LoRA Pilot (The Last Docker Image You'll Ever Need)

End-to-end Stable Diffusion workspace in one container, with one persistent /workspace. LoRA Pilot bundles dataset prep, model management, training, inference, and media workflow into one integrated stack, so you can spend time creating instead of fixing broken envs.

Release-by-release details: CHANGELOG

Why LoRA Pilot

• Three proven LoRA trainer stacks in one place: Kohya SS, AI Toolkit, and Diffusion Pipe (plus TrainPilot for quick Kohya setup).
• 30+ training model families without juggling five half-compatible environments.
• ComfyUI + InvokeAI for rendering with shared models, persistent outputs, and built-in model pulling.
• Full SD lifecycle covered: dataset tagging/prep, model/dataset management, training, inference tuning, and media review.
• One control panel for ops: ControlPilot handles services, downloads, logs, docs, and runtime controls.
• Persistent by design: models, datasets, outputs, configs, and logs all live under /workspace.

What's in the box?

• Kohya SS - battle-tested LoRA trainer UI with broad model support.
• AI Toolkit - modern trainer stack wired into the same workspace and model store.
• Diffusion Pipe + TensorBoard - scalable training pipeline plus live experiment telemetry.
• ComfyUI (+ ComfyUI-Manager, ComfyUI-Downloader) - node-based inference playground and workflow automation.
• InvokeAI - dedicated inference stack in its own venv, ready when you need it.
• ControlPilot - central command center for services, models, datasets, docs, and training orchestration.
• TagPilot - fast dataset tagging/prep flow.
• TrainPilot - guided Kohya run flow with sane profile defaults.
• MediaPilot - generated image browser/organizer for curation and review.
• JupyterLab and code-server for notebook/dev workflows.
• Copilot sidecar (optional) - workspace-aware assistant integration.

Supported training families

Short version: it supports SD1, SD2, SDXL, SD3, FLUX.1 (dev/schnell/kontext), Chroma, Lumina-Image 2.0, LTX/LTX2, HunyuanVideo, Wan2.1/Wan2.2, Cosmos, HiDream, Qwen-Image, Z-Image and more for training, plus almost everything for inference.

Screenshot of Control Pilot UI

Everything is orchestrated by supervisord and writes to /workspace, so reboots do not nuke your progress.

Nice quality-of-life bits:

• Want max stability? Use :stable. Want newest features? Use :latest.
• Jupyter and code-server settings/plugins persist between restarts.
• Venv switching gymnastics are gone; the stack is prewired.
• Handy CLI tools (mc, nano, unzip, model scripts) are already there.
• Need SDXL base? models pull sdxl-base and continue with your life.
• Need a quick Kohya run? trainpilot builds a sane config from dataset size + selected quality.
• Prefer UI? ControlPilot handles service state, logs, and workflows.
• Prefer CLI? pilot status, pilot start, pilot stop are right there.

Installation

Option 1: RunPod template (fastest)

• One-click deploy: https://console.runpod.io/deploy?template=gg1utaykxa&ref=o3idfm0n

Option 2: Local Docker (Compose)

cp .env.example .env
docker compose -f docker-compose.yml up -d

More setup docs:

• Linux/macOS/local compose: DOCKER_COMPOSE.md
• Full compose guide: docker-compose/README.md
• Windows guide: docs/WINDOWS_INSTALLATION.md

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Storage layout

/workspace is home base. Keep that persisted and you keep your project.

Expected directories (created on boot if possible):

• /workspace/models (shared by everything; Invoke now points here too)
• /workspace/datasets (with /workspace/datasets/images and /workspace/datasets/ZIPs)
• /workspace/outputs (with /workspace/outputs/comfy and /workspace/outputs/invoke)
• /workspace/apps
  • Comfy: user + custom nodes under /workspace/apps/comfy
  • Diffusion Pipe under /workspace/apps/diffusion-pipe
  • Invoke under /workspace/apps/invoke
  • Kohya under /workspace/apps/kohya
  • MediaPilot under /workspace/apps/MediaPilot (https://github.com/vavo/MediaPilot)
  • TagPilot under /workspace/apps/TagPilot (https://github.com/vavo/TagPilot)
  • TrainPilot under /workspace/apps/TrainPilot(not yet on GitHub)
• /opt/pilot/repos/ai-toolkit (source) with persistent links to /workspace/datasets, /workspace/models, and /workspace/outputs/ai-toolkit
• /workspace/config
• /workspace/cache
• /workspace/logs

RunPod volume guidance

The /workspace directory is the only volume that truly matters. Models, datasets, outputs, and config all live there, so that is the one you back up.

Disk sizing (practical, not theoretical):

• Root/container disk: 20–30 GB recommended
• /workspace volume: 100 GB minimum, more if you plan to store multiple base models/checkpoints.

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Credentials

Bootstrapping writes secrets to:

• /workspace/config/secrets.env

Typical entries:

• JUPYTER_TOKEN=...
• CODE_SERVER_PASSWORD=...

