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name: unpaired-inference description: "Guide CycleGAN-Turbo unpaired translation with pretrained day/night/rain/clear models and custom checkpoints." disable-model-invocation: true metadata: disco-role: operating license: MIT

Unpaired Inference

Use this sub-skill for CycleGAN-Turbo unpaired image translation when the task is to run or plan inference with:

• pretrained day_to_night, night_to_day, clear_to_rainy, or rainy_to_clear models;
• a custom CycleGAN-Turbo checkpoint selected by --model_path;
• prompt, direction, image preprocessing, precision, output naming, CUDA, xformers, or checkpoint-download troubleshooting for unpaired translation.

Do not use this sub-skill for Pix2Pix-Turbo paired edge/sketch/custom paired checkpoint inference; route those tasks to paired-inference. Do not use it for dataset layout, training, validation metrics, or checkpoint creation; route those tasks to training.

Start here

1. Choose pretrained or custom mode from CLI and API reference.
2. Build a safe command with the bundled command helper before running source-checkout inference.
3. Follow a pretrained or custom recipe in workflows.
4. If a run fails before producing the output image, use troubleshooting to classify the failure before changing prompts, directions, image sizes, or the runtime environment.

Operating rules

• Pretrained model names already carry their caption and direction in the model constructor; do not pass --prompt or --direction with --model_name.
• Custom checkpoint paths require both --prompt and --direction; use a2b for source-domain A to target-domain B and b2a for the reverse direction.
• The source inference script requires CUDA execution and enables xformers memory-efficient attention; the bundled helper only validates and prints commands, it does not run the model.
• The output file uses the input image basename inside --output_dir; choose a unique output directory or basename when comparing multiple runs.