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 4201–4225 of 6689 papers

TitleStatusHype
High-Fidelity Diffusion-based Image Editing—0
Make-A-Character: High Quality Text-to-3D Character Generation within Minutes—0
Semantic Draw Engineering for Text-to-Image Creation—0
Prompt-Propose-Verify: A Reliable Hand-Object-Interaction Data Generation Framework using Foundational Models—0
Learning from Mistakes: Iterative Prompt Relabeling for Text-to-Image Diffusion Model TrainingCode0
Generative AI and the History of Architecture—0
Asymmetric Bias in Text-to-Image Generation with Adversarial AttacksCode0
Synthesizing Environment-Specific People in Photographs—0
Emage: Non-Autoregressive Text-to-Image Generation—0
DreamTuner: Single Image is Enough for Subject-Driven Generation—0
Fine-grained Forecasting Models Via Gaussian Process Blurring EffectCode0
Diff-Oracle: Deciphering Oracle Bone Scripts with Controllable Diffusion Model—0
All but One: Surgical Concept Erasing with Model Preservation in Text-to-Image Diffusion Models—0
Conditional Image Generation with Pretrained Generative Model—0
Unlocking Pre-trained Image Backbones for Semantic Image Synthesis—0
A self-attention-based differentially private tabular GAN with high data utility—0
Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation—0
Prompting Hard or Hardly Prompting: Prompt Inversion for Text-to-Image Diffusion Models—0
Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics—0
MagicScroll: Nontypical Aspect-Ratio Image Generation for Visual Storytelling via Multi-Layered Semantic-Aware Denoising—0
The Right Losses for the Right Gains: Improving the Semantic Consistency of Deep Text-to-Image Generation with Distribution-Sensitive Losses—0
DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated ContentCode0
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations—0
Tell Me What You See: Text-Guided Real-World Image Denoising—0
Towards the Unification of Generative and Discriminative Visual Foundation Model: A Survey—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