---
license: cc-by-nc-4.0
task_categories:
- image-to-image
- text-to-image
tags:
- synthetic
- blender
- urban-planning
- controlnet
- multimodal
size_categories:
- 10K<n<100K
---

# Synthetic Urban Multimodal Dataset (v0.1)

This dataset contains **8,000 high-quality synthetic images** of procedurally generated city models, designed specifically for training and evaluating AI models such as ControlNet and LoRA.

## Dataset Description
All assets were generated using a custom Blender-Python automated pipeline. The dataset provides 100 unique urban architectures, each rendered from 8 directions with 10 different artistic styles.

- **Total Samples:** 8,000 (Total 40,000 files across modalities)
- **Resolution:** 512x512
- **Modalities per sample:**
  - `rgb_images`: Stylized urban rendering.
  - `depth_maps`: High-precision depth information.
  - `normal_maps`: Surface normal vectors.
  - `albedo_images`: Pure texture color (unlit).
  - `mask_images`: Binary/Alpha masks for segmentation.

## Data Structure
The dataset follows the standard Hugging Face metadata format. Each entry in `metadata.jsonl` maps the RGB image to its corresponding multimodal maps and captions.

## Intended Use
- Training ControlNet for spatial and structural control.
- Fine-tuning LoRA for specific architectural or artistic styles.
- Research in Computer Vision and Synthetic Data generation.

## License
This dataset is provided under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. Commercial use is prohibited.

## Author & Technical Background
Developed by [ひのき(jp-cypress) / https://zenn.dev/jp_cypress].
For technical details on how this dataset was procedurally generated using Blender API and Python, please refer to the technical report on Zenn (Japanese).