ControlNet Repo Skill

Use this skill for tasks involving the ControlNet 1.0 repository: preparing control maps, running or adapting Gradio demos, validating Fill50K-style training data, creating ControlNet initialization checkpoints, inspecting configs, or debugging source-checkout setup.

This repo is a source checkout, not an installable Python distribution. Do not assume pip install control-net exists. A working runtime usually needs the documented environment dependencies, external Stable Diffusion/ControlNet checkpoints, detector weights, and compatible GPU/Torch setup.

Start Here

• Read references/repo-provenance.md before deciding whether this skill matches a checkout or needs refresh.
• Read references/evidence-map.md for the source evidence and script-bundling decisions behind this skill.
• Read references/troubleshooting.md for cross-cutting install/import, checkpoint, CUDA, model-download, and non-packaged-checkout failures.
• Run scripts/check_controlnet_checkout.py for a safe checkout/config/import diagnostic; it does not launch Gradio, load checkpoints, download models, train, or generate images.

Route By Task

User task	Read
Prepare or debug Canny, HED, MLSD, MiDaS depth/normal, OpenPose, or Uniformer conditioning maps	sub-skills/annotators-and-preprocessing/SKILL.md
Choose, inspect, run, or adapt a ControlNet Gradio image-generation app	sub-skills/gradio-inference-apps/SKILL.md
Validate Fill50K-style data, write a custom dataset, or adapt tutorial training	sub-skills/training-and-datasets/SKILL.md
Inspect configs/APIs, create init checkpoints, dry-run key mappings, or transfer ControlNet weights	sub-skills/model-and-weight-utilities/SKILL.md

Safe Setup Expectations

1. Create an environment compatible with the repository's environment.yaml family: Python 3.8-era ML stack, PyTorch/TorchVision, OpenCV, Gradio, PyTorch Lightning, OmegaConf, Transformers, OpenCLIP, and optional detector dependencies.
2. Treat model files as external assets. Stable Diffusion checkpoints belong with the model/config workflow, ControlNet demo checkpoints belong with Gradio app operation, and detector checkpoints belong with annotator preprocessing.
3. Use safe diagnostics first: parse configs and signatures, validate data layouts, and dry-run state-dict key mapping before launching servers, loading checkpoints, using CUDA, downloading weights, or training.
4. Prefer CPU/location-safe inspection for checkpoint metadata; only run CUDA, Gradio servers, or long training when the user explicitly wants execution and has provided assets/hardware.

Minimal Safe Checks

python path/to/control-net/scripts/check_controlnet_checkout.py --repo-root path/to/ControlNet --json

Then route to the nearest sub-skill for workflow-specific checks:

• annotators-and-preprocessing/scripts/inspect_annotator_inputs.py --self-check
• gradio-inference-apps/scripts/extract_gradio_signatures.py --repo-root path/to/ControlNet --json
• training-and-datasets/scripts/validate_fill50k_dataset.py --write-example-fixture path/to/tmp-fill50k --validate-written-fixture
• model-and-weight-utilities/scripts/inspect_weight_mapping.py --self-test

Common Boundaries

• Do not import gradio_*2image.py merely to inspect it; the source scripts build models, load checkpoints, move models to CUDA, and launch Gradio at top level.
• Do not instantiate learned annotators unless detector checkpoints, optional dependencies, CUDA/Torch compatibility, and network policy are clear.
• Do not run tutorial training as a smoke test; it requires Fill50K data, initialized checkpoints, GPU memory, and may trigger model/tokenizer downloads.
• Do not run checkpoint conversion scripts without explicit input/output paths and overwrite safeguards; use the bundled dry-run inspector first.

Refresh Signals

Run refresh-repo-skill if the current checkout changes public Gradio scripts, cldm/, ldm/, annotator/, models/*.yaml, tutorial scripts, docs, environment dependencies, or source script behavior relative to references/repo-provenance.md.