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name: training-and-model description: "Train, fine-tune, and understand zi2zi's TensorFlow 1.x conditional adversarial U-Net, experiment layout, losses, label shuffling, and checkpoint monitoring." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

zi2zi training and model

Use this sub-skill when a task is about zi2zi training, fine-tuning, model structure, loss terms, checkpoint management, or experiment diagnostics. It does not run long training automatically; it explains the legacy workflow and gives safe command planning and preflight checks.

Use this sub-skill when

• The user wants to train zi2zi from train.obj and val.obj.
• The user wants to fine-tune only a subset of labels or freeze the encoder.
• The task mentions train.py, experiment_dir, checkpoint, sample, logs, flip_labels, fine_tune, inst_norm, freeze_encoder, or loss names such as L1, constant, category, or total variation.
• The user needs to understand why d_loss saturates, why label shuffling was added, or how checkpoints and TensorBoard outputs are organized.

Route elsewhere

• Data rendering and .obj packaging go to data-preparation.
• Inference, interpolation, and export go to inference-and-export.
• Cross-cutting compatibility issues go to the root compatibility and troubleshooting references.

Core training model facts

• UNet is a pix2pix-like encoder/decoder generator with skip connections and a discriminator that predicts real/fake and style category.
• The model combines several losses: adversarial cheat loss, L1 loss, category loss, constant/encoding loss, and optional total variation loss.
• flip_labels=1 enables the no-target-source branch used for label shuffling and saturation recovery.
• freeze_encoder=1 excludes encoder variables from generator updates.
• fine_tune accepts a comma-separated list of integer labels, which the data provider uses to filter training and validation records.
• inst_norm=1 switches decoder normalization to conditional instance normalization instead of batch normalization.

Standard experiment layout

The README and source expect an experiment tree like this:

experiment/
  data/
    train.obj
    val.obj
  checkpoint/
  logs/
  sample/

train.py creates the checkpoint, log, and sample directories if they do not exist. Checkpoints are saved under a batch-size-specific model directory named experiment_<id>_batch_<batch>.

Recommended workflow

1. Confirm train.obj and val.obj are present and non-empty.
2. Choose --embedding_num large enough for the highest label in the data.
3. Decide whether you want batch norm or conditional instance norm.
4. For a normal full run, set batch size, learning rate, epoch count, and sample /checkpoint cadence deliberately rather than accepting defaults blindly.
5. Use --resume=1 only when you want to continue an existing experiment.
6. Use --fine_tune for label-subset adaptation and --flip_labels=1 when the discriminator saturates during later fine-tuning.
7. Watch TensorBoard logs and the sample/ images for collapse or label issues.

Read training-workflow.md for command shapes, preflight checks, and safe run-planning. Read model-architecture.md for architecture and loss details. Read troubleshooting.md for TensorFlow, checkpoint, OOM, and data-layout failures.

Bundled helper

• scripts/plan_zi2zi_training.py validates a training command and prints the intended legacy zi2zi invocation without launching the TensorFlow loop.

What not to promise automatically

• Do not promise a successful long training run without a compatible legacy TensorFlow environment, valid .obj files, and explicit runtime budget.
• Do not claim GPU verification from a CPU-only graph build.
• Do not infer checkpoint compatibility across different batch sizes, embedding sizes, or normalization settings without checking the variable names and shapes.