AI Video Studio

This runnable example turns a versioned creative brief into a complete short video without a hosted model or API. Python creates the storyboard, deterministic illustrations, and narration waveform; FFmpeg produces the MP4 inside the provided image.

AI Video Studio. Nodes in the same column can run in parallel once their dependencies complete.

Inputs

Creative brief with captions, colors, and narration tones
Smoke or demo profile

Outputs

Storyboard JSON
Rendered frame sequence
Narration WAV
MP4 or portable YUV4MPEG video
Quality report and manifest

Provenance to preserve

Brief revision
Per-stage generated files
Frame count and frame rate
Final artifact sizes

Run this example locally

Create a storyboard, procedural scene frames, generated narration audio, and a video from a versioned local brief. FFmpeg is used when available on the host; the supplied Docker image includes it. The portable fallback writes a valid YUV4MPEG stream rather than pretending to have produced an MP4.

Install Go and Python 3.12 or newer.

git clone https://github.com/UFFeScience/akoflow-examples.git &&
cd akoflow-examples &&
git checkout --detach d890c3171e3661890305b763b8fc3ede48bb61b4 &&
cd generative-ai/video-studio &&
./prepare.sh &&
./run.sh

Run the block from the directory where you keep your repositories. If you already cloned the repository, enter this example's directory and run ./prepare.sh && ./run.sh.

Preparation downloads the Go module and any declared Python packages once. The workload then runs locally with offline input data. Every meaningful step is a separate process; independent activities run in parallel after their dependencies complete.

Read outputs/manifest.json for the generated artifacts, outputs/dag-validation.json for dependency waves, and outputs/stage-<activity>.json for each activity's observed result. A failed stage stops the run and prevents a successful final manifest.

Expected outputs: storyboard.json, frames.json, narration.wav, final.mp4, quality-report.json.

Run the same activities in Desktop

Start your local AkôFlow daemon with Docker available and configure AKOFLOW_API_TOKEN in your shell. From this example's directory, run:

go run ../../tools/daemon-local -root ../.. -example generative-ai/video-studio

This command validates the daemon connection, builds the declared image, registers the local environment and workflow, and submits a real run. The daemon owns execution and file transfers. Open the printed run ID in Desktop to inspect each activity.