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name: training-and-configs description: "Author, inspect, adapt, and troubleshoot config-driven training and model construction for diffusion and autoencoding workflows in generative-models." disable-model-invocation: true metadata: disco-role: operating license: MIT

Training and Configs

Use this sub-skill when a task involves main.py --base, OmegaConf dotlist overrides, instantiate_from_config, DiffusionEngine training configs, AutoencodingEngine, GeneralConditioner, toy MNIST configs, USER placeholders, resume behavior, or Lightning callbacks/log directories.

Do not use this sub-skill for inference execution, checkpoint sampling, UI demos, or watermarking. Route SDXL/API inference tasks to ../inference-api/SKILL.md, video sampling scripts to ../video-sampling/SKILL.md, and demos or watermarking to ../demos-and-watermarking/SKILL.md.

What This Covers

• Build and inspect model, data, and lightning YAML sections for config-driven training.
• Trace nested target/params objects that will be consumed by instantiate_from_config.
• Adapt diffusion configs around DiffusionEngine, denoisers, samplers, losses, conditioners, and first-stage autoencoders.
• Adapt autoencoding configs around AutoencodingEngine and legacy AutoencoderKL wrappers.
• Reason about main.py merge order, CLI overrides, trainer defaults, logging, callbacks, and resume semantics.
• Validate configs statically without starting training, downloading checkpoints, or probing GPUs.

Fast Static Check

Use the bundled helper for safe inspection before any training command:

python sub-skills/training-and-configs/scripts/inspect_training_config.py \
  --config path/to/base.yaml \
  --dotlist lightning.trainer.devices=0 model.params.sampler_config.params.num_steps=10

Add --json when another tool needs structured output. The helper parses and merges config files, reports top-level sections, target paths, placeholders, likely missing target keys, data assumptions, resume/name conflicts, and a command template. It never imports training modules, instantiates models, downloads datasets, loads checkpoints, or calls the trainer.

Required Mental Model

main.py treats YAML as the source of truth. It loads every --base config from left to right, builds an OmegaConf dotlist from unknown CLI tokens, merges the dotlist last, pops lightning out of the project config, and then instantiates config.model and config.data by calling instantiate_from_config on each object.

Every object meant for instantiate_from_config needs:

target: dotted.import.Path
params:
  key: value

target resolves to a Python object and params are passed as keyword arguments. Missing target usually fails with KeyError: Expected key target to instantiate. Import typos fail later when the module/class path cannot be imported.

Reference Map

• references/configuration.md explains schema patterns, merge rules, target paths, placeholders, and safe overrides.
• references/api-reference.md records config-facing signatures and important target classes verified against sgm version 0.1.0.
• references/workflows.md gives safe authoring, adaptation, resume, and validation workflows.
• references/troubleshooting.md maps common symptoms to recoveries.
• scripts/inspect_training_config.py provides executable static validation help.

Safety Rules

• Prefer static inspection over main.py for validation; even --train false --no-test true can still instantiate model/data objects and call data preparation.
• Do not recommend long training, checkpoint downloads, webdataset reads, or MNIST/CIFAR downloads as verification.
• Treat CKPT_PATH, DATA_PATH, USER comments, and dataset shard URLs as placeholders until a human supplies local values.
• Keep --name and --resume mutually exclusive; use --resume_from_checkpoint with --name only when intentionally creating a new log folder from a checkpoint.
• Assume GPU defaults unless lightning.trainer.accelerator and lightning.trainer.devices are explicitly reviewed.