---

name: image-translation description: "Use Keras-GAN CycleGAN, DiscoGAN, and Pix2Pix image-to-image translation workflows safely: model APIs, dataset layout checks, adaptation guidance, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Image Translation: CycleGAN, DiscoGAN, Pix2Pix

Use this sub-skill when a task involves Keras-GAN image-to-image translation with CycleGAN, DiscoGAN, or Pix2Pix. Treat the upstream project as a stale collection of standalone educational scripts, not an importable package or maintained training framework.

Route here for

• Unpaired two-domain translation such as apple2orange, summer2winter_yosemite, horse2zebra, or other A/B domain folders.
• Paired side-by-side conditional translation such as facades or edges2shoes.
• Inspecting or adapting the class APIs CycleGAN, DiscoGAN, and Pix2Pix, their DataLoader helpers, PatchGAN output shapes, sample-image behavior, or stale Keras/TensorFlow runtime assumptions.
• Safe dataset-layout validation before any constructor smoke test or training attempt.

Route elsewhere

• MNIST or latent-vector generators such as GAN, DCGAN, CGAN, AC-GAN, InfoGAN, BiGAN, CoGAN, LSGAN, WGAN, or WGAN-GP.
• Specialized restoration/domain-adaptation workflows such as Context Encoder, SRGAN, or PixelDA.
• Network dataset acquisition or long training jobs unless the user explicitly authorizes them; the original download scripts are reference-only.

Start with these bundled resources

1. Choose the workflow and API surface in references/model-api-reference.md.

2. Validate local data with the safe helper before running any model code:

   python sub-skills/image-translation/scripts/check_dataset_layout.py \
     --dataset-root datasets/apple2orange \
     --workflow cyclegan \
     --min-files 1 \
     --check-images
   

   Replace datasets/apple2orange with the dataset directory visible to the caller. The helper performs no network access, imports no Keras models, and does not train.

3. Use references/data-formats.md for expected folder layouts, split names, side-by-side image conventions, and preprocessing assumptions.

4. Use references/workflows.md for bounded adaptation, constructor-smoke, dataset-name, image-resolution, and sample-output workflows.

5. Use references/troubleshooting.md when imports, SciPy image utilities, empty batches, PatchGAN labels, or paired/unpaired data conventions fail.

Safety defaults

• Do not run the original network download scripts automatically.
• Do not run full training as a verification step; prefer dataset checks and constructor/import smoke checks in a pinned legacy environment.
• Keep generated outputs in caller-controlled directories such as images/<dataset_name>/<epoch>_<batch>.png; note that the educational scripts create saved_model/ directories in the checkout but do not save weights by default.
• If adapting code, preserve the disc_patch = (img_rows / 2**4, img_rows / 2**4, 1) pattern and update it whenever image resolution changes.