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 4101–4150 of 6689 papers

TitleStatusHype
Breaking Free: How to Hack Safety Guardrails in Black-Box Diffusion Models!Code0
Text2Street: Controllable Text-to-image Generation for Street Views—0
FoolSDEdit: Deceptively Steering Your Edits Towards Targeted Attribute-aware Distribution—0
Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?—0
M^3Face: A Unified Multi-Modal Multilingual Framework for Human Face Generation and Editing—0
Risk-Sensitive Diffusion: Robustly Optimizing Diffusion Models with Noisy Samples—0
Separable Multi-Concept Erasure from Diffusion Models—0
Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models—0
Variational Quantum Circuits Enhanced Generative Adversarial Network—0
Can Shape-Infused Joint Embeddings Improve Image-Conditioned 3D Diffusion?—0
Neural Language of Thought Models—0
Unconditional Latent Diffusion Models Memorize Patient Imaging Data: Implications for Openly Sharing Synthetic DataCode0
High-Quality Medical Image Generation from Free-hand Sketch—0
Head and Neck Tumor Segmentation from [18F]F-FDG PET/CT Images Based on 3D Diffusion Model—0
Fréchet Distance for Offline Evaluation of Information Retrieval Systems with Sparse Labels—0
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation—0
Image Anything: Towards Reasoning-coherent and Training-free Multi-modal Image Generation—0
Diffusion Model Compression for Image-to-Image Translation—0
X-ray Image Generation as a Method of Performance Prediction for Real-Time Inspection: a Case StudyCode0
Diffutoon: High-Resolution Editable Toon Shading via Diffusion Models—0
Multilingual Text-to-Image Generation Magnifies Gender Stereotypes and Prompt Engineering May Not Help YouCode0
Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors—0
Spatial-Aware Latent Initialization for Controllable Image Generation—0
Object-Driven One-Shot Fine-tuning of Text-to-Image Diffusion with Prototypical Embedding—0
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation—0
Annotated Hands for Generative ModelsCode0
Image Synthesis with Graph Conditioning: CLIP-Guided Diffusion Models for Scene Graphs—0
Explicitly Representing Syntax Improves Sentence-to-layout Prediction of Unexpected SituationsCode0
StyleInject: Parameter Efficient Tuning of Text-to-Image Diffusion Models—0
UrbanGenAI: Reconstructing Urban Landscapes using Panoptic Segmentation and Diffusion Models—0
No Longer Trending on Artstation: Prompt Analysis of Generative AI Art—0
UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion—0
CIMGEN: Controlled Image Manipulation by Finetuning Pretrained Generative Models on Limited Data—0
Faster Projected GAN: Towards Faster Few-Shot Image Generation—0
Codebook-enabled Generative End-to-end Semantic Communication Powered by Transformer—0
Text-to-Image Cross-Modal Generation: A Systematic Review—0
Large-scale Reinforcement Learning for Diffusion Models—0
Diffusion Model Conditioning on Gaussian Mixture Model and Negative Gaussian Mixture Gradient—0
CLIP Model for Images to Textual Prompts Based on Top-k Neighbors—0
DiffusionGPT: LLM-Driven Text-to-Image Generation System—0
Efficient generative adversarial networks using linear additive-attention TransformersCode0
Instilling Multi-round Thinking to Text-guided Image Generation—0
SCoFT: Self-Contrastive Fine-Tuning for Equitable Image Generation—0
Key-point Guided Deformable Image Manipulation Using Diffusion Model—0
Generation of Synthetic Images for Pedestrian Detection Using a Sequence of GANs—0
ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image GenerationCode0
Seek for Incantations: Towards Accurate Text-to-Image Diffusion Synthesis through Prompt Engineering—0
Frequency-Time Diffusion with Neural Cellular Automata—0
Parrot: Pareto-optimal Multi-Reward Reinforcement Learning Framework for Text-to-Image Generation—0
Scissorhands: Scrub Data Influence via Connection Sensitivity in NetworksCode0
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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