Comment intégrer Runway Gen-3 dans vos workflows pros en 2026

#!/bin/bash

export RUNWAY_API_KEY="sk-XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX"
export RUNWAY_API_URL="https://api.runwayml.com/v1"

# Vérifier FFmpeg
ffmpeg -version

# Installer SDK Python (optionnel mais recommandé)
pip install runwayml

# Pour Node.js
npm install @runwayml/sdk

import os
import requests
import time
import json

API_KEY = os.getenv('RUNWAY_API_KEY')
API_URL = os.getenv('RUNWAY_API_URL', 'https://api.runwayml.com/v1')

headers = {
    'Authorization': f'Bearer {API_KEY}',
    'Content-Type': 'application/json'
}

payload = {
    'model': 'gen3a',
    'input': {
        'prompt': 'Un chat roux saute agilement par-dessus une clôture en bois sous soleil couchant, style cinématique réaliste',
        'duration': 5,
        'resolution': '720p'
    },
    'params': {
        'seed': 42
    }
}

response = requests.post(f'{API_URL}/tasks', headers=headers, json=payload)
task = response.json()
print(f'Task ID: {task["id"]}')

time.sleep(30)  # Poll initial
status_resp = requests.get(f'{API_URL}/tasks/{task["id"]}', headers=headers)
print(status_resp.json())

{
  "model": "gen3a",
  "input": {
    "prompt": "Femme élégante en robe rouge danse tango passionné dans salon Art Déco 1920s. Mouvements précis, tissu fluide, expressions intenses.",
    "negative_prompt": "déformation mains, flou mouvement, couleurs saturées, artefacts IA, basse résolution",
    "duration": 8,
    "resolution": "1080p",
    "fps": 24
  },
  "params": {
    "steps": 50,
    "guidance_scale": 7.5,
    "seed": 12345
  }
}

import os
import requests
import time

API_KEY = os.getenv('RUNWAY_API_KEY')
API_URL = 'https://api.runwayml.com/v1'
headers = {'Authorization': f'Bearer {API_KEY}'}

task_id = 'your-task-id-here'

max_retries = 20
for i in range(max_retries):
    resp = requests.get(f'{API_URL}/tasks/{task_id}', headers=headers)
    data = resp.json()
    status = data.get('status')
    print(f"Attempt {i+1}: {status}")
    if status == 'succeeded':
        video_url = data['output']['video_url']
        print(f"Vidéo prête: {video_url}")
        break
    elif status in ['failed', 'canceled']:
        print("Erreur:", data.get('error'))
        break
    time.sleep(30)
else:
    print("Timeout: relancez manuellement")

import { NextRequest, NextResponse } from 'next/server';

const API_KEY = process.env.RUNWAY_API_KEY!;
const API_URL = 'https://api.runwayml.com/v1';

export async function POST(req: NextRequest) {
  const { prompt } = await req.json();

  const payload = {
    model: 'gen3a',
    input: { prompt, duration: 5, resolution: '720p' }
  };

  const res = await fetch(`${API_URL}/tasks`, {
    method: 'POST',
    headers: { Authorization: `Bearer ${API_KEY}`, 'Content-Type': 'application/json' },
    body: JSON.stringify(payload)
  });

  const task = await res.json();
  return NextResponse.json({ taskId: task.id });
}

#!/bin/bash
VIDEO_URL="https://output.runwayml.com/video.mp4"
INPUT="temp.mp4"
OUTPUT="final_4k.mp4"

# Download
curl -L "$VIDEO_URL" -o "$INPUT"

# Upscale AI + sharpen
ffmpeg -i "$INPUT" -vf "scale=3840:2160:flags=lanczos,unsharp=5:5:1.0:5:5:0.0" -c:v libx264 -crf 18 -preset slow "$OUTPUT"

# Cleanup
rm "$INPUT"
echo "Upscalé: $OUTPUT"