---

name: "lora-training" description: "Routes HunyuanVideo-I2V LoRA training tasks, including DeepSpeed launcher setup, training hyperparameters, resume/init checkpoints, and the dataset expectations that come from the latent-extraction workflow." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

LoRA Training

Use this sub-skill when the user wants to train, resume, or debug a LoRA effect model for HunyuanVideo-I2V.

What Belongs Here

• Running or adapting the DeepSpeed LoRA launcher.
• Choosing training hyperparameters and memory-saving flags.
• Resuming from an experiment directory or checkpoint.
• Checking whether the processed latent dataset is in the right layout.
• Understanding where the final pytorch_lora_kohaya_weights.safetensors file appears.

What Does Not Belong Here

• Building the latent dataset from raw videos. Use ../data-preparation/SKILL.md.
• Single-image inference or xDiT generation. Use ../inference/SKILL.md.

Read First

• references/workflows.md for the canonical train/resume flow.
• references/cli-reference.md for the verified training flags.
• references/api-reference.md for the data loader and helper functions that govern the training schema.
• references/troubleshooting.md for DeepSpeed, checkpoint, and dataset issues.
• ../../references/checkpoints.md because training loads the same checkpoints as inference.

Bundled Script

• scripts/run_lora_training.py — build or execute the canonical DeepSpeed command safely. It lives under $SKILL_ROOT; invoke it from $CHECKOUT_ROOT with --repo-root "$CHECKOUT_ROOT". Use --dry-run first. Install requirements-optional.txt only when this GPU workflow is actually selected.

Typical Workflow

Commands run from the real checkout root; $SKILL_ROOT is not the checkout:

1. Confirm the checkpoint tree with $SKILL_ROOT/scripts/check_checkpoint_layout.py --ckpts-root "$CHECKOUT_ROOT/ckpts" --mode train.
2. Confirm the processed latent dataset layout with $SKILL_ROOT/sub-skills/data-preparation/scripts/check_dataset_layout.py.
3. Print the canonical DeepSpeed command with $SKILL_ROOT/sub-skills/lora-training/scripts/run_lora_training.py --repo-root "$CHECKOUT_ROOT".
4. Add --execute only when the GPU memory and dataset paths are ready.
5. Inspect the run directory for args.json, logs, and the LoRA checkpoint.

Constraints to Remember

• The README documents 79GB for 360p LoRA training.
• task-flag and output-dir are required by the training script.
• The launcher expects the processed latent dataset, not raw videos.
• The base model, VAE, and text encoders are loaded before the first training step.