Contrastive Unpaired Translation

Use this repo skill for the CUT family of image-to-image translation workflows in this checkout: CUT, FastCUT, and SinCUT; dataset preparation and layout checks; and preset experiment command generation.

Start here

• Read references/repo-provenance.md when you need to confirm whether this skill matches the current checkout or before refreshing the skill.
• Use scripts/check_runtime.py when you want a quick import and CLI smoke check from a repo root.
• Read references/troubleshooting.md when smoke checks, checkpoint paths, legacy imports, or launcher commands misbehave.
• If you only want the main model workflows, go to sub-skills/translation-workflows/.
• If you need dataset conversion or folder-layout prep, go to sub-skills/data-preparation/.
• If you want preset command strings or launcher behavior, go to sub-skills/experiment-launchers/.

Install and smoke check

The checked code runs from a Python environment with a compatible PyTorch and torchvision build plus the small runtime dependencies used by the repo scripts.

Typical install shape:

1. Create an isolated Python environment.
2. Install a CUDA-capable or CPU-compatible torch/torchvision pair for your host.
3. Install the repository runtime dependencies from requirements.txt.
4. Add opencv-python-headless when you plan to use the dataset-preparation helpers that import cv2.

A minimal smoke check is:

python scripts/check_runtime.py --repo-root .

If you only need the CLI entry points, python train.py --help and python test.py --help are the quickest verified checks.

What this skill covers

• Training and testing CUT/FastCUT on unaligned datasets.
• Switching CUT mode between CUT and FastCUT.
• SinCUT single-image translation defaults and workflow notes.
• Loading checkpoints and writing results to checkpoints/ and results/.
• Visualizer/HTML output behavior and visdom usage.
• Dataset helpers for Cityscapes, aligned side-by-side pairs, and cat-face cropping.
• Launcher command presets for python -m experiments.

What this skill does not promise

• It does not promise the stale --model test example from upstream README text; this checkout has no models/test_model.py.
• It does not promote legacy CycleGAN as a first-class supported route. The legacy code is noted in troubleshooting only because it still exists in the checkout but has incomplete option wiring.
• It does not require or bundle network download scripts for large datasets.

Routes

sub-skills/translation-workflows/

Read this for CUT/FastCUT/SinCUT model selection, option families, checkpoint loading, output inspection, and the public training/test CLI.

sub-skills/data-preparation/

Read this for dataset directory structure, Cityscapes preparation, A/B pair assembly, aligned side-by-side exports, and grumpifycat-style image cropping.

sub-skills/experiment-launchers/

Read this for safe command generation, launcher presets, GPU ID selection rules, and the python -m experiments CLI shape.

Freshness check

Before you trust the route map, compare the current checkout against references/repo-provenance.md. If the commit or evidence paths changed, refresh this skill rather than assuming the guidance is still current.