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name: wiseodd-generative-models description: "Routes the legacy Generative Models script collection for GAN, VAE, RBM, and Helmholtz Machine MNIST workflows, catalog lookup, and compatibility troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Unlicense

Generative Models

Use this repo skill for the legacy Generative Models script collection: GAN, VAE, RBM, and Helmholtz Machine examples implemented as standalone MNIST training scripts in TensorFlow, PyTorch, and NumPy.

This repository is not an installable Python package and does not expose a CLI. Treat it as a catalog of legacy research scripts plus compatibility guidance for future checkouts of the same repo.

Quick routing

User asks for...	Route to
GAN variants, adversarial losses, WGAN/WGAN-GP, InfoGAN, DiscoGAN, DualGAN, ALI/BiGAN, GibbsNet	sub-skills/gan/SKILL.md
VAE, CVAE, denoising VAE, adversarial autoencoder, adversarial variational Bayes	sub-skills/vae/SKILL.md
Binary Restricted Boltzmann Machines, CD vs PCD, Bernoulli visible/hidden units	sub-skills/rbm/SKILL.md
Binary Helmholtz Machine, wake-sleep algorithm, recognition/generative weights	sub-skills/helmholtz-machine/SKILL.md
Exact family-to-script catalog, source artifact labels, framework coverage	references/model-catalog.md
Legacy dependency stack and modern incompatibilities	references/compatibility.md
Cross-family failures and recovery order	references/troubleshooting.md

Minimal compatibility check

Run the bundled diagnostic helper before telling a user that an unmodified legacy script will run:

python scripts/check_legacy_stack.py

Use --strict when a workflow requires an unchanged script run and warnings should become a failing check. Use --repo-root <checkout> only when the user is actively working in a checkout and wants the helper to inspect whether MNIST_data/ and the expected family directories exist.

For catalog lookup without reopening the source tree, run:

python scripts/model_catalog.py --family gan
python scripts/model_catalog.py --model wgan-gp

Operating assumptions

• The historical dependency file targets a very old stack: Python 3.5.1-era NumPy/SciPy/scikit-learn/matplotlib plus Keras 1.1.1, while TensorFlow and PyTorch were installed separately.
• Most scripts load MNIST through tensorflow.examples.tutorials.mnist.input_data, which is absent from modern TensorFlow 2.x installs.
• Several PyTorch scripts use old scalar logging patterns such as loss.data[0], which fail on modern PyTorch.
• RBM, Helmholtz, and some GAN/VAE scripts use removed NumPy aliases such as np.float or np.int.
• Generated sample images are expected under an out/ directory relative to the script's working directory in a source checkout.

When not to use this skill

• Do not use this skill for modern diffusion, Stable Diffusion, Diffusers, LoRA training, or image-generation services unless the task explicitly compares them with these classic MNIST scripts.
• Do not use it for generic PyTorch/TensorFlow installation help unrelated to this repository's legacy script patterns.
• Do not claim this repository provides packaged import APIs, console entry points, reusable datasets, tests, or production training pipelines.

Provenance and refresh

Read references/repo-provenance.md before deciding whether this skill is current for a new checkout. Refresh the skill if the commit, script inventory, dependency notes, or family layout differs from the provenance snapshot.