---

name: training description: "Configure and launch LatentSync U-Net and SyncNet training with safe config, file-list, DDP, checkpoint, and VRAM guidance." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training

Route LatentSync training work through this sub-skill when the task involves choosing or adapting U-Net or SyncNet training configs, generating dataset file lists, rendering distributed launch commands, or diagnosing training-time config/checkpoint/VRAM failures.

What this covers

• U-Net stage selection for stage1, stage1_512, stage2, stage2_512, and stage2_efficient configurations.
• SyncNet training selection for pixel, pixel-with-attention, and latent-space variants.
• Safe torchrun command rendering for scripts.train_unet and scripts.train_syncnet without executing long training unless explicitly requested.
• Dataset fileslist creation and preflight checks for training-ready .mp4 clips.
• Checkpoint, DDP/NCCL, config-path, malformed-fileslist, and VRAM troubleshooting.

Primary entry points

• scripts/run_training.py — render or explicitly execute a safe torchrun command for U-Net or SyncNet training; run it after editing a config copy and use --preflight to validate data/file-list paths.
• scripts/write_fileslist.py — recursively write deterministic training fileslists from one or more processed dataset roots.
• references/workflows.md — U-Net and SyncNet training flow, dataset/file-list preparation, command patterns, and post-training validation notes.
• references/configuration.md — config anatomy and how the U-Net and SyncNet variants differ.
• references/troubleshooting.md — failure-mode matrix for checkpoints, DDP, fileslists, VRAM, and config mismatches.

Included source surfaces

• scripts/train_unet.py and train_unet.sh, adapted through scripts/run_training.py.
• scripts/train_syncnet.py and train_syncnet.sh, adapted through scripts/run_training.py.
• tools/write_fileslist.py, adapted as scripts/write_fileslist.py.
• configs/unet/*.yaml, configs/syncnet/*.yaml, configs/audio.yaml, and configs/scheduler_config.json, distilled in the bundled references and launcher metadata.
• docs/syncnet_arch.md, distilled in references/configuration.md.
• latentsync/data/*_dataset.py, latentsync/models/*.py, latentsync/trepa/loss.py, latentsync/utils/util.py, and latentsync/whisper/audio2feature.py, used to explain runtime requirements and failure modes.

Explicitly excluded

• Raw preprocessing pipeline internals; use the data-preparation sub-skill for those stages.
• Inference UI/CLI operation; use the inference sub-skill after a checkpoint exists.
• Full metric/evaluation workflows; this sub-skill only notes the training scripts' built-in validation artifacts.

Operational summary

• Run training from a LatentSync checkout because the repo has no packaging metadata; the bundled scripts accept --repo-root to make that explicit.
• Use torchrun, not direct python -m, because both training entry points initialize distributed state from torchrun-provided environment variables.
• Prefer the bundled launcher in dry-run mode first, then use --execute only after paths, fileslists, checkpoints, CUDA devices, and VRAM budget are verified.
• Treat stage2_512.yaml as a high-VRAM production configuration, not as a casual smoke target.