Batch Upscale Videos

Why this matters

Upscaling one clip is a demo; a library is a pipeline problem. Folder-prefix batch jobs give queueing, retries, and per-file status without a scheduler.

Staging by value (hero content first) means the visible win lands on day one.

How it works

Submit the folder with your scale and model; each file becomes a job with its own status and output URL.

Submit a job with an input URL (S3, GCS or HTTPS), poll the job URL, and download the rendered output. No GPU, no queues, no ffmpeg builds to babysit.

Code

import requests, time

API = "https://api.mlslabs.io/v1/super-resolution/jobs"
headers = {"X-API-Key": "YOUR_API_KEY"}
payload = {"input": "s3://bucket/library/", "scale": 2, "model": "real-esrgan-x4", "pattern": "*.mp4"}

resp = requests.post(API, json=payload, headers=headers)
job = resp.json()
while job["status"] not in ("succeeded", "failed"):
    time.sleep(3)
    job = requests.get(job["url"], headers=headers).json()

print("Output:", job["output_url"])

Pro tips

• Stage by value: hero titles first, then the long tail.
• Batch in 2x by default — 4x on select titles where it matters.
• Log per-file model+settings so QC can reproduce any output.

Model the batch upscale in Postman.