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Reigh-Worker

GPU worker for Reigh, for running locally or in the cloud via Reigh Worker Orchestrator — processes video generation tasks using Wan2GP.

Quick Start

# 1. Linux baseline: Ubuntu 22.04/24.04 with NVIDIA drivers, ffmpeg, git, curl
#    Ubuntu 24.04+ needs Python 3.10 from deadsnakes before the command below works:
#    sudo add-apt-repository ppa:deadsnakes/ppa && sudo apt-get update

# 2. Bootstrap uv once
curl -LsSf https://astral.sh/uv/install.sh | sh
export PATH="$HOME/.local/bin:$PATH"

# 3. Sync the locked environment from the repo root
uv sync --locked --python 3.10 --extra cuda124

# 4. Run the worker
uv run --python 3.10 python run_worker.py \
  --reigh-access-token "your-worker-token" \
  --wgp-profile 4 \
  --idle-release-minutes 15

Get credentials from reigh.art.

The Reigh app now generates two command tabs:

  • Install: bootstrap or force-resync at the configured Worker repo location
  • Run: the normal day-to-day launch path, which still runs uv sync before starting

Both commands always cd into the configured repo path first so copy-pasting from a fresh terminal in your home directory still works.

Packaging Notes

  • pyproject.toml is the canonical dependency definition for the worker runtime.
  • requirements.txt remains committed as rollback ballast during the rollout.
  • uv.lock is expected to be refreshed from the worker repo root with Python 3.10.
  • Wan2GP/requirements.txt now comes from the pinned Wan2GP/ submodule. If upstream requirements change, bump the submodule pointer, mirror the required dependency changes into the root project metadata, and regenerate uv.lock.

Rollback

  • There is no runtime pip fallback on the migrated branch. Failed uv sync runs should fail loudly.
  • If the very first uv-based launch fails after migrating an existing machine, restore the newest venv.pre-uv-* or .venv.pre-uv-* backup back to venv/ or .venv/, remove .uv-migrated, and investigate from there.
  • If the release itself must be rolled back, revert the uv rollout commits and return to the pre-uv revision that still bootstraps from the committed requirements.txt.

Standalone Usage

Use the generation engine without Reigh for local testing or custom pipelines:

# Join two video clips with AI-generated transition
python examples/join_clips_example.py \
    --clip1 scene1.mp4 --clip2 scene2.mp4 \
    --output transition.mp4 --prompt "smooth camera glide"

# Regenerate corrupted frames
python examples/inpaint_frames_example.py \
    --video my_video.mp4 --start-frame 45 --end-frame 61 \
    --output fixed.mp4 --prompt "smooth motion"

Using HeadlessTaskQueue Directly

from headless_model_management import HeadlessTaskQueue, GenerationTask
from pathlib import Path

queue = HeadlessTaskQueue(wan_dir=str(Path(__file__).parent / "Wan2GP"), max_workers=1)
queue.start()

task = GenerationTask(
    id="my_task",
    model="wan_2_2_vace_lightning_baseline_2_2_2",
    prompt="a cat walking through a garden",
    parameters={"video_length": 81, "resolution": "896x512", "seed": 42}
)

queue.submit_task(task)
result = queue.wait_for_completion(task.id, timeout=600)
print(f"Output: {result.get('output_path')}" if result.get("success") else f"Error: {result.get('error')}")

queue.stop()

Debugging

uv run --python 3.10 python -m debug task <task_id>          # Investigate a task
uv run --python 3.10 python -m debug tasks --status Failed   # List recent failures

Code Health

Project Structure

See STRUCTURE.md for detailed project layout.

Powered By

Wan2GP by deepbeepmeep — the Wan2GP/ directory is a git submodule pinned to banodoco/Wan2GP.