---

name: keras-gan description: "Use Keras-GAN legacy Keras/TensorFlow GAN scripts for image generation, image-to-image translation, inpainting, PixelDA, and SRGAN workflows with safe inspection and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: MIT

Keras-GAN Repo Skill

Use this repo skill when a task involves the Keras-GAN repository: a stale but useful collection of standalone educational Keras implementations of generative adversarial networks. The repository is not an installable Python package; treat each model directory as a standalone script family and use the bundled helpers here to inspect, validate, or adapt it safely.

Do not launch the original scripts' __main__ defaults as routine checks. Many defaults run thousands of epochs, may download Keras datasets or external archives, and write relative images/ or saved_model/ outputs.

First steps

1. Read references/compatibility-and-install.md before running any Keras code. The scripts target legacy standalone Keras 2.x with TensorFlow 1.x behavior.

2. Run the bundled runtime checker when diagnosing an environment:

   python scripts/check_legacy_runtime.py
   python scripts/check_legacy_runtime.py --json
   

3. Use references/model-catalog.md to choose the right model family.

4. Route to the smallest focused sub-skill below.

5. Use references/troubleshooting.md for cross-cutting install/import, stale dependency, output-path, data-download, and long-training failures.

6. Read references/repo-provenance.md before deciding whether this skill is current for a checkout.

Route by task

User task or signal	Use
Vanilla GAN, DCGAN, CGAN, AC-GAN, SGAN, InfoGAN, LSGAN, BGAN, BiGAN, Adversarial Autoencoder, CoGAN, DualGAN, WGAN, or WGAN-GP on MNIST-style arrays	sub-skills/mnist-generators/SKILL.md
Compare GAN variants, inspect class APIs, run static inventory, or smoke-test representative model constructors without training	sub-skills/mnist-generators/SKILL.md
CycleGAN, DiscoGAN, Pix2Pix, apple2orange, edges2shoes, facades, unpaired A/B domains, paired side-by-side image translation, PatchGAN label shapes	sub-skills/image-translation/SKILL.md
Validate image-translation dataset folders before training or adapting code	sub-skills/image-translation/scripts/check_dataset_layout.py via the image-translation sub-skill
CCGAN, ContextEncoder, context inpainting, CIFAR cats/dogs missing patches, PixelDA MNIST-to-MNIST-M adaptation, SRGAN 4x super-resolution, CelebA/VGG19 risk	sub-skills/domain-and-restoration/SKILL.md
Validate PixelDA cache files, SRGAN image directory, or restoration workflow output directories without importing Keras	sub-skills/domain-and-restoration/scripts/check_restoration_layout.py via the domain-and-restoration sub-skill

Operating constraints

• Use a legacy runtime for faithful execution: Python 3.7-era, TensorFlow 1.15.x, Keras 2.2.x, keras-contrib 2.0.x, NumPy 1.18.x, SciPy 1.2.x, Matplotlib, Pillow, scikit-image, h5py, and protobuf below 3.21 are the verified compatibility family.
• Modern Keras 3 or TensorFlow 2 errors are usually dependency-staleness issues, not evidence that the original GAN idea is invalid.
• Treat GPU as optional acceleration. The selected safe verification scope is CPU-compatible; full training can use GPUs only after the user accepts runtime, data, and output side effects.
• Original dataset download scripts and Keras dataset loaders can perform network access. Ask before running them when network is not already authorized.
• Keep all generated outputs in caller-controlled run directories. The source scripts write relative paths such as images/, images/<dataset_name>/, and saved_model/.

Safe verification patterns

• Static source inventory is preferred before Keras imports.
• Constructor smoke checks are acceptable only in a legacy-compatible runtime and should never call train(...).
• Dataset layout helpers are safe: they do not import Keras, download data, train models, or mutate datasets.
• Full native training loops are skip-expensive by default. Use one-epoch wrappers with synthetic or already-cached data only after the user accepts short compute and file writes.

When not to use this skill

• The user needs a maintained GAN framework, modern tf.keras, PyTorch GAN implementations, or production-grade training infrastructure rather than Keras-GAN's educational scripts.
• The task is Stable Diffusion, Diffusers, LoRA, ComfyUI, or modern image-generation pipelines unrelated to this repository.
• The task is repository maintenance unrelated to using/adapting the GAN scripts; use a Python repository-maintenance workflow instead.