models through Classical Search—0
Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution—0
Davidsonian Scene Graph: Improving Reliability in Fine-grained Evaluation for Text-to-Image Generation—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