Reigh-Worker

GPU worker for Reigh — handles video generation tasks using Wan2GP.

This is a headless wrapper around the Wan2GP engine that enables programmatic video generation and queue-based processing. It connects to the Reigh application to claim and execute video generation tasks on GPU infrastructure.

Architecture

• Reigh — The main application (frontend + API)
• Reigh-Worker (this repo) — GPU worker that processes video generation jobs using Wan2GP

Powered by Wan2GP

This worker is built on top of Wan2GP by deepbeepmeep, a powerful video generation engine. The Wan2GP/ directory contains the upstream engine code with some modificiations.

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Running With Reigh: Quick Start (GPU)

1. Create venv: python3 -m venv venv_headless_wan2gp && source venv_headless_wan2gp/bin/activate
2. Install deps:
   • python -m pip install --upgrade pip wheel setuptools
   • python -m pip install -r Wan2GP/requirements.txt
   • python -m pip install -r requirements.txt
3. Set credentials and run the worker:

SUPABASE_SERVICE_ROLE_KEY="your-service-role-key-from-reigh" python worker.py \
    --supabase-url "https://your-project.supabase.co" \
    --worker "my-worker-001"

Note: Get your credentials from reigh.art. For standalone usage without credentials, see Running Without Reigh below.

Running Without Reigh: Standalone Usage

Note: The standalone/headless mode in this repo is primarily for internal testing. For general standalone usage, we recommend using Wan2GP's built-in headless mode directly, which is more feature-complete and actively maintained.

You can use the generation engine directly without connecting to Reigh's database. This is useful for local testing, scripting, or building custom pipelines.

Using the Examples

The examples/ directory contains self-contained scripts:

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

# Regenerate a range of frames within a video (fix corrupted frames, etc.)
python examples/inpaint_frames_example.py \
    --video my_video.mp4 \
    --start-frame 45 \
    --end-frame 61 \
    --output fixed_video.mp4 \
    --prompt "smooth continuous motion"

Using HeadlessTaskQueue Directly

For custom scripts, use HeadlessTaskQueue to manage model loading and generation:

import os, sys
from pathlib import Path

# Setup paths
project_root = Path(__file__).parent
sys.path.insert(0, str(project_root))
wan_root = project_root / "Wan2GP"

from headless_model_management import HeadlessTaskQueue, GenerationTask

# Initialize queue (loads models on first use)
queue = HeadlessTaskQueue(wan_dir=str(wan_root), max_workers=1)
queue.start()

try:
    # Create a generation task
    task = GenerationTask(
        id="my_task_001",
        model="wan_2_2_vace_lightning_baseline_2_2_2",
        prompt="a cat walking through a garden",
        parameters={
            "video_length": 81,
            "resolution": "896x512",
            "num_inference_steps": 6,
            "guidance_scale": 3.0,
            "seed": 42,
        }
    )
    
    # Submit and wait for result
    queue.submit_task(task)
    result = queue.wait_for_completion(task.id, timeout=600)
    
    if result.get("success"):
        print(f"Generated: {result['output_path']}")
    else:
        print(f"Failed: {result.get('error')}")
finally:
    queue.stop()

Available Models

Common model presets (see Wan2GP/defaults/*.json for full list):

Model Key	Type	Description
wan_2_2_vace_lightning_baseline_2_2_2	VACE	Fast video inpainting/editing (6 steps)
wan_2_2_i2v_lightning_baseline_2_2_2	I2V	Image-to-video with Lightning LoRAs
wan_2_2_t2v_14B	T2V	Text-to-video (14B params)
qwen_image_edit_20B	Image	Qwen-based image editing

Helper Scripts

• scripts/gpu_diag.sh – Prints GPU/NVML diagnostics to help debug nvidia-smi issues in containers
• debug.py – Debug tool for investigating tasks and worker state
• create_test_task.py – Create test tasks in Supabase for debugging

Debugging

Use python debug.py to investigate tasks and worker state:

python debug.py task <task_id>          # Investigate a specific task
python debug.py tasks --status Failed   # List recent failures

Notes

• The wrapper prefers upstream‑first fixes to maintain clean Wan2GP updates.