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 4051–4100 of 6689 papers

TitleStatusHype
Attacks and Defenses for Generative Diffusion Models: A Comprehensive Survey—0
Attack Deterministic Conditional Image Generative Models for Diverse and Controllable Generation—0
Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images—0
InfoCNF: An Efficient Conditional Continuous Normalizing Flow with Adaptive Solvers—0
InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers—0
Towards Better Text-to-Image Generation Alignment via Attention Modulation—0
Towards CGAN-based Satellite Image Synthesis with Partial Pixel-Wise Annotation—0
Information Theoretic Text-to-Image Alignment—0
Towards Consistent and Controllable Image Synthesis for Face Editing—0
Inpaint Biases: A Pathway to Accurate and Unbiased Image Generation—0
Towards Controllable and Interpretable Face Completion via Structure-Aware and Frequency-Oriented Attentive GANs—0
InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations—0
Inspecting and Interacting with Meaningful Music Representations using VAE—0
Inspirational Adversarial Image Generation—0
A Training-Free Style-Personalization via Scale-wise Autoregressive Model—0
Towards Effective Usage of Human-Centric Priors in Diffusion Models for Text-based Human Image Generation—0
A Training-Free Style-aligned Image Generation with Scale-wise Autoregressive Model—0
AtomoVideo: High Fidelity Image-to-Video Generation—0
Instance-Guided Context Rendering for Cross-Domain Person Re-Identification—0
Instance Map based Image Synthesis with a Denoising Generative Adversarial Network—0
Instance Segmentation of Visible and Occluded Regions for Finding and Picking Target from a Pile of Objects—0
Instant 3D Human Avatar Generation using Image Diffusion Models—0
InstantBooth: Personalized Text-to-Image Generation without Test-Time Finetuning—0
Atmospheric Turbulence Correction via Variational Deep Diffusion—0
InstantDrag: Improving Interactivity in Drag-based Image Editing—0
InstantFamily: Masked Attention for Zero-shot Multi-ID Image Generation—0
A Tiered GAN Approach for Monet-Style Image Generation—0
A Tale of Two Latent Flows: Learning Latent Space Normalizing Flow with Short-run Langevin Flow for Approximate Inference—0
Instella-T2I: Pushing the Limits of 1D Discrete Latent Space Image Generation—0
Instilling Multi-round Thinking to Text-guided Image Generation—0
AsyncDSB: Schedule-Asynchronous Diffusion Schrödinger Bridge for Image Inpainting—0
InstructBooth: Instruction-following Personalized Text-to-Image Generation—0
InstructEngine: Instruction-driven Text-to-Image Alignment—0
Instruct-Imagen: Image Generation with Multi-modal Instruction—0
Instruction-augmented Multimodal Alignment for Image-Text and Element Matching—0
Instruction Tuning-free Visual Token Complement for Multimodal LLMs—0
InstructVid2Vid: Controllable Video Editing with Natural Language Instructions—0
Integral Equations and Machine Learning—0
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation—0
Towards Explainable Partial-AIGC Image Quality Assessment—0
Towards Fair and Robust Face Parsing for Generative AI: A Multi-Objective Approach—0
ASurvey: Spatiotemporal Consistency in Video Generation—0
A Survey on Vision Autoregressive Model—0
Intelligent image synthesis to attack a segmentation CNN using adversarial learning—0
Interactive Fashion Content Generation Using LLMs and Latent Diffusion Models—0
Interactive Illumination Invariance—0
Interactive Image Manipulation with Natural Language Instruction Commands—0
Towards Few-Call Model Stealing via Active Self-Paced Knowledge Distillation and Diffusion-Based Image Generation—0
Interactive Optimization of Generative Image Modeling using Sequential Subspace Search and Content-based Guidance—0
Interactive Text 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