Getting Started¶

TorchFont ships a small but opinionated toolbox for working with vector fonts in PyTorch. This page walks you through installation, validates the environment, and provides a short end-to-end example that loads glyphs, applies transforms, and feeds them into a dataloader.

Prerequisites¶

Python 3.10 or newer
PyTorch 2.6 or newer
Git when you plan to use torchfont.datasets.GoogleFonts or torchfont.datasets.FontRepo

Installation¶

The recommended path is to manage dependencies with uv.

uv add torchfont

If you prefer pip, run:

pip install torchfont

Confirm the installation by importing the package and printing the version number:

import torchfont
print(torchfont.__version__)

Downloading Fonts¶

TorchFont does not bundle any font assets. For a lightweight sandbox, clone a subset of Google Fonts into data/google_fonts with the provided dataset helper:

from torchfont.datasets import GoogleFonts

dataset = GoogleFonts(
    root="data/google_fonts",
    ref="main",
    download=True,  # performs a sparse checkout the first time
)

The dataset keeps the sparse checkout up to date on subsequent runs. Point root to any writable cache directory.

First Glyph Dataset¶

The torchfont.datasets.FontFolder dataset turns a directory of .otf or .ttf files into an indexable PyTorch-style dataset. Each item contains the pen command types and normalized coordinates emitted by torchfont.io.pens.TensorPen, plus style/content labels.

from torch.utils.data import DataLoader

from torchfont.datasets import FontFolder
from torchfont.transforms import Compose, LimitSequenceLength, Patchify

transform = Compose(
    (
        LimitSequenceLength(max_len=256),
        Patchify(patch_size=32),
    )
)

dataset = FontFolder(
    root="~/fonts",  # scans recursively
    codepoint_filter=[ord("A"), ord("B"), ord("C")],
    transform=transform,
)

def collate_fn(batch):
    types, coords, styles, contents = [], [], [], []
    for (sample, (style, content)) in batch:
        t, c = sample
        types.append(t)
        coords.append(c)
        styles.append(style)
        contents.append(content)
    return types, coords, styles, contents

loader = DataLoader(dataset, batch_size=32, shuffle=True, collate_fn=collate_fn)
types, coords, style_labels, content_labels = next(iter(loader))

Working with Google Fonts¶

The torchfont.datasets.GoogleFonts dataset mirrors the official repository via sparse checkout. It exposes the same sample structure as FontFolder, so you can reuse the same transforms and dataloaders:

from torchfont.datasets import GoogleFonts

google_fonts = GoogleFonts(
    root="data/google_fonts",
    ref="main",
    transform=transform,
    download=True,
    patterns=("ofl/*/*.ttf", "ofl/*/*.otf"),
)

sample, (style_label, content_label) = google_fonts[0]

Next Steps¶

Continue to the User Guide for a deeper dive into the dataset, transform, and I/O utilities.
Review Contributor Guide if you plan to submit fixes or new features.