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 3651–3675 of 6689 papers

TitleStatusHype
StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning—0
FontStudio: Shape-Adaptive Diffusion Model for Coherent and Consistent Font Effect Generation—0
What If We Recaption Billions of Web Images with LLaMA-3?—0
DiTFastAttn: Attention Compression for Diffusion Transformer Models—0
Words Worth a Thousand Pictures: Measuring and Understanding Perceptual Variability in Text-to-Image Generation—0
WMAdapter: Adding WaterMark Control to Latent Diffusion Models—0
Understanding and Mitigating Compositional Issues in Text-to-Image Generative ModelsCode0
Diffusion Soup: Model Merging for Text-to-Image Diffusion Models—0
Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance—0
Beware of Aliases -- Signal Preservation is Crucial for Robust Image Restoration—0
Understanding Visual Concepts Across ModelsCode0
Progress Towards Decoding Visual Imagery via fNIRS—0
Commonsense-T2I Challenge: Can Text-to-Image Generation Models Understand Commonsense?—0
Eye-for-an-eye: Appearance Transfer with Semantic Correspondence in Diffusion Models—0
Instant 3D Human Avatar Generation using Image Diffusion Models—0
The Effect of Training Dataset Size on Discriminative and Diffusion-Based Speech Enhancement Systems—0
Can Prompt Modifiers Control Bias? A Comparative Analysis of Text-to-Image Generative Models—0
TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models—0
OmniControlNet: Dual-stage Integration for Conditional Image Generation—0
Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language ModelsCode0
Rapid Review of Generative AI in Smart Medical Applications—0
GANetic Loss for Generative Adversarial Networks with a Focus on Medical ApplicationsCode0
AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation—0
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at InitializationCode0
PQPP: A Joint Benchmark for Text-to-Image Prompt and Query Performance PredictionCode0
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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