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 3251–3300 of 6689 papers

TitleStatusHype
Diffscaler: Enhancing the Generative Prowess of Diffusion Transformers—0
Improving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling—0
Improving Diffusion-Based Image Synthesis with Context Prediction—0
Improving Editability in Image Generation with Layer-wise Memory—0
An Analysis on Quantizing Diffusion Transformers—0
Generative Steganography Network—0
Generative Steganography Diffusion—0
Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model—0
Latent-Shift: Latent Diffusion with Temporal Shift for Efficient Text-to-Video Generation—0
Improving GANs with A Dynamic Discriminator—0
Latent Space Consistency for Sparse-View CT Reconstruction—0
Generative Steganographic Flow—0
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion—0
Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration—0
Generative Sensing: Transforming Unreliable Sensor Data for Reliable Recognition—0
Generative Self-training for Cross-domain Unsupervised Tagged-to-Cine MRI Synthesis—0
DDPM based X-ray Image Synthesizer—0
An Analysis of Human Alignment of Latent Diffusion Models—0
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models—0
Generative Probabilistic Image Colorization—0
Generative Portrait Shadow Removal—0
DDMM-Synth: A Denoising Diffusion Model for Cross-modal Medical Image Synthesis with Sparse-view Measurement Embedding—0
Latent Space Bayesian Optimization with Latent Data Augmentation for Enhanced Exploration—0
Generative OpenMax for Multi-Class Open Set Classification—0
Analyzing the Latent Space of GAN through Local Dimension Estimation—0
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