---
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"])
```