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dataset_info:
features:
- name: url
dtype: string
- name: objects
list:
- name: bbox
list: int64
- name: category
dtype: string
- name: long_local_prompt
dtype: string
- name: short_local_prompt
dtype: string
- name: long_global_caption
dtype: string
- name: short_global_caption
dtype: string
- name: image_hash
dtype: string
splits:
- name: train
num_bytes: 2672741454
num_examples: 499249
download_size: 665190298
dataset_size: 2672741454
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Abstract
Layout-to-Image generation has made substantial progress in image generation with spatial and object-level control. However, existing methods still struggle with complex scenes containing many overlapping and interacting objects. We argue that training data is a particular bottleneck: existing datasets lack examples with dense, complex object interactions. To address this gap, we introduce **OverLay++**, a large-scale Layout-to-Image dataset with structurally complex scenes. OverLay++ contains approximately 500K images with an average of 6.6 objects per image, exceeding existing datasets by 1.67× in annotation density. Beyond annotation density, OverLay++ provides rich semantic detail with object captions over six times longer than in current datasets. Our dataset generation pipeline is simple and robust, producing accurate overlapping regions with rich per-object captions. Across multiple benchmarks, state-of-the-art Layout-to-Image methods trained on OverLay++ dataset show consistent improvement and faster convergence demonstrating the importance of dense, overlap-aware, and caption-rich supervision for controllable image generation. Our code is available at [https://mlpc-ucsd.github.io/OverLayPP](https://mlpc-ucsd.github.io/OverLayPP).
## Dataset description
Each example contains:
- `objects`: a list of annotated objects, each with:
- `bbox`
- `category`
- `long_local_prompt`
- `short_local_prompt`
- `long_global_caption`
- `short_global_caption`
- `url`: the original source URL
- `image_hash`: the original image identity hash when available
The object annotations pair image-coordinate bounding boxes with local prompts at two
levels of detail. Global captions summarize the complete image.
## Example usage
```python
from datasets import load_dataset
ds = load_dataset("mlpcucsd/OverLayPP", split="train", token=True)
sample = ds[0]
print(sample["short_global_caption"])
print(sample["objects"][0]["category"])
print(sample["objects"][0]["short_local_prompt"])
print(sample["url"])
```
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