Your AI playground in a box - because who has time to configure 17 different tools? Ever wanted to train LoRAs but ended up in dependency hell? We've been there. LoRA Pilot is a magical container that bundles everything you need for AI datasets management, training and image generation into one neat package. No more crying over broken dependencies at 3 AM.
Currently supports SD1, SD2, SDXL, SD3, FLUX.1 (dev, schnell), Chroma, Lumina-Image 2.0, LTX-Video, HunyuanVideo, Wan2.1, Wan2.2, Cosmos, HiDream, Z-Index and few more for training and almost every model out there for inference.
- ControlPilot – one web UI to manage telemetry, services, models, upload and tag/caption datasets or start training using kohya/diffusion-pipe
- 🎨 ComfyUI (+ ComfyUI-Manager preinstalled) - Your node-based playground
- 🏋️ Kohya SS - Where LoRAs are born (web UI included!)
- 📓 JupyterLab - For when you need to get nerdy
- 💻 code-server - VS Code in your browser (because local setups are overrated)
- 🔮 InvokeAI - Living in its own virtual environment (the diva of the bunch)
- 🚂 Diffusion Pipe - Training + TensorBoard, all cozy together
- TagPilot – dataset tagger embedded on the same port as ControlPilot
- TrainPilot - the easiest way to run SDXL training on kohya
- GUI for dpipe - a web UI for diffusion pipe
- 🤖 Copilot (optional) - a sidecar that wraps the
copilotCLI so it can operate on/workspacewith tools (via ControlPilot tab)
Screenshot of Control Pilot UI
Everything is orchestrated by supervisord and writes to /workspace so you can actually keep your work. Imagine that!
Few of the thoughtful details that really bothered me when I was using other SD (Stable Diffusion) docker images:
- If you want stabiity, just choose :stable and you'll always have 100% working image. Why change anything if it works? (I promise not to break things in :latest though)
- when you login to Jupyter or VS code server, change the theme, add some plugins or setup a workspace - unlike with other containers, your settings and extensions will persist between reboots
- no need to change venvs once you login - everything is already set up in the container
- did you always had to install mc, nano or unzip after every reboot? No more!
- there are loads of custom made scripts to make your workflow smoother and more efficient if you are a CLI guy;
- Need SDXL1.0 base model? "models pull sdxl-base", that's it!
- Want to run another kohya training without spending 30 minutes editing toml file?Just run "trainpilot", choose a dataset from the select box, desired lora quality and a proven-to-always-work toml will be generated for you based on the size of your dataset.
- ControlPilot gives you a web UI to manage all services without needing to use the command line
- prefer CLI and want to manage your services? Never been easier: "pilot status", "pilot start", "pilot stop" - all managed by supervisord
| Service | Port |
|---|---|
| Diffusion Pipe (TensorBoard) | 4444 |
| ComfyUI | 5555 |
| Kohya SS | 6666 |
| ControlPilot | 7878 |
| code-server | 8443 |
| JupyterLab | 8888 |
| InvokeAI (optional) | 9090 |
| Copilot sidecar (internal) | 7879 |
Expose them in RunPod (or just use my RunPod template - https://console.runpod.io/deploy?template=gg1utaykxa&ref=o3idfm0n).
LoRA Pilot also runs on localhost via Docker Compose (GPU, CPU, and dev setups included).
Quick start:
cp .env.example .env
docker compose -f docker-compose.yml up -dSee DOCKER_COMPOSE.md for a short overview and docker-compose/README.md for the full guide.
The container treats /workspace as the only place that matters.
Expected directories (created on boot if possible):
/workspace/models(shared by everything; Invoke now points here too)/workspace/datasets(with/workspace/datasets/imagesand/workspace/datasets/ZIPs)/workspace/outputs(with/workspace/outputs/comfyand/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 - TagPilot under
/workspace/apps/TagPilot(https://github.com/vavo/TagPilot) - TrainPilot under
/workspace/apps/TrainPilot(not yet on GitHub)
- Comfy: user + custom nodes under
/workspace/config/workspace/cache/workspace/logs
The /workspace directory is the only volume that needs to be persisted. All your models, datasets, outputs, and configurations will be stored here. Whether you choose to use a network volume or local storage, this is the only directory that needs to be backed up.
Disk sizing (practical, not theoretical):
- Root/container disk: 20–30 GB recommended
/workspacevolume: 100 GB minimum, more if you plan to store multiple base models/checkpoints.
Bootstrapping writes secrets to:
/workspace/config/secrets.env
Typical entries:
JUPYTER_TOKEN=...CODE_SERVER_PASSWORD=...
COMFY_PORT=5555 KOHYA_PORT=6666 DIFFPIPE_PORT=4444 CODE_SERVER_PORT=8443 JUPYTER_PORT=8888 INVOKE_PORT=9090 COPILOT_SIDECAR_PORT=7879
JUPYTER_ALLOW_ORIGIN_PAT=... # extra origin regex appended to defaults (RunPod proxy + localhost + 127.0.0.1)
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
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)
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.
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.
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.
models pull sdxl-base
models list
- 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.
This is not only my hobby project, but also a docker image I actively use for my own work. I love automation. Effectivity. Cost savings. I create 2-3 new builds a day to keep things fresh and working. I'm also happy to implement any reasonable feature requests. If you need help or have questions, feel free to reach out or open an issue on GitHub.
Reddit: u/no3us
- Fixed ComfyUI proxy - Resolved httpx import issues for localhost compatibility
- Universal deployment - ComfyUI live preview now works on both RunPod and localhost
- Mixed content fixes - Proper HTTPS/HTTP handling in different environments
- Training logs fixed - Kohya training logs now display correctly on localhost
- Live preview - Real-time image generation display in ControlPilot
- WebSocket proxy - Seamless ComfyUI WebSocket connection handling
- Dynamic URLs - Automatic detection of RunPod vs localhost deployment
- Last image display - Shows latest generated image when no new images are being generated
- ComfyUI - Node-based magic
- ComfyUI-Manager - The organizer
- Kohya SS - LoRA whisperer
- code-server - Code anywhere
- JupyterLab - Data scientist's best friend
- InvokeAI - The fancy pants option
- Diffusion Pipe - Training powerhouse
- TensorBoard - Visualization tool
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
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