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Summary

The Video Upscaler skill provides professional-grade video quality enhancement by leveraging a powerful, multi-model backend. It intelligently selects the best AI model (Topaz, SeedVR2, etc.) based on the user-defined profile to achieve optimal results, transforming low-resolution or noisy footage into crisp, cinematic-quality video.

This skill abstracts away the complexity of choosing and configuring different AI upscaling models. Instead of dealing with dozens of technical parameters, the user simply chooses a high-level goal, and the skill handles the rest.

Features

  • Multi-Model Backend: Dynamically routes requests to the best model for the job (Topaz, SeedVR2, etc.) via a unified API.
  • Profile-Based Enhancement: Offers a range of pre-configured profiles for common use cases, from standard 2x upscaling to 4K cinematic conversion and 60 FPS frame boosting.
  • Asynchronous by Design: Handles long-running video processing jobs without blocking the agent.
  • Simple Interface: Requires only a video URL and a profile name to start.

How It Works

The skill operates in a simple, two-step asynchronous workflow:

  1. Submit Job: The agent calls the /upscale endpoint with a video URL and a profile name. The service validates the request, selects the appropriate AI model, and submits the job to the fal.ai backend. It immediately returns a task_id.

  2. Poll for Status: The agent uses the task_id to periodically call the /status/{task_id} endpoint. The status will be queued, in_progress, or completed. Once completed, the response will contain the URL of the final, upscaled video.

Available Profiles

Profile NameDescription
standard_x22x upscale using Topaz Proteus v4. Best all-around quality for live-action footage.
cinema_4kUpscale to 4K (2160p) using SeedVR2. Best for cinematic content requiring temporal consistency.
frame_boost_60fps2x upscale + frame interpolation to 60 FPS using Topaz Apollo v8. Best for sports and action.
ai_video_enhance4x upscale using Topaz. Best for AI-generated videos that need resolution boosting.
web_optimizedUpscale to 1080p with web-optimized H264 output. Best for social media and web publishing.

End-to-End Example

User Request: "Enhance this video to 4K cinematic quality: [video_url]"

1. Agent -> Skill (Submit Job)

The agent identifies the user's intent and calls the /upscale endpoint with the cinema_4k profile.

curl -X POST http://<your_backend_url>/upscale \
  -H "Content-Type: application/json" \
  -d 
    "video_url": "[video_url]",
    "profile": "cinema_4k"
  }

Response:

{
  "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  "model_used": "fal-ai/seedvr/upscale/video",
  "profile": "cinema_4k"
}

2. Agent -> Skill (Poll for Status)

The agent waits and then polls the status endpoint.

curl http://<your_backend_url>/status/a1b2c3d4-e5f6-7890-1234-567890abcdef

Response (In Progress):

{
  "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  "status": "in_progress",
  "logs": ["Processing frame 100/1200..."]
}

Response (Completed):

{
  "task_id": "a1b2c3d4-e5f6-7890-1234-567890abcdef",
  "status": "completed",
  "result": {
    "video_url": "https://.../upscaled_video.mp4"
  }
}

3. Agent -> User

The agent delivers the final, upscaled video URL to the user.

Source

git clone https://github.com/wells1137/skills-gen/blob/main/skills/video-upscaler/SKILL.mdView on GitHub

Overview

Video Upscaler leverages a multi-model backend to intelligently route each video to the best AI model (Topaz, SeedVR2, and others) for upscale and enhancement. It offers profile-based presets from standard 2x upscaling to 4K cinema conversion and 60 FPS boost, while processing asynchronously to handle long tasks without blocking.

How This Skill Works

Submit a job to the upscale endpoint with your video URL and a profile name. The service validates the request, selects the appropriate model via the fal.ai backend, and returns a task_id. You then poll the status endpoint using the task_id until status becomes completed, which includes the final video URL.

When to Use It

  • Upscale archival or low resolution footage to cinema 4K using cinema_4k
  • Boost sports or action clips to 60 FPS using frame_boost_60fps
  • Enhance AI generated videos to higher resolution with ai_video_enhance
  • Prepare web-friendly clips for social media with web_optimized 1080p
  • General 2x upscale for standard live action footage with standard_x2

Quick Start

  1. Step 1: Choose a profile and provide the video URL to the upscale endpoint
  2. Step 2: Receive a task_id and model used from the API response
  3. Step 3: Poll /status/{task_id} until status is completed and retrieve the final video URL

Best Practices

  • Start with a clear goal and pick the profile that matches the target output
  • Ensure the video URL is publicly accessible by the backend
  • Leverage asynchronous processing by polling instead of blocking
  • Review model-specific outputs and perform quality checks before distribution
  • Archive the final video URL and note the profile and settings used

Example Use Cases

  • Restoring archival footage to 4K cinema quality with cinema_4k
  • Sports clips upscaled to 60 FPS frame_boost_60fps for smoother playback
  • AI-generated footage boosted to high resolution with ai_video_enhance
  • Web-ready content produced with web_optimized for social distribution
  • A standard live-action clip upscaled 2x for quick turnaround using standard_x2

Frequently Asked Questions

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