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

Showing 28512875 of 6689 papers

TitleStatusHype
An Empirical Study and Analysis of Text-to-Image Generation Using Large Language Model-Powered Textual RepresentationCode0
Gradient Estimators for Implicit ModelsCode0
Fast Direct: Query-Efficient Online Black-box Guidance for Diffusion-model Target GenerationCode0
Fast Diffusion ModelCode0
A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image ReconstructionCode0
Contrastive Image Synthesis and Self-supervised Feature Adaptation for Cross-Modality Biomedical Image SegmentationCode0
Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing ImagesCode0
Latent Feature and Attention Dual Erasure Attack against Multi-View Diffusion Models for 3D Assets ProtectionCode0
A Layer-Based Sequential Framework for Scene Generation with GANsCode0
Fast Adaptive Meta-Learning for Few-Shot Image GenerationCode0
KPE: Keypoint Pose Encoding for Transformer-based Image GenerationCode0
Pose Guided Person Image GenerationCode0
Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image GeneratorCode0
Latent Flow TransformerCode0
Laplacian-Steered Neural Style TransferCode0
Contrast-augmented Diffusion Model with Fine-grained Sequence Alignment for Markup-to-Image GenerationCode0
Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning ServicesCode0
Language Guided Adversarial PurificationCode0
AttrLostGAN: Attribute Controlled Image Synthesis from Reconfigurable Layout and StyleCode0
GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image GenerationCode0
F-ANcGAN: An Attention-Enhanced Cycle Consistent Generative Adversarial Architecture for Synthetic Image Generation of NanoparticlesCode0
Attributing Fake Images to GANs: Learning and Analyzing GAN FingerprintsCode0
LAION-5B: An open large-scale dataset for training next generation image-text modelsCode0
Langevin Autoencoders for Learning Deep Latent Variable ModelsCode0
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow ReconstructionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Improved DDPMFID12.3Unverified
2ADMFID11.84Unverified
3BigGAN-deepFID8.1Unverified
4Polarity-BigGANFID6.82Unverified
5VQGAN+Transformer (k=mixed, p=1.0, a=0.005)FID6.59Unverified
6MaskGITFID6.18Unverified
7VQGAN+Transformer (k=600, p=1.0, a=0.05)FID5.2Unverified
8CDMFID4.88Unverified
9ADM-GFID4.59Unverified
10RINFID4.51Unverified
#ModelMetricClaimedVerifiedStatus
1PresGANFID52.2Unverified
2RESFLOWFID48.29Unverified
3Residual FlowFID46.37Unverified
4GLF+perceptual loss (ours)FID44.6Unverified
5ProdPoly no activation functionsFID40.45Unverified
6ProdPoly no activation functionsFID36.77Unverified
7ACGANFID35.47Unverified
8DenseFlow-74-10FID34.9Unverified
9NVAE w/ flowFID32.53Unverified
10QSNGANFID31.97Unverified
#ModelMetricClaimedVerifiedStatus
1GLIDE + CLSFID30.87Unverified
2GLIDE + CLIPFID30.46Unverified
3GLIDE + CLS-FREEFID29.22Unverified
4GLIDE + CLIP + CLS + CLS-FREEFID29.18Unverified
5PGMGANFID21.73Unverified
6CLR-GANFID20.27Unverified
7FMFID14.45Unverified
8CT (Direct Generation, NFE=1)FID13Unverified
9CT (Direct Generation, NFE=2)FID11.1Unverified
10GLIDE +CLSKID7.95Unverified