README.md

metadata

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.

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

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"])