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Default ports

Service	Port
Diffusion Pipe (TensorBoard)	4444
ComfyUI	5555
Kohya SS	6666
ControlPilot	7878
MediaPilot	7878 (/mediapilot)
code-server	8443
AI Toolkit	8675
JupyterLab	8888
InvokeAI (optional)	9090
Copilot sidecar (internal)	7879

Ports (optional overrides)

COMFY_PORT=5555 KOHYA_PORT=6666 DIFFPIPE_PORT=4444 CODE_SERVER_PORT=8443 JUPYTER_PORT=8888 INVOKE_PORT=9090 AI_TOOLKIT_PORT=8675 COPILOT_SIDECAR_PORT=7879

AI Toolkit (optional)

AI_TOOLKIT_DB_PATH=/workspace/config/ai-toolkit/aitk_db.db

DB is persisted under /workspace by default

Jupyter (optional)

JUPYTER_ALLOW_ORIGIN_PAT=... # extra origin regex appended to defaults (RunPod proxy + localhost + 127.0.0.1)

Shutdown scheduler (ControlPilot)

RUNPOD_POD_SHUTDOWN=stop # default; safe for local storage RUNPOD_POD_SHUTDOWN=remove # terminate pod (network volume only) RUNPOD_VOLUME_TYPE=network # auto-select remove RUNPOD_VOLUME_TYPE=local # auto-select stop

Hugging Face (optional but often necessary)

HF_TOKEN=... # for gated models HF_HUB_ENABLE_HF_TRANSFER=1 # faster downloads (requires hf_transfer, included) HF_XET_HIGH_PERFORMANCE=1 # faster Xet storage downloads (included)

Diffusion Pipe (optional)

DIFFPIPE_CONFIG=/workspace/config/diffusion-pipe.toml DIFFPIPE_LOGDIR=/workspace/diffusion-pipe/logs DIFFPIPE_NUM_GPUS=1 If DIFFPIPE_CONFIG is unset, the service just runs TensorBoard on DIFFPIPE_PORT.

Model downloader (built-in)

The image includes a system-wide command: • models (alias: pilot-models)

Usage: • models list • models pull [--dir SUBDIR] • models pull-all

You can also download models using Lora Pilot's web interface running at port 7878.

Manifest

Models are defined in the manifest shipped in the image: • /opt/pilot/models.manifest

A default copy is also shipped here (useful as a reference/template): • /opt/pilot/config/models.manifest.default

If your get-models.sh supports workspace overrides, the intended override location is: • /workspace/config/models.manifest

(If you don’t have override logic yet, copy the default into /workspace/config/ and point the script there. Humans love paper cuts.)

Both models and modelsgui will use /workspace/config/models.manifest when present.

Example usage

download SDXL base checkpoint into /workspace/models/checkpoints

models pull sdxl-base

list all available model nicknames

models list

Security note (because reality exists)

• supervisord can run with an unauthenticated unix socket by default.
• This image is meant for trusted environments like your own RunPod pod.
• Don’t expose internal control surfaces to the public internet unless you enjoy chaos monkeys.

Support

LoRA Pilot is not just a side project, it is actively used in real production workflows. Builds are frequent, breakages are taken seriously, and reasonable feature requests are welcome. If you need help or have questions, feel free to reach out or open an issue on GitHub.

Reddit: u/no3us

Sponsor

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🆕 Recent Updates

See full details in CHANGELOG.

ControlPilot 2.x redesign

• Unified command center for services, model/dataset management, trainers, logs, and docs.
• Cleaner operations UX with better service actions, autostart control, model pulls, and dashboard telemetry.

MediaPilot and TagPilot workflow integration

• MediaPilot is now the built-in output browser for fast curation, comparison, and cleanup.
• TagPilot handles dataset tagging/prep in the same stack, including reliable large dataset saves to /workspace.

Service maintenance and updateability

• Added service version checks and in-app update actions in Services.
• Added rollback metadata + boot-time reconcile flow driven by /workspace/config/service-updates.toml.

Training + inference stack updates

• Added AI Toolkit as a first-class trainer with persistent DB/config under /workspace.
• Upgraded InvokeAI to 6.11.1.

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🙏 Standing on the shoulders of giants

• ComfyUI - Node-based magic
• ComfyUI-Manager - The organizer
• Kohya SS - LoRA whisperer
• AI Toolkit - modern trainer stack
• code-server - Code anywhere
• JupyterLab - Data scientist's best friend
• InvokeAI - The fancy pants option
• Diffusion Pipe - Training powerhouse
• TensorBoard - Visualization tool
• GitHub Copilot SDK/CLI - assistant foundation

📜 License

MIT License - go wild, make cool stuff, just don't blame us if your AI starts writing poetry about toast.

Made with ❤️ and way too much coffee by vavo

"If it works, don't touch it. If it doesn't, reboot. If that fails, we have Docker." - Ancient sysadmin wisdom

Project links

• GitHub repo: https://github.com/vavo/lora-pilot
• Docker Hub image: https://hub.docker.com/r/notrius/lora-pilot
• RunPod template: https://console.runpod.io/deploy?template=gg1utaykxa&ref=o3idfm0n

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