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 3876–3900 of 6689 papers

TitleStatusHype
On the Relation Between Linear Diffusion and Power Iteration—0
Understanding (Un)Intended Memorization in Text-to-Image Generative Models—0
On the Role of Receptive Field in Unsupervised Sim-to-Real Image Translation—0
On the Scalability of Diffusion-based Text-to-Image Generation—0
On the Scalability of GNNs for Molecular Graphs—0
On the Semantic Latent Space of Diffusion-Based Text-to-Speech Models—0
On the Sins of Image Synthesis Loss for Self-supervised Depth Estimation—0
On the Solution of Linearized Inverse Scattering Problems in Near-Field Microwave Imaging by Operator Inversion and Matched Filtering—0
On the Study of Sample Complexity for Polynomial Neural Networks—0
AnimeDL-2M: Million-Scale AI-Generated Anime Image Detection and Localization in Diffusion Era—0
On Using Backpropagation for Speech Texture Generation and Voice Conversion—0
An Image-like Diffusion Method for Human-Object Interaction Detection—0
OPa-Ma: Text Guided Mamba for 360-degree Image Out-painting—0
Open Domain Dialogue Generation with Latent Images—0
Opening the Black Box: Hierarchical Sampling Optimization for Estimating Human Hand Pose—0
An Image is Worth Multiple Words: Multi-attribute Inversion for Constrained Text-to-Image Synthesis—0
Open Set Learning with Counterfactual Images—0
Open Set Synthetic Image Source Attribution—0
An Image is Worth More Than 16x16 Patches: Exploring Transformers on Individual Pixels—0
AniFaceDrawing: Anime Portrait Exploration during Your Sketching—0
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations—0
OpFlowTalker: Realistic and Natural Talking Face Generation via Optical Flow Guidance—0
OP-LoRA: The Blessing of Dimensionality—0
OptGAN: Optimizing and Interpreting the Latent Space of the Conditional Text-to-Image GANs—0
Optical Diffusion Models for 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