Technical Specifications of runwayml-upscale-video
| Specification | Details |
|---|---|
| Model ID | runwayml-upscale-video |
| Provider | Runway |
| Model category | Video upscaling / enhancement workflow |
| Primary function | Enhances uploaded videos and upscales them to 4K output. |
| Input type | Video input supplied by the user. Runway describes it as working with uploaded videos, including older footage, compressed files, and low-resolution content. |
| Output type | Upscaled 4K video. Example outputs shown by Runway include 3840 × 2160. |
| Processing style | Simple app-style workflow with minimal manual configuration; Runway states there are no dials, settings, or formatting required. |
| Best-fit use cases | Restoring lower-resolution clips, improving compressed footage, preparing videos for higher-resolution delivery, and enhancing archive or social media source material. This is an inference from Runway’s stated use cases and examples. |
| Delivery pattern | Asynchronous task-based API workflows are standard in Runway’s developer platform, where generation requests return a task ID that is later polled for completion. |
| Authentication | Bearer API key authentication is used in Runway’s API platform, with the X-Runway-Version header required on API requests. |
| SDK availability | Official SDKs are available for Node.js and Python. |
What is runwayml-upscale-video?
runwayml-upscale-video is CometAPI’s platform identifier for Runway’s video upscaling capability, which is designed to take an uploaded video and enhance it to full 4K resolution. Runway presents this workflow as a streamlined “Upscale Video” app that requires little to no manual tuning.
In practice, this model is aimed at creators and developers who need to improve the apparent quality of footage that starts out lower resolution, compressed, or visually soft. Runway’s public materials emphasize a simple upload-and-generate experience rather than a parameter-heavy restoration pipeline.
Although Runway’s public developer documentation focuses primarily on generation endpoints such as image-to-video and task retrieval, its API platform overall uses an asynchronous task model, so CometAPI integrations for runwayml-upscale-video should typically be treated as submit-a-job, then retrieve-results workflows.
Main features of runwayml-upscale-video
- 4K upscaling: The core capability is converting uploaded video into full 4K output, with Runway explicitly advertising 4K enhancement and showing example outputs at 3840 × 2160.
- Simple workflow: Runway describes the experience as requiring no dials, settings, or formatting, making it suitable for users who want straightforward enhancement without complex tuning.
- Works with existing footage: The tool is designed for uploaded source videos rather than only newly generated clips, including older footage, compressed media, and low-resolution files.
- Useful for quality recovery: Runway’s examples highlight improvements in environmental detail, skin detail and texture, and motion in low-light scenes.
- Asynchronous processing compatibility: Runway’s API ecosystem returns task IDs and supports polling for completion, which fits well with production integrations that need queued or background processing.
- Developer-friendly ecosystem: Runway provides official Node.js and Python SDKs, which can simplify authenticated request handling and task retrieval in applications that integrate video AI workflows.
How to access and integrate runwayml-upscale-video
Step 1: Sign Up for API Key
To access runwayml-upscale-video, first create an account through the Runway developer portal and set up an organization. Runway’s setup flow indicates that API keys are created at the organization level, and credits must be added before production use. The API key is shown only once, so it should be stored securely in a secret manager or environment variable.
Step 2: Send Requests to runwayml-upscale-video API
Use your CometAPI API key to send requests to the runwayml-upscale-video endpoint. In a typical integration, you submit the input payload, authenticate with your API credentials, and start an asynchronous processing task for the video upscaling job.
Runway’s own API platform uses Bearer-token authentication, versioned headers, and SDK-based request flows for media tasks, so the same architectural pattern is a good fit when integrating this model through CometAPI. Official SDKs are available for Node.js and Python if you are building a backend workflow around model invocation.
Step 3: Retrieve and Verify Results
After submitting the request, retrieve the task result and verify that processing completed successfully. Runway’s task system supports polling until the task reaches a terminal state such as SUCCEEDED, FAILED, or CANCELED, and recommends polling at intervals of 5 seconds or more with backoff handling. Once complete, confirm that the returned asset matches your expected 4K enhancement requirements before storing or delivering it downstream.