---
license: mit
library_name: transformers
tags:
- image-to-image
- lineart
inference: false
---

# MangaLineExtraction-hf

The huggingface `transformers` compatible version of [MangaLineExtraction_PyTorch](https://github.com/ljsabc/MangaLineExtraction_PyTorch).

Original repo: https://github.com/ljsabc/MangaLineExtraction_PyTorch

## Example


```py
from PIL import Image
import torch

from transformers import AutoModel, AutoImageProcessor

REPO_NAME = "p1atdev/MangaLineExtraction-hf"

model = AutoModel.from_pretrained(REPO_NAME, trust_remote_code=True)
processor = AutoImageProcessor.from_pretrained(REPO_NAME, trust_remote_code=True)

image = Image.open("./sample.jpg")

inputs = processor(image, return_tensors="pt")

with torch.no_grad():
    outputs = model(inputs.pixel_values)

line_image = Image.fromarray(outputs.pixel_values[0].numpy().astype("uint8"), mode="L")
line_image.save("./line_image.png")
```
or you can use the pipeline

```py
from transformers import pipeline

pipe = pipeline("image-to-image", model="p1atdev/MangaLineExtraction-hf", trust_remote_code=True)
pipe("sample.jpg")
```

|`sample.jpg`|Generated line image|
|-|-|
|<img src="./images/sample.jpg" width="320px" alt="Source image">|<img src="./images/line_image.png" width="320px" alt="Generated line image">|


## Model Details

### Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Chengze Li, Xueting Liu, Tien-Tsin Wong
- **Converted by:** Plat
- **License:** MIT

### Model Sources

- **Repository:** https://github.com/ljsabc/MangaLineExtraction_PyTorch
- **Paper:** https://ttwong12.github.io/papers/linelearn/linelearn.pdf
- **Project page:** https://www.cse.cuhk.edu.hk/~ttwong/papers/linelearn/linelearn.html 

## Citation 

**BibTeX:**

```bibtex
@article{li-2017-deep,
    author   = {Chengze Li and Xueting Liu and Tien-Tsin Wong},
    title    = {Deep Extraction of Manga Structural Lines},
    journal  = {ACM Transactions on Graphics (SIGGRAPH 2017 issue)},
    month    = {July},
    year     = {2017},
    volume   = {36},
    number   = {4},
    pages    = {117:1--117:12},
}
```