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 3051–3100 of 6689 papers

TitleStatusHype
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Deep Boosting: Joint Feature Selection and Analysis Dictionary Learning in Hierarchy—0
Beyond Words: Advancing Long-Text Image Generation via Multimodal Autoregressive Models—0
Deep Algorithm Unrolling for Biomedical Imaging—0
GMM-Based Generative Adversarial Encoder Learning—0
The Missing U for Efficient Diffusion Models—0
Hybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models—0
HYPE-C: Evaluating Image Completion Models Through Standardized Crowdsourcing—0
HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models—0
HYPE: Human-eYe Perceptual Evaluation of Generative Models—0
Hyperbolic Generative Adversarial Network—0
Glyph-ByT5: A Customized Text Encoder for Accurate Visual Text Rendering—0
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks—0
DEEP ADVERSARIAL FORWARD MODEL—0
GLoD: Composing Global Contexts and Local Details in Image Generation—0
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories—0
GLocal: Global Graph Reasoning and Local Structure Transfer for Person Image Generation—0
Decoupling Training-Free Guided Diffusion by ADMM—0
Hyper-SD: Trajectory Segmented Consistency Model for Efficient Image Synthesis—0
BodyGAN: General-Purpose Controllable Neural Human Body Generation—0
Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation—0
Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images—0
Global Context with Discrete Diffusion in Vector Quantised Modelling for Image Generation—0
Semantically Consistent Person Image Generation—0
A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN—0
IA-T2I: Internet-Augmented Text-to-Image Generation—0
GL-GAN: Adaptive Global and Local Bilevel Optimization model of Image Generation—0
ICE-Bench: A Unified and Comprehensive Benchmark for Image Creating and Editing—0
Decouple-Then-Merge: Towards Better Training for Diffusion Models—0
ID^3: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition—0
IDAdapter: Learning Mixed Features for Tuning-Free Personalization of Text-to-Image Models—0
ID-Aligner: Enhancing Identity-Preserving Text-to-Image Generation with Reward Feedback Learning—0
Glauber Generative Model: Discrete Diffusion Models via Binary Classification—0
ID-Cloak: Crafting Identity-Specific Cloaks Against Personalized Text-to-Image Generation—0
Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning—0
Designing GANs: A Likelihood Ratio Approach—0
Identifying and Mitigating the Security Risks of Generative AI—0
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts—0
Identity-Aware CycleGAN for Face Photo-Sketch Synthesis and Recognition—0
Identity Encoder for Personalized Diffusion—0
Identity From Here, Pose From There: Self-Supervised Disentanglement and Generation of Objects Using Unlabeled Videos—0
iDesigner: A High-Resolution and Complex-Prompt Following Text-to-Image Diffusion Model for Interior Design—0
GLASS: Guided Latent Slot Diffusion for Object-Centric Learning—0
Beyond Text: Implementing Multimodal Large Language Model-Powered Multi-Agent Systems Using a No-Code Platform—0
IE-Bench: Advancing the Measurement of Text-Driven Image Editing for Human Perception Alignment—0
IFaceUV: Intuitive Motion Facial Image Generation by Identity Preservation via UV map—0
IFAdapter: Instance Feature Control for Grounded Text-to-Image Generation—0
Grand Challenge of 106-Point Facial Landmark Localization—0
Interpreting the Latent Space of GANs via Correlation Analysis for Controllable Concept Manipulation—0
Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data—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