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 1676–1700 of 6689 papers

TitleStatusHype
Using Physics Informed Generative Adversarial Networks to Model 3D porous media—0
Fine-Tuning Image-Conditional Diffusion Models is Easier than You ThinkCode4
MM2Latent: Text-to-facial image generation and editing in GANs with multimodal assistanceCode1
OmniGen: Unified Image GenerationCode7
Improving the Efficiency of Visually Augmented Language ModelsCode0
2S-ODIS: Two-Stage Omni-Directional Image Synthesis by Geometric Distortion CorrectionCode0
On Synthetic Texture Datasets: Challenges, Creation, and Curation—0
Robust image representations with counterfactual contrastive learningCode1
SimInversion: A Simple Framework for Inversion-Based Text-to-Image Editing—0
VAE-QWGAN: Addressing Mode Collapse in Quantum GANs via Autoencoding Priors—0
Cross-modality image synthesis from TOF-MRA to CTA using diffusion-based models—0
MotionCom: Automatic and Motion-Aware Image Composition with LLM and Video Diffusion PriorCode0
One-Shot Learning for Pose-Guided Person Image Synthesis in the WildCode1
E-Commerce Inpainting with Mask Guidance in Controlnet for Reducing Overcompletion—0
Generalizing Alignment Paradigm of Text-to-Image Generation with Preferences through f-divergence Minimization—0
GRIN: Zero-Shot Metric Depth with Pixel-Level Diffusion—0
Finetuning CLIP to Reason about Pairwise DifferencesCode1
Beta-Sigma VAE: Separating beta and decoder variance in Gaussian variational autoencoderCode0
Enhancing Privacy in ControlNet and Stable Diffusion via Split Learning—0
GroundingBooth: Grounding Text-to-Image Customization—0
InstantDrag: Improving Interactivity in Drag-based Image Editing—0
High-Frequency Anti-DreamBooth: Robust Defense against Personalized Image SynthesisCode0
Scribble-Guided Diffusion for Training-free Text-to-Image GenerationCode1
Click2Mask: Local Editing with Dynamic Mask GenerationCode1
Improving Virtual Try-On with Garment-focused Diffusion 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