OverLay++: Dense-Overlap Layout-to-Image Generation Dataset

arXiv:2610.09071 · cs.CV · Submitted 2026-10-06 · Read on arXiv

cs.CV

Submitted: 2026-10-06

Updated: 2026-10-06

Code: https://github.com/kakaobrain/coyo-dataset

Project page: https://mlpc-ucsd.github.io/OverLayPP

Terminology

• Diffusion Model — 14× in this paper · explained in 8 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Alignment — 11× in this paper · explained in 1 episode
• Grounding — 11× in this paper · explained in 3 episodes
• Diffusion Models — 10× in this paper · explained in 6 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 5× in this paper
• Conference on Computer Vision and Pattern Recognition — 5× in this paper
• Accuracy — 3× in this paper · explained in 1 episode
• Fidelity — 3× in this paper · explained in 2 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Foundation Models — 3× in this paper · explained in 7 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Inception Score — 1× in this paper
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Multimodal Dataset — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• OminiControl: Minimal and Universal Control for Diffusion Transformer
• DICEPTION: A Generalist Diffusion Model for Visual Perceptual Tasks
• 3DIS: Depth-Driven Decoupled Instance Synthesis for Text-to-Image Generation
• 3DIS-FLUX: simple and efficient multi-instance generation with DiT rendering
• CreatiLayout: Siamese Multimodal Diffusion Transformer for Creative Layout-to-Image Generation
• EliGen: Entity-Level Controlled Image Generation with Regional Attention
• MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis
• Denoising Diffusion Implicit Models
• Classifier-Free Diffusion Guidance
• Hierarchical Text-Conditional Image Generation with CLIP Latents
• Lay-Your-Scene: Natural Scene Layout Generation with Diffusion Transformers
• LAION-5B: An open large-scale dataset for training next generation image-text models
• OverLayBench: A Benchmark for Layout-to-Image Generation with Dense Overlaps
• Kosmos-2: Grounding Multimodal Large Language Models to the World
• Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
• Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
• LLM Blueprint: Enabling Text-to-Image Generation with Complex and Detailed Prompts
• Training-free Regional Prompting for Diffusion Transformers
• HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image Generation
• Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks

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