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name: data-and-training description: "Prepares AnyDoor datasets, preprocessing, training, and weight conversion." metadata: disco-role: operating disable-model-invocation: true license: MIT

Data and Training

Use this sub-skill when the task is about AnyDoor dataset layout, preprocessing, debugging data samples, starting training, or converting initialization weights.

What this sub-skill owns

• Dataset path validation and dataset-family conventions.
• UVO annotation reorganization.
• Training recipe and resource planning.
• Data-debug sampling and visualization.
• Stable Diffusion 2.1 to AnyDoor-style weight conversion guardrails.

What this sub-skill does not own

• Generation workflows and mask validation for inference.
• Environment installation and checkpoint patching.
• Demo launch or Cog prediction details.

Route those to the sibling sub-skills.

Read first

• ../../references/overview.md
• ../../references/checkpoints-and-configs.md
• ../../references/troubleshooting.md
• references/dataset-formats.md
• references/training-workflows.md
• references/model-architecture.md
• references/troubleshooting.md

Use these scripts

• scripts/check_dataset_config.py to inspect datasets.yaml-style layouts.
• scripts/rewrite_uvo_annotations.py to convert UVO annotations into the repo’s expected map format.
• scripts/convert_control_init.py to validate the SD2.1 conversion workflow.
• scripts/run_train_checked.sh to print or run the training command after validating the setup.

Trigger phrases

This is usually the right branch when the user says things like:

• “How should I structure my dataset?”
• “Why does the data loader fail?”
• “How do I process UVO annotations?”
• “What does run_dataset_debug do?”
• “How do I start training?”
• “How do I convert the SD2.1 checkpoint?”
• “What datasets does AnyDoor mix during training?”

Input contract

The data side of AnyDoor expects user-provided roots for things like:

• YouTube-VOS / YouTube-VIS image and annotation trees,
• VIPSeg panoptic masks,
• UVO sparse videos and JSON mappings,
• MOSE image and annotation roots,
• MVImageNet text/index files and image trees,
• VITON-HD, DressCode, FashionTryon, SAM, Saliency, and LVIS-style layouts.

The exact path keys live in configs/datasets.yaml.

Output contract

A successful data/training route should explain:

• which dataset family the user is preparing,
• what file layout that family expects,
• which labels or annotation conventions matter,
• what can be validated safely without loading the entire dataset,
• and whether the task is a quick config check or a long-running training path.

Default workflow order

1. Verify the dataset config structure.
2. Confirm the file layout and label conventions for the chosen dataset family.
3. Run a small preprocessing or debug helper if needed.
4. Only then consider training or weight conversion.

Common decisions

• Dataset paths still contain placeholders: stop and patch them first.
• UVO annotation layout is raw: run the rewriter before training.
• Need a quick sanity check only: use the dataset config checker instead of a full dataloader.
• Training requested without checkpoints: explain the initialization and resource requirements first.
• Conversion requested: point out the source script’s stale config path and use the bundled guardrail script.

Troubleshooting focus

This branch owns symptoms such as:

• placeholder dataset roots,
• wrong foreground labels in parse masks,
• missing pycocotools, lvis, or panopticapi,
• dataset loaders that work only on real data and therefore need safe config checks first,
• Lightning/DDP issues,
• and the source weight-conversion helper’s path bug.

Handy reminders

• The training recipe is multi-dataset and GPU-oriented.
• BaseDataset.process_pairs produces the ref, jpg, hint, extra_sizes, and crop metadata that the inference side also relies on.
• Several dataset families use different foreground labels in parse masks.
• Full training is much heavier than a config or import check.

Cross-links

When the issue is really about setup or checkpoints, route back to setup-and-checkpoints. When the issue is really about generation or the demo, route to inference-and-demo.

Quality bar

A future agent should be able to prepare AnyDoor data, explain the relevant annotations, and distinguish between a safe config validation and a heavy training run without reopening the source repository.