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 2151–2175 of 6689 papers

TitleStatusHype
AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection—0
Efficiency Meets Fidelity: A Novel Quantization Framework for Stable Diffusion—0
Effect of Instance Normalization on Fine-Grained Control for Sketch-Based Face Image Generation—0
Collaging Class-specific GANs for Semantic Image Synthesis—0
Effective Shortcut Technique for GAN—0
Accelerating Score-based Generative Models for High-Resolution Image Synthesis—0
FRDiff : Feature Reuse for Universal Training-free Acceleration of Diffusion Models—0
EEG to fMRI Synthesis: Is Deep Learning a candidate?—0
Collage Diffusion—0
EEG Signal Dimensionality Reduction and Classification using Tensor Decomposition and Deep Convolutional Neural Networks—0
Learning Patterns in Sample Distributions for Monte Carlo Variance Reduction—0
ED-NeRF: Efficient Text-Guided Editing of 3D Scene with Latent Space NeRF—0
ASGDiffusion: Parallel High-Resolution Generation with Asynchronous Structure Guidance—0
EditID: Training-Free Editable ID Customization for Text-to-Image Generation—0
FRAP: Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting—0
FREA-Unet: Frequency-aware U-net for Modality Transfer—0
EDiT: Efficient Diffusion Transformers with Linear Compressed Attention—0
EditAR: Unified Conditional Generation with Autoregressive Models—0
Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation—0
Edify Image: High-Quality Image Generation with Pixel Space Laplacian Diffusion Models—0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis—0
A self-attention-based differentially private tabular GAN with high data utility—0
EdgeRelight360: Text-Conditioned 360-Degree HDR Image Generation for Real-Time On-Device Video Portrait Relighting—0
EDGE-Rec: Efficient and Data-Guided Edge Diffusion For Recommender Systems Graphs—0
Collaborative Control for Geometry-Conditioned PBR 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