Cross-platform AI video enhancement CLI for video upscaling, image upscaling, frame interpolation, and video restoration. PixelFlow uses Real-ESRGAN, RIFE, and FFmpeg to improve video quality, increase video resolution, generate high-FPS video, and automate AI-powered video processing workflows on macOS, Linux, and Windows.
Upscale. Interpolate. Enhance.
PixelFlow is an open-source command-line tool for:
- AI video upscaling
- AI image upscaling
- Video frame interpolation
- 30 FPS to 60 FPS conversion
- 60 FPS to 120 FPS conversion
- Video enhancement and restoration
- Batch video processing
- Automated video workflows
Powered by:
- Real-ESRGAN for image and video upscaling
- RIFE for frame interpolation and FPS boosting
- FFmpeg for video decoding, encoding, and media processing
Ideal for content creators, developers, video engineers, AI enthusiasts, and anyone looking to enhance videos using open-source AI models.
PixelFlow is a Python 3.12+ package, published on PyPI as pixelflow-cli (the
import package and the command are both pixelflow). For everyday use, install
it as an isolated CLI with pipx:
pipx install pixelflow-cliOr into an environment with pip:
pip install pixelflow-cliThen provision the external tools (FFmpeg, Real-ESRGAN, RIFE) for your platform:
pixelflow init
pixelflow doctor # verify everything is readydoctor prints a report like:
FFmpeg OK
FFprobe OK
Real-ESRGAN OK
RIFE OK
Backend OK ncnn
Platform OK macOS arm64
Temp directory OK
Common use cases include:
- Upscaling 720p videos to 1080p or 4K
- Converting 24 FPS, 30 FPS, or 60 FPS videos to higher frame rates
- Enhancing anime, gameplay recordings, screen recordings, and archived footage
- Automating video enhancement pipelines from the terminal
# Upscale a single image
pixelflow upscale-image input.png -o output.png
# Upscale a video 2× (audio preserved)
pixelflow upscale-video input.mp4 -o output.mp4 --scale 2
# Interpolate to 60 fps
pixelflow interpolate input.mp4 -o output.mp4 --fps 60
# Upscale and interpolate in one pass
pixelflow enhance-video input.mp4 -o output.mp4 --scale 2 --fps 60State (downloaded binaries, models, cache) lives in an OS-appropriate
application directory; override it with the PIXELFLOW_HOME environment
variable. See docs/SPEC.md for the full specification.
# Create an environment and install with dev extras
python -m venv .venv && source .venv/bin/activate
pip install -e ".[dev]"
# Quality gate
ruff check .
black --check .
pytestThe package builds with hatchling into a single
platform-independent wheel (the heavy AI binaries are fetched at runtime by
pixelflow init, not bundled).
Automated (recommended). Pushing a v* tag triggers
.github/workflows/publish.yml, which builds
the distributions and publishes them via PyPI
Trusted Publishing (OIDC — no API
token stored in the repo). One-time setup: add a trusted publisher on PyPI for
this repo + the publish.yml workflow and a pypi environment.
# Bump version in pyproject.toml + update CHANGELOG.md, then:
git tag -a v0.1.1 -m "PixelFlow 0.1.1" && git push origin v0.1.1Manual. To build and upload yourself:
rm -rf dist/
python -m build # produces dist/*.tar.gz and dist/*.whl
python -m twine check dist/*
python -m twine upload dist/* # username: __token__, password: <pypi token>Produce a self-contained executable per platform:
pyinstaller --onefile --name pixelflow pixelflow/cli.pyBuild on each target OS (cross-compilation is not supported) to produce:
pixelflow-macos-arm64
pixelflow-linux-x64
pixelflow-windows-x64.exe
Publish a formula to a tap that downloads the release tarball and installs the
pixelflow entry point:
class Pixelflow < Formula
desc "Cross-platform AI video enhancement CLI"
homepage "https://github.com/pixelflow/pixelflow"
url "https://files.pythonhosted.org/.../pixelflow-0.1.0.tar.gz"
sha256 "<sha256>"
depends_on "python@3.12"
# ... resource/install blocks ...
endThen brew install pixelflow/tap/pixelflow.
Submit a manifest to
microsoft/winget-pkgs referencing
the PyInstaller .exe release asset, then winget install PixelFlow.PixelFlow.
PixelFlow is an open-source AI video enhancement project focused on video upscaling, image upscaling, frame interpolation, and cross-platform video processing.
See LICENSE.