This page compares the source lines of code (SLOC) of huggingface/transformers and huggingface/diffusers using cached OctoCounts reports. Code size is not code quality: a larger count only means more source material, not a better or worse project.
As of 2026-09-06, huggingface/transformers contains 1,315,929 total lines (1,033,660 code) across 3,307 files, while huggingface/diffusers contains 898,783 total lines (718,838 code) across 1,645 files as of 2026-08-26. huggingface/transformers is about 1.4x the size of huggingface/diffusers by code lines. Code size is not code quality: a larger count only means more source material, not a better or worse project.
| Metric | huggingface/transformers | huggingface/diffusers |
|---|---|---|
| Files | 3,307 | 1,645 |
| Total lines | 1,315,929 | 898,783 |
| Code lines | 1,033,660 | 718,838 |
| Comment lines | 103,315 | 64,002 |
| Blank lines | 178,954 | 115,943 |
| Languages counted | 11 | 10 |
Top languages in huggingface/transformers: Python (99.6% of code), JSON (0.2% of code), TOML (0.1% of code). Top languages in huggingface/diffusers: Python (99.8% of code), Jupyter Notebooks (0.1% of code), Dockerfile (0.0% of code). Python, Dockerfile, YAML appear in both top language lists; JSON, TOML appear only in huggingface/transformers's top languages; Jupyter Notebooks, Shell appear only in huggingface/diffusers's top languages.
Methodology: both counts come from cached OctoCounts reports generated with tokei. huggingface/transformers was counted at ref c93057d4835cd31752bb56f59989dd27696eb45b (commit c93057d4835c) on 2026-09-06; huggingface/diffusers was counted at ref main (commit d57cecde92a6) on 2026-08-26. See the counting methodology for ignored directories and analysis options.
Evidence and next steps:
Note: code size is not code quality. OctoCounts only reports reproducible line counts and makes no claim that either project is better.
huggingface/transformers has more code: 1,033,660 code lines versus 718,838 for huggingface/diffusers, about 1.4x as much, based on cached OctoCounts reports as of 2026-09-06.
Not necessarily. SLOC measures size, not complexity, quality, or maintainability. A larger codebase can mean more features, more generated or vendored code, more verbose language idioms, or more tests — none of which imply the code is harder to work with. Use SLOC to gauge the scale of what you'd be reading or maintaining, not as a quality signal for huggingface/transformers, huggingface/diffusers, or any repository.
Methodology: both counts come from cached OctoCounts reports generated with tokei. huggingface/transformers was counted at ref c93057d4835cd31752bb56f59989dd27696eb45b (commit c93057d4835c) on 2026-09-06; huggingface/diffusers was counted at ref main (commit d57cecde92a6) on 2026-08-26. See the counting methodology for ignored directories and analysis options.
Yes. This page shows the default branch for each repository. Use the interactive comparison tool to pick any public GitHub repository, branch, tag, or commit SHA for both sides and get a fresh side-by-side report.