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 2201–2225 of 6689 papers

TitleStatusHype
Open-Source Acceleration of Stable-Diffusion.cpp Deployable on All DevicesCode0
On the Issues of TrueDepth Sensor Data for Computer Vision Tasks Across Different iPad GenerationsCode0
Optimal Eye Surgeon: Finding Image Priors through Sparse Generators at InitializationCode0
Class-Splitting Generative Adversarial NetworksCode0
Array Camera Image Fusion using Physics-Aware TransformersCode0
On the Diversity of Realistic Image SynthesisCode0
AR-RAG: Autoregressive Retrieval Augmentation for Image GenerationCode0
On Learning 3D Face Morphable Model from In-the-wild ImagesCode0
On the Cultural Gap in Text-to-Image GenerationCode0
A DNN Optimizer that Improves over AdaBelief by Suppression of the Adaptive Stepsize RangeCode0
A Robust Attentional Framework for License Plate Recognition in the WildCode0
On GANs and GMMsCode0
Class-Distinct and Class-Mutual Image Generation with GANsCode0
Class-Continuous Conditional Generative Neural Radiance FieldCode0
On gradient regularizers for MMD GANsCode0
Adversarial Out-domain Examples for Generative ModelsCode0
ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion DesignCode0
On Differentially Private 3D Medical Image Synthesis with Controllable Latent Diffusion ModelsCode0
DPGEN: Differentially Private Generative Energy-Guided Network for Natural Image SynthesisCode0
CIGLI: Conditional Image Generation from Language & ImageCode0
Omni-Directional Image Generation from Single Snapshot ImageCode0
Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANsCode0
DomainStudio: Fine-Tuning Diffusion Models for Domain-Driven Image Generation using Limited DataCode0
DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric FinetuningCode0
ChefFusion: Multimodal Foundation Model Integrating Recipe and Food Image GenerationCode0
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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