---
dataset_info:
  features:
  - name: input_image
    dtype: image
  - name: edit_prompt
    dtype: string
  - name: edited_image
    dtype: image
  - name: index
    dtype: int64
  splits:
  - name: train
    num_bytes: 8686582352.97
    num_examples: 7265
  download_size: 8686714223
  dataset_size: 8686582352.97
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-nc-4.0
task_categories:
- image-to-image
- text-to-image
language:
- en
tags:
- ar
size_categories:
- 1K<n<10K
---
# 🖼️ Portrait to Anime Style Tranfer Data

This dataset consists of paired human and corresponding anime-style images, accompanied by descriptive prompts. The human images are sourced from the CelebA dataset, and the anime-style counterparts were generated using a combination of state-of-the-art GAN architectures and diffusion models.

It is designed to support a wide range of tasks,
 - **GAN research**
 - **Diffusion model fine-tuning**
 - **Model evaluation**
 - **Benchmarking for image-to-image and text-to-image generation**.

## 📁 Dataset Structure

Each sample contains:
- `input_image`: Original image 
- `edit_prompt`: Text instruction describing the desired style
- `edited_image`: Resulting image after applying the edit
- `index`: default integer with 0 value

## 🚀 How to Use

```python
from datasets import load_dataset

# Replace with your dataset path
dataset = load_dataset("murali1729S/portrait_2_avatar",split="train")
```
## 📚 References

This dataset builds upon the following works:

- W. Xiao *et al.*, "**Appearance-Preserved Portrait-to-Anime Translation via Proxy-Guided Domain Adaptation**," *IEEE Transactions on Visualization and Computer Graphics*, vol. 30, no. 7, pp. 3104–3120, July 2024. [https://doi.org/10.1109/TVCG.2022.3228707](https://doi.org/10.1109/TVCG.2022.3228707)

- Z. Liu, P. Luo, X. Wang, and X. Tang, "**Deep Learning Face Attributes in the Wild**," in *Proceedings of the International Conference on Computer Vision (ICCV)*, December 2015.