---

name: inference-and-export description: "Run zi2zi checkpoint inference, style interpolation, transition GIF planning, and generator-only export from legacy TensorFlow 1.x checkpoints." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

zi2zi inference and export

Use this sub-skill when a task is about generating glyphs from a trained zi2zi checkpoint, interpolating between style labels, creating frame sequences or a GIF, or exporting generator-only variables for later reuse. It does not launch long inference jobs automatically; it explains the checkpoint contract and prints safe command templates.

Use this sub-skill when

• The user has a checkpoint directory and wants generated glyphs.
• The task mentions infer.py, export.py, model_dir, source_obj, embedding_ids, interpolate, steps, output_gif, uroboros, or exported generator weights.
• The user wants to know how inference chooses between single-style generation and random style selection.
• The user needs to diagnose checkpoint restore failures, output-image naming, or interpolation problems.

Route elsewhere

• Data rendering and .obj packaging go to data-preparation.
• Training, fine-tuning, or label shuffling go to training-and-model.
• Compatibility questions go to the root compatibility reference.

Core inference facts

• infer.py restores generator variables from a checkpoint and uses a source object file to provide images.
• If embedding_ids contains one integer, the model uses that style for every source image. If it contains multiple IDs, the script picks randomly from the list for each batch.
• interpolate=1 asks the model to linearly interpolate between style vectors. With uroboros=1, the interpolation path closes the loop by appending the first ID to the end of the chain.
• output_gif triggers GIF compilation from the rendered interpolation frames.
• export.py saves generator-only variables, which include the embedding table and generator weights.

Output conventions

• Normal inference writes files named inferred_%04d.png in the save directory.
• Interpolation writes frame_<src>_<dst>_step_<idx>.png files.
• GIF compilation expects those frame PNGs in the save directory and writes the named GIF beside them.
• Export uses TensorFlow checkpoint naming with a generator model prefix.

Recommended workflow

1. Verify the checkpoint directory contains a TensorFlow checkpoint state.
2. Decide whether you want one style ID, a random mix, or interpolation between two or more style IDs.
3. Confirm the source object file contains paired glyph records and that its batch size is compatible with the inference command.
4. Use the command planner to validate flags and paths before launching any long job.
5. Inspect outputs after the first few batches, especially if the chosen style labels or batch size do not match the training run.

Read inference-workflow.md for command shapes and evaluation steps. Read checkpoints-and-outputs.md for restore/export expectations and filename conventions. Read troubleshooting.md for checkpoint, batch-size, GIF, and source-object failures.

Bundled helper

• scripts/plan_zi2zi_inference.py validates inference, interpolation, and export arguments and prints the command that a future agent should run in a legacy zi2zi environment.

What not to promise automatically

• Do not promise restoration of a checkpoint without checking that the concrete checkpoint files exist and match the chosen hyperparameters.
• Do not promise a successful interpolation run unless embedding_ids contains at least two integers.
• Do not promise export success if the model directory contains only partial or mismatched checkpoint shards.