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 1401–1450 of 6689 papers

TitleStatusHype
Masked and Adaptive Transformer for Exemplar Based Image TranslationCode1
Forget-Me-Not: Learning to Forget in Text-to-Image Diffusion ModelsCode1
WordStylist: Styled Verbatim Handwritten Text Generation with Latent Diffusion ModelsCode1
MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion PathCode1
Fully Hyperbolic Convolutional Neural Networks for Computer VisionCode1
SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapyCode1
Memory-Efficient 3D Denoising Diffusion Models for Medical Image ProcessingCode1
Freestyle Layout-to-Image SynthesisCode1
CoLa-Diff: Conditional Latent Diffusion Model for Multi-Modal MRI SynthesisCode1
UrbanGIRAFFE: Representing Urban Scenes as Compositional Generative Neural Feature FieldsCode1
Efficient Scale-Invariant Generator with Column-Row Entangled Pixel SynthesisCode1
High Fidelity Image Synthesis With Deep VAEs In Latent SpaceCode1
End-to-End Diffusion Latent Optimization Improves Classifier GuidanceCode1
Set-the-Scene: Global-Local Training for Generating Controllable NeRF ScenesCode1
Medical diffusion on a budget: Textual Inversion for medical image generationCode1
NeRF-GAN Distillation for Efficient 3D-Aware Generation with ConvolutionsCode1
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram SynthesisCode1
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question AnsweringCode1
MAGVLT: Masked Generative Vision-and-Language TransformerCode1
Polynomial Implicit Neural Representations For Large Diverse DatasetsCode1
Localizing Object-level Shape Variations with Text-to-Image Diffusion ModelsCode1
Object-Centric Slot DiffusionCode1
Discovering Interpretable Directions in the Semantic Latent Space of Diffusion ModelsCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
GlueGen: Plug and Play Multi-modal Encoders for X-to-image GenerationCode1
Efficient Diffusion Training via Min-SNR Weighting StrategyCode1
P+: Extended Textual Conditioning in Text-to-Image GenerationCode1
SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral PerspectiveCode1
Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion ModelsCode1
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language KnowledgeCode1
SelfPromer: Self-Prompt Dehazing Transformers with Depth-ConsistencyCode1
EHRDiff: Exploring Realistic EHR Synthesis with Diffusion ModelsCode1
TrojDiff: Trojan Attacks on Diffusion Models with Diverse TargetsCode1
Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image GenerationCode1
Vector Quantized Time Series Generation with a Bidirectional Prior ModelCode1
Transformer-based Image Generation from Scene GraphsCode1
Zeroth-Order Optimization Meets Human Feedback: Provable Learning via Ranking OraclesCode1
Learning multi-scale local conditional probability models of imagesCode1
Few-Shot Defect Image Generation via Defect-Aware Feature ManipulationCode1
Dense Pixel-to-Pixel Harmonization via Continuous Image RepresentationCode1
A Complete Recipe for Diffusion Generative ModelsCode1
ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of PneumothoraxCode1
Single Image Backdoor Inversion via Robust Smoothed ClassifiersCode1
Continuous-Time Functional Diffusion ProcessesCode1
Dissolving Is Amplifying: Towards Fine-Grained Anomaly DetectionCode1
BrainCLIP: Bridging Brain and Visual-Linguistic Representation Via CLIP for Generic Natural Visual Stimulus DecodingCode1
ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image DetectionCode1
Teaching CLIP to Count to TenCode1
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMCCode1
Prompt Stealing Attacks Against Text-to-Image Generation ModelsCode1
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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