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name: img2img-turbo description: "Route img2img-turbo paired and unpaired image-to-image translation, training, dataset validation, and CUDA troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

img2img-turbo

Use this skill for the GaParmar/img2img-turbo source checkout. It covers two model families built on Stable Diffusion Turbo: paired Pix2Pix-Turbo workflows and unpaired CycleGAN-Turbo workflows.

Start here

1. Read repository provenance to confirm the skill matches the current checkout.

2. Verify the Python environment with the bundled checker. Pass the source-checkout path with --repo-root when you are not already inside the repo root:

   python scripts/check_environment.py --repo-root /path/to/img2img-turbo --scope all --check-help --require-cuda
   

   Use --scope paired, --scope unpaired, or --scope training if you only need one route.

3. If you are importing modules directly from the checkout, add src/ to PYTHONPATH; the repository is a source checkout, not a packaged distribution.

4. Route to the focused sub-skill below before writing long commands or code.

Route by task

• Paired edge/sketch/custom-checkpoint inference or paired Gradio demos → sub-skills/paired-inference/SKILL.md
• Unpaired day/night/rain/clear inference or custom CycleGAN checkpoints → sub-skills/unpaired-inference/SKILL.md
• Fill50K / horse2zebra training, dataset validation, and checkpoint handoff → sub-skills/training/SKILL.md

Shared references

• Read installation and environment when you need to create or repair a Python/CUDA environment.
• Read model overview when choosing between paired and unpaired routes or checking checkpoint expectations.
• Read troubleshooting for cross-cutting import, CUDA, dependency, and source-checkout failures.
• Read repository provenance before deciding whether to refresh this skill for a different checkout.

Shared script

• Run scripts/check_environment.py for a safe no-download smoke check. It can verify source imports, CUDA visibility, and source CLI --help checks without launching models or servers.

Operating notes

• The source scripts construct CUDA-oriented models and do not have a verified CPU fallback for actual image generation or training.
• Keep runtime guidance self-contained. Use the bundled references and scripts instead of pointing future agents back to the original checkout paths.
• Do not assume pip install . or pip install -e . works here; the repository uses source files under src/ and the runtime is driven by the checked-out scripts plus the verified Python environment.
• Full checkpoint downloads, long training runs, and Gradio server launches are deliberate actions, not default smoke checks.