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name: i-gan description: "Operate the legacy iGAN repository for interactive GAN image generation, constrained synthesis, image projection, model artifacts, and DCGAN training workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

i-gan

Use this repo skill when a task involves the iGAN / Interactive GAN codebase for GAN-based image generation, visual manipulation on the image manifold, legacy Theano DCGAN models, PyQt4 interaction, constraint-map synthesis, latent projection, or training iGAN-style DCGAN artifacts.

This skill is intentionally self-contained: use the bundled references and helpers here instead of reopening the original README or shell scripts. The helpers are dry-run planners and validators by default; they do not download large artifacts, import Theano/PyQt4, train models, launch a GUI, or use a GPU.

Fast route map

User task	Read this
Download-plan a pretrained model, choose model zoo names, build random sample commands, or inspect dcgan_theano.Model facts	sub-skills/model-inference/SKILL.md
Generate images from color, mask, and edge constraints without the GUI	sub-skills/constraint-generation/SKILL.md
Launch or explain the interactive drawing UI, controls, shortcuts, ShadowDraw, or AverageExplorer	sub-skills/interactive-ui/SKILL.md
Project an input image into latent space using cnn, opt, or cnn_opt solvers	sub-skills/image-projection/SKILL.md
Plan datasets, HDF5 creation, DCGAN training, batchnorm, predictor training, packing, or custom model configs	sub-skills/training-data/SKILL.md

Setup / installation stance

The repository does not publish package metadata for pip install; operate it as a script checkout with repo-local modules. If a user asks for installation, clone or unpack the iGAN source, stage workflow-specific artifacts, and prepare only the legacy dependencies required by the chosen route. The minimal safe verification command is the bundled preflight helper below, not a Theano import.

What to verify first

1. Read references/repo-provenance.md when deciding whether this skill matches the current checkout.

2. Run the checkout preflight helper before native execution:

   python scripts/check_igan_checkout.py --repo-root <iGAN-checkout> --format text
   

3. If the task asks to run generation, UI, projection, or training, confirm that the user has explicitly supplied or authorized the legacy runtime, model/data artifacts, and hardware/display requirements listed below.

4. Use the nearest sub-skill helper to build a command or validate inputs before launching original heavy code.

Runtime reality check

The original repository is a legacy script-based project, not an installable Python package. Its native workflows assume a Python2-era stack with Theano, CUDA/cuDNN, OpenCV, PyQt4/qdarkstyle for the UI, Lasagne/SciPy/PIL for projection, and Fuel/h5py/tqdm for training/data preparation. Modern Python and modern CUDA hosts often need compatibility work. Do not treat static helper success as proof that native Theano execution works.

The bundled helpers are safe for planning and validation:

• scripts/check_igan_checkout.py inspects a checkout for expected source files, optional artifact files, GPU/display signals, and likely blockers without importing repo modules.
• Sub-skill command builders construct reproducible shell commands and THEANO_FLAGS plans without executing them.
• URL planners print artifact URLs and target paths without downloading.
• Input validators inspect file presence and lightweight image headers without requiring OpenCV.

Common operating sequence

1. Identify the intended workflow and load the matching sub-skill.
2. Run or read the sub-skill helper to produce a dry command/data/artifact plan.
3. Check references/troubleshooting.md for cross-cutting dependency, model, display, and backend failures.
4. Stage any model, AlexNet, or dataset artifacts only after network and disk use are approved.
5. If a compatible legacy runtime exists, run the generated command and record whether native execution actually happened.
6. If native execution is unavailable, return a precise handoff: command plan, missing artifacts/dependencies/backends, and the sub-skill references used.

When not to use this skill

• Use a modern Diffusers/Stable Diffusion/ComfyUI skill when the task is about diffusion pipelines, LoRA, schedulers, or node graphs rather than this iGAN Theano codebase.
• Use a generic computer-vision or PyTorch training skill when the user is not specifically working with iGAN-style DCGAN scripts or artifacts.
• Do not use this skill as proof of native runtime verification; it provides operating guidance and safe preflight helpers unless a legacy environment is separately prepared and tested.

Output contract

When handing work back to a user or another agent, include the selected route, model/dataset/artifact names, command or validation plan, artifact status, whether any native command was actually run, the expected outputs, and remaining legacy runtime blockers. Never claim that downloads, GPU compilation, UI launch, training, or image generation occurred unless those commands were explicitly run and their outputs were checked.