inlineresearch/inline-studio repository overview

⁠Inline Studio

AI filmmaking on a node canvas. Generate locally on your own GPU and train your own LoRAs on the same canvas, with the built-in Inline Core engine and hosted models. Every render is kept as a versioned take, so you never lose the good version.

Free and open source, GPL-3.0. inlinestudio.art⁠ · GitHub⁠ · Discord⁠

No GPU of your own? Deploy it on RunPod in one click⁠.

⁠Quick start

docker run --gpus all -p 8848:8848 -p 8888:8888 -p 8080:8080 \
  -v "$HOME/inline-workspace:/workspace" \
  --shm-size=16g \
  inlineresearch/inline-studio:latest

Open http://localhost:8848⁠.

That volume mount is the important part. Everything the app writes lives under /workspace: model weights, projects, takes, trained LoRAs, settings and your fal key. Without it, all of that is destroyed when the container is removed.

There is a Compose file in the repo at docker/compose.yaml⁠ if you prefer that.

⁠What you need

• An NVIDIA GPU and the NVIDIA Container Toolkit. The image is amd64 only.
• Driver R580 or newer. The image is built against CUDA 13.0, which has kernels for Turing (sm_75, so a T4 works) through Blackwell (sm_120, so a 5090 or an RTX PRO 6000 works). Volta, Pascal and Maxwell are not covered by CUDA 13 and will not run.
• Disk. The image is about 10GB. Model weights are extra and go on your volume, see below.
• A generous --shm-size. Docker defaults to 64MB, which the trainer's dataloader workers will exhaust. 16g is a safe figure.

⁠Models are not in the image

Nothing is baked in, which keeps the image small and lets you pull only what you use. On first run, open a generate or train node in the app and use its download button. Files land in /workspace/models, so they survive a restart and are shared by every container you point at the same volume.

Rough sizes for the full set behind each node:

⁠Ports

JupyterLab and the file browser print a generated password to the container log on every start. Set JUPYTER_PASSWORD and FILEBROWSER_PASSWORD to choose your own, or set ENABLE_JUPYTER=0 and ENABLE_FILEBROWSER=0 to turn them off.

⁠Environment

FAL_KEY is only read when no key has been saved in the app yet. Once you save one in Settings it lives on the volume and wins over the environment variable.

⁠LoRA training

Training is cheaper than generating. A 16GB card trains all three image models at 512px, and a LoRA trained at 512 applies at any generation resolution.

Open the Trainer tab, make a dataset, caption it, wire Load Dataset into Caption into Train LoRA, and run. Finished adapters land in /workspace/models/loras and appear in the loader node straight away, with a download button in the Outputs panel.

Full reference, including measured VRAM and runtimes: TRAINING.md⁠. Walkthroughs per model: inlinestudio.art/lora-training⁠.

⁠Tags

Every tag is built from the matching GitHub release by CI, so the image and the source always agree.

⁠Notes

• The app has no authentication. Do not publish port 8848 to the open internet.
• Long single requests can time out behind a reverse proxy. Generation streams over a websocket and is unaffected, but a very large model download started from the UI may need retrying.
• Multi-GPU generation (xDiT) is not installed in this image. It fails to build on many systems, so it stays an opt-in source install.

⁠License

GPL-3.0-or-later. Model weights carry their own licences.