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name: model-inference description: "Set up iGAN pretrained DCGAN artifacts, choose model zoo entries, build sample-generation commands, and use dcgan_theano.Model API facts safely." disable-model-invocation: true metadata: disco-role: operating license: MIT

model-inference

Use this sub-skill when the task is about loading or selecting iGAN pretrained DCGAN models, planning model artifact downloads, building the standalone sample command, or understanding the dcgan_theano.Model inference API without opening the source checkout.

Route here

• Choose among the iGAN pretrained DCGAN model zoo entries.
• Plan where a *.dcgan_theano model file should live before running iGAN.
• Build a safe command for random sample generation from a pretrained model.
• Diagnose missing model artifacts before launching GPU/Theano code.
• Explain dcgan_theano.Model construction, generation, transforms, and output shapes.
• Preserve model URL and target-path evidence without performing network access.

Route elsewhere

• Interactive PyQt4 drawing, sliders, candidate panes, ShadowDraw UI, or launch flags belong in interactive-ui.
• Color-map, mask, edge-map, or headless constrained generation workflows belong in constraint-generation.
• Image-to-latent projection, AlexNet artifacts, cnn, opt, or cnn_opt solvers belong in image-projection.
• Dataset creation, HDF5 files, DCGAN training, batchnorm estimation, predictor training, and model packing belong in training-data.

Core facts

• The inference model type used by this repo is dcgan_theano.
• The default model file convention is ./models/<model_name>.dcgan_theano.
• The sample workflow emits a grid image, defaulting to <model_name>_dcgan_theano_samples.png.
• The documented sample run generates 196 images in batches of 49, then arranges them as a 14 x 14 grid.
• Full native execution assumes a legacy Python2-era Theano CUDA/cuDNN stack plus OpenCV and a downloaded model file. Command building and URL planning are safe dry-run operations and do not validate GPU execution.
• All bundled scripts in this sub-skill are side-effect safe by default: they do not import Theano, train models, use a GPU, or download files.

Model zoo names

Use one of these canonical pretrained names unless you intentionally add a new compatible config and model artifact:

Model name	Channels	Notes
outdoor_64	3	64x64 landscape model.
church_64	3	64x64 LSUN church model.
handbag_64	3	64x64 handbag model.
shoes_64	3	64x64 shoe-photo model.
hed_shoes_64	1	64x64 shoe-sketch/HED model; often paired with UI shadow mode.

For architecture and transform details, read references/api-reference.md. For model setup and sample workflows, read references/model-workflows.md.

Standard workflow

1. Select the model name from the model zoo.
2. Use the artifact planner to confirm the public URL and expected target path.
3. Download or stage the model artifact outside the helper if network access and storage are explicitly allowed.
4. Build the sample-generation command using the command builder.
5. If a legacy runtime is available, run the generated command with appropriate THEANO_FLAGS; otherwise keep the command as a reproducible handoff.
6. If native execution fails, consult references/troubleshooting.md and the root troubleshooting reference when the issue is cross-cutting.

Bundled helpers

• scripts/igan_artifact_urls.py prints the model artifact URL, expected target path, optional preview-sample URLs, and a missing/present status check. It does not download or modify files.
• scripts/build_model_command.py prints a reproducible command and environment plan equivalent to the standalone sample script. It can also report whether the planned model file is currently present.

Example dry plan for a missing artifact:

python sub-skills/model-inference/scripts/igan_artifact_urls.py outdoor_64 --check-existing

Example dry sample command with explicit paths:

python sub-skills/model-inference/scripts/build_model_command.py \
  --model_name shoes_64 \
  --model_file models/shoes_64.dcgan_theano \
  --output_image outputs/shoes_grid.png \
  --check-model

Validation signals

A healthy dry-run handoff includes:

• model_name matches a known model-zoo/config entry or the user explicitly allowed a custom name.
• model_type is dcgan_theano unless the task is about extending the backend.
• The model target ends with .dcgan_theano and follows the default convention unless a custom file is supplied.
• The command includes THEANO_FLAGS for GPU use or clearly states that flags were intentionally omitted.
• --check-model reports present before actual generation, or reports missing with the artifact planner command needed to obtain the URL.

Native execution caveat

The original sample workflow is a valuable native verification candidate, but it is not a safe default check. It imports Theano, compiles a generator, loads a pickle-like model file, uses CUDA/cuDNN in the documented setup, imports OpenCV, and writes an image. Treat successful dry command generation as static planning, not proof that the legacy model can run on the current machine.

Troubleshooting first steps

• Missing model file: run the artifact planner and stage the exact <model_name>.dcgan_theano target.
• Unknown model name: compare with the model zoo table and the config table in references/api-reference.md.
• Import or compile failures: check the legacy stack matrix in references/troubleshooting.md.
• Color/channel surprises for hed_shoes_64: verify that downstream workflows expect a one-channel model and route UI shadow behavior to the UI sub-skill.

Output contract for future agents

When asked to produce a model-inference handoff, return the chosen model name, model type, planned model file, artifact URL and target, command/env plan, expected output image, whether the file exists, whether native execution was actually attempted, and unresolved runtime blockers. Do not claim a download, GPU compile, or sample image was produced unless that command was explicitly run and verified.