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Magnific API uses Magnific.ai technology, now available as a comprehensive API service.

Video Upscaler Topaz uses Topaz Starlight generative models for high-quality video upscaling with model selection, AI frame interpolation, and noise control.
Video Upscaler Topaz is an AI-powered video upscaling API built on Topaz Starlight generative models. It enhances video resolution with generative detail synthesis and supports optional frame-rate changes through AI frame interpolation. The API offers two enhancement models — starlight_precise_2_5 for the highest quality and starlight_fast_2 for faster processing — with output resolutions of 720p, 1K, 2K (default), and 4K. For faithful, non-generative upscaling with a strength blend, see Video Upscaler Precision.

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Key capabilities

  • Topaz Starlight models: Choose starlight_precise_2_5 (highest quality, default) or starlight_fast_2 (faster, slight quality trade-off)
  • Resolution up to 4K: Upscale videos to 720p, 1k, 2k (default), or 4k output resolution while preserving the source aspect ratio
  • AI frame interpolation: Change the output frame rate with target_fps. When it differs from the source, an interpolation model is applied — apollo (recommended) or chronos (optimized for slow-motion and large frame-rate changes)
  • Noise control: Adjustable noise reduction / addition intensity (0-1, default 0)
  • Async processing: Webhook notifications or polling for task completion
  • Flexible input: Accepts a publicly accessible HTTPS URL or an uploaded file reference (upl_...)

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Use cases

  • Film restoration: Upscale archival and classic footage to modern resolutions with generative detail recovery
  • Content repurposing: Enhance low-resolution clips for large-screen display or broadcast delivery
  • Video production: Upscale B-roll and legacy footage to match project resolution requirements
  • Smooth motion: Boost frame rate with AI interpolation for smoother playback of low-fps footage
  • E-commerce: Enhance product videos with higher resolution and detail
  • Educational content: Modernize older training videos and recordings for high-resolution displays

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Upscale videos with Topaz

Create a Topaz upscaling task by submitting a video to the API. The service returns a task ID for async polling or webhook notification.

POST /v1/ai/video-upscaler-topaz

Create a new Topaz video upscaling task

GET /v1/ai/video-upscaler-topaz

List all Topaz upscaler tasks

GET /v1/ai/video-upscaler-topaz/{task-id}

Get task status and results by ID

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Parameters

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Frequently Asked Questions

Video Upscaler Topaz is an AI-powered video upscaling API built on Topaz Starlight generative models. You submit a video via a publicly accessible HTTPS URL (or an upl_ upload reference), receive a task ID, then poll for results or receive a webhook notification when processing completes. The service synthesizes generative detail while upscaling to your selected resolution, and can optionally change the frame rate with AI interpolation.
starlight_precise_2_5 is the highest-quality generative upscaler and is the default. starlight_fast_2 is a faster generative upscaler with a slight trade-off in quality. Choose Fast when turnaround time matters more than maximum fidelity.
Set target_fps to change the output frame rate. When target_fps differs from the source frame rate, an interpolation model generates the intermediate frames — apollo (recommended for most videos) or chronos (optimized for slow-motion and large frame-rate changes). If you omit target_fps, the source frame rate is kept and no interpolation is applied. Frame interpolation requires a target of at least 15 fps.
Video Upscaler Topaz uses Topaz Starlight generative models that synthesize new detail while upscaling, and adds AI frame interpolation. Video Upscaler Precision focuses on faithful, non-generative upscaling with a strength blend between original and upscaled output. Choose Topaz for generative enhancement and frame-rate changes; choose Precision to stay true to the original without AI-generated additions.
Video Upscaler Topaz supports four output resolutions: 720p, 1k, 2k (default), and 4k. The source aspect ratio is always preserved. Higher resolutions increase processing time. See the Pricing page for details on each resolution tier.
Rate limits vary by subscription tier. See the Rate Limits page for current limits by plan.
Pricing depends on the video and the output resolution you select. See the Pricing page for current rates and subscription options.

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Best practices

  • Input quality: Start from the highest-quality source video available. Heavily compressed inputs may have artifacts amplified during upscaling
  • Model selection: Use starlight_precise_2_5 for maximum quality; switch to starlight_fast_2 when turnaround time is the priority
  • Frame interpolation: Use apollo for most footage; choose chronos for slow-motion or large frame-rate increases. Keep target_fps at 15 or above for interpolation
  • Noise tuning: Start with the default of 0 and increase only if the source needs additional noise handling
  • Resolution selection: Choose 2K (default) for most use cases as a good balance of quality and processing time. Use 4K when the target display or delivery format requires it
  • Production integration: Use webhooks instead of polling for scalable, event-driven applications
  • Error handling: Implement retry logic with exponential backoff for 503 errors during high traffic
  • Video Upscaler Precision: Faithful, non-generative video upscaling with a strength blend and FPS boost
  • Video Upscaler: Standard video upscaler with creativity controls, processing flavors, and a Turbo endpoint
  • VFX: Apply cinematic visual effects to videos with professional filters