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name: translation-workflows description: "Routes CUT, FastCUT, and SinCUT training, testing, checkpoint loading, and result inspection workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

translation-workflows

Use this sub-skill when the task is about the repository's main image-to-image translation flows: training CUT or FastCUT, testing a trained model, loading checkpoints, or running the single-image SinCUT variant.

Read this when

• The user wants to train CUT or FastCUT on an unaligned dataset.
• The user wants to test a checkpoint and inspect HTML or visdom outputs.
• The user wants the single-image SinCUT path with one image per domain.
• The user asks which --model, --CUT_mode, or option family to use.
• The user needs to understand checkpoint and results directory conventions.
• The user wants a quick runtime/import smoke check for the repo package.

What this sub-skill covers

• train.py and test.py as the public entry points.
• options/base_options.py, options/train_options.py, and options/test_options.py.
• models/cut_model.py, models/sincut_model.py, models/networks.py, and models/base_model.py.
• data/base_dataset.py, data/unaligned_dataset.py, and data/singleimage_dataset.py as they affect these workflows.
• util/visualizer.py and util/html.py for logging and output artifacts.
• Pretrained checkpoint loading through --checkpoints_dir, --name, --epoch, and --pretrained_name.

What this sub-skill does not cover

• Dataset conversion, Cityscapes preprocessing, and image pairing. Read ../data-preparation/ for those tasks.
• Launcher presets and tmux behavior. Read ../experiment-launchers/ for those tasks.
• Legacy CycleGAN as a first-class route. It is mentioned only in troubleshooting.

Use the bundled helper

Run scripts/check_runtime.py when you want a safe import and backend smoke check from a repo root:

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

That helper is the quickest way to confirm the installed runtime can import the repo modules that these workflows use.

Core workflow map

• CUT/FastCUT training: choose --model cut and set --CUT_mode CUT or --CUT_mode FastCUT.
• CUT/FastCUT testing: use test.py with the trained name, checkpoint epoch, and results directory.
• SinCUT training: choose --model sincut and use a dataset with one image in trainA and one image in trainB.
• SinCUT testing: use the same --model sincut checkpoint and the single-image dataset layout.
• Pretrained inference: use --name, --epoch, --phase, and --results_dir to point at the saved checkpoint and output tree.

Read next

• references/cli-reference.md for the verified flag families and their interactions.
• references/workflows.md for end-to-end command sequences and output conventions.
• references/troubleshooting.md for missing-dependency, checkpoint, output-path, and legacy-model failures.

Common routing choices

• If the task is about train.py or test.py options, stay here.
• If the task is about data folders or conversion scripts, route to ../data-preparation/.
• If the task is about python -m experiments presets, route to ../experiment-launchers/.

Quick reminders

• --gpu_ids -1 is the CPU path.
• --direction AtoB is the default; BtoA is available when the dataset or experiment needs it.
• --preprocess, --load_size, and --crop_size control input resizing and cropping.
• --results_dir is only for testing; training writes checkpoints and web outputs under --checkpoints_dir.
• The upstream --model test example is stale in this checkout because there is no models/test_model.py.