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name: training description: "Guide Pix2Pix-Turbo and CycleGAN-Turbo training, dataset validation, safe example downloads, checkpoints, metrics, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

img2img-turbo Training

Use this sub-skill when a user wants to train or fine-tune img2img-turbo models, validate paired or unpaired training data, acquire the small documented example datasets, reason about training checkpoints/metrics, or debug training launches.

Do not run full training, model inference, dataset downloads, or metric model downloads by default. Start with data validation and command construction; ask before launching expensive CUDA jobs or network actions.

Route by task

• Paired Pix2Pix-Turbo training: use data formats for train_A/train_B plus prompt JSON requirements, then use training workflows for accelerate launch patterns.
• Unpaired CycleGAN-Turbo training: use data formats for train_A/train_B, test_A/test_B, and fixed prompt files, then use training workflows for accelerate launch patterns.
• Dataset validation before training: run the bundled dataset validator. It uses only the filesystem and JSON parsing; it does not import tokenizers, models, CUDA, or metric packages.
• Example data acquisition: use the bundled safe downloader only after user approval. It requires --dataset, --output-dir, and --yes before any network action.
• Training failures: consult training troubleshooting, especially for prompt mismatches, missing fixed prompts, W&B/offline logging, FID/LPIPS/DINO dependencies, xformers/CUDA, accelerate ports/processes, NCCL_P2P_DISABLE, and storage growth.
• Inference with a trained checkpoint: route Pix2Pix-Turbo checkpoint usage to paired-inference and CycleGAN-Turbo checkpoint usage to unpaired-inference. This sub-skill only explains the checkpoint handoff.

Safe first steps

From this training/ sub-skill directory, validate the dataset before any expensive launch:

python scripts/validate_training_dataset.py --mode paired --dataset-folder data/my_fill50k
python scripts/validate_training_dataset.py --mode unpaired --dataset-folder data/my_horse2zebra

For documented example datasets, show the action first; only run with explicit user approval:

bash scripts/download_example_dataset.sh --help
bash scripts/download_example_dataset.sh --dataset fill50k --output-dir data --yes
bash scripts/download_example_dataset.sh --dataset horse2zebra --output-dir data --yes

Operating constraints

• Training is a CUDA-first workflow. Source training code constructs CUDA discriminators/metrics and does not have a verified CPU substitute for truthful training.
• Full training and full model inference were intentionally not used as default verification checks because they are long-running, GPU-heavy, write checkpoints/logs, and may download pretrained or metric assets.
• Keep runtime guidance self-contained: use the references here instead of reopening original repository training documents or scripts.