Fast Style Transfer Repo Skill

Use this skill when a task involves Logan Engstrom's TensorFlow Fast Style Transfer repository, real-time neural style transfer, the bundled training runtime, the bundled image stylization runtime, the bundled video stylization runtime, transform-network checkpoints, VGG19 .mat assets, or COCO-style training image directories.

This repo is script-oriented rather than an installable Python package. This skill is self-contained: the workflow details, adapted runtime wrappers, CLI flags, validation helpers, troubleshooting, and provenance needed for normal use are bundled here.

Read first

• Read references/repo-provenance.md before deciding whether this skill matches a checkout or should be refreshed.
• Read references/setup-and-assets.md when preparing TensorFlow dependencies, VGG19 .mat, COCO training images, pretrained checkpoints, or ffmpeg/moviepy assets.
• Read references/troubleshooting.md for cross-cutting install/import, TensorFlow version, asset download, checkpoint, and backend failures.
• Run or adapt scripts/inspect_fast_style_transfer.py for a safe dependency/source-surface check. It does not download assets, restore checkpoints, train, or process media.

Route by task

User task	Read
Train a style transform network, choose loss weights, validate style/train/VGG inputs, or plan a long GPU run	sub-skills/training/SKILL.md
Stylize one image or a directory of images from a trained checkpoint, debug dimensions, output paths, devices, or checkpoint restore	sub-skills/image-stylization/SKILL.md
Stylize a video from a trained checkpoint, check moviepy/ffmpeg, plan batch/device settings, or debug video output	sub-skills/video-stylization/SKILL.md

Public prerequisites

The historical repository documentation names TensorFlow-era dependencies. For modern inspection and command planning, verify a Python environment with:

python - <<'PY'
import tensorflow as tf, numpy, scipy, imageio, PIL, moviepy
print('tensorflow', tf.__version__)
print('gpu devices', tf.config.list_physical_devices('GPU'))
PY

For practical full training or large video/image batches, use a compatible GPU TensorFlow stack. CPU can validate parsers and run small image inference, but full training on CPU is normally impractical.

Core runtime surfaces:

• Training script: python sub-skills/training/scripts/run_training.py --checkpoint-dir ... --style ... --train-path ... --vgg-path ...
• Image stylization script: python sub-skills/image-stylization/scripts/run_image_stylization.py --checkpoint ... --in-path ... --out-path ...
• Video stylization script: python sub-skills/video-stylization/scripts/run_video_stylization.py --checkpoint ... --in-path ... --out-path ...
• Bundled runtime modules used by those scripts: scripts/fast_style_transfer_runtime/transform.py, scripts/fast_style_transfer_runtime/optimize.py, scripts/fast_style_transfer_runtime/vgg.py, and scripts/fast_style_transfer_runtime/utils.py.

The generated validation helpers in this skill check paths, numeric options, media shapes, optional dependencies, and command readiness without running expensive neural style transfer.

Asset model

This skill does not bundle model weights, VGG networks, COCO data, checkpoints, or example media. Treat these as user-supplied runtime assets:

• VGG19 .mat for training losses.
• A directory of training content images such as COCO train2014 or an equivalent image corpus.
• A style image used to train a new transform network.
• A trained Fast Style Transfer checkpoint for image or video stylization.
• ffmpeg/moviepy support for video workflows.

Minimum safe checks

Before a costly run, prefer these safe checks:

python scripts/inspect_fast_style_transfer.py --json
python sub-skills/training/scripts/validate_training_inputs.py --help
python sub-skills/image-stylization/scripts/validate_image_stylization_inputs.py --help
python sub-skills/video-stylization/scripts/validate_video_stylization_inputs.py --help

When a local checkout is available and you want source-surface inspection, pass its directory to the root inspector:

python scripts/inspect_fast_style_transfer.py --repo-root /path/to/fast-style-transfer --json

Do not use these checks as proof that pretrained checkpoints, VGG assets, COCO data, GPU speed, or full video encoding work; those require separate runtime assets and backend verification.

Common decisions

• Need a checkpoint? Use the training sub-skill if creating one; use image/video sub-skills if consuming one.
• Need the fastest path to stylize a still image? Use image-stylization; pass /cpu:0 only for small or debugging runs, and a GPU device for throughput when TensorFlow GPU is configured.
• Need to process mixed-size image directories? Use image-stylization and either normalize image dimensions first or enable the script's different-dimensions grouping path.
• Need video output? Use video-stylization; verify checkpoint, moviepy, ffmpeg, and device/batch choices before processing frames.
• Need downloads? Read setup/assets guidance; do not run network or large-data downloads implicitly.

Do not use this skill when

• The task is about a different style-transfer implementation, PyTorch neural style transfer, diffusion image generation, or a generic TensorFlow tutorial unrelated to this repository.
• The user needs legal/commercial licensing advice beyond the repository's public research-use notice and citation metadata.
• The task asks to modify the repository source code rather than use its public workflows; use a repository-maintenance workflow instead.