---
license: mit
library_name: pytorch
pipeline_tag: image-classification
tags:
  - texture
  - tileability
  - metric
  - cvpr2024
---

# TexTile: A Differentiable Metric for Texture Tileability

Pretrained weights for **TexTile** [CVPR 2024], a differentiable metric that measures how well a texture image can be tiled with itself without visible seams or repeating artifacts.

[Carlos Rodriguez-Pardo](https://carlosrodriguezpardo.es/), [Dan Casas](https://dancasas.github.io/), [Elena Garces](https://www.elenagarces.es/), [Jorge Lopez-Moreno](http://www.jorg3.com/)

- Paper: [arXiv:2403.12961](https://arxiv.org/abs/2403.12961)
- Code: [github.com/crp94/textile](https://github.com/crp94/textile)

## Files

| File | Description |
| --- | --- |
| `model.safetensors` | Weights in safetensors format. Loaded by the `textile-metric` package. |
| `textile_v3.pth` | Original PyTorch state dict (identical weights). |

## Usage

```bash
pip install textile-metric
```

```python
import textile
from textile.utils.image_utils import read_and_process_image

loss_textile = textile.Textile()  # downloads model.safetensors from this repo
image = read_and_process_image(YOUR_PATH)
textile_value = loss_textile(image)
```

The architecture is a ConvNeXt-Base with added linear self-attention layers; see `textile/utils/create_model.py` in the code repository.

## Citation

```
@inproceedings{Rodriguez-Pardo_2024_CVPR,
    author = {Rodriguez-Pardo, Carlos and Casas, Dan and Garces, Elena and Lopez-Moreno, Jorge},
    title = {TexTile: A Differentiable Metric for Texture Tileability},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year = {2024}
}
```