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topaz-video-enhancement

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Use when enhancing, upscaling, restoring, denoising, sharpening, deinterlacing, stabilizing, motion-deblurring, colorizing, SDR-to-HDR converting, or frame-rate/slow-motion converting video (and secondarily images) with Topaz Labs — whether cleaning up AI-generated video, compressed UGC, or old archival footage, or building a generate-low-res-then-upscale delivery pipeline. Covers choosing the right named Topaz model for a specific footage problem, setting parameters, driving the Topaz Platform (Video/Image) REST API's async job lifecycle, judging when enhancement helps versus when it introduces artifacts (over-smoothing, temporal shimmer, face hallucination), and QA of enhanced output. Do not use for originating new video content, non-Topaz upscalers, or purely editorial cuts.

skills/providers/video-enhancement/topaz-video-enhancement/SKILL.md

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# Topaz Labs video (and image) enhancement

Topaz Labs makes AI enhancement/restoration models delivered three ways: the
**desktop apps** (Topaz Video AI, Gigapixel, Photo AI), and the **Topaz Platform
REST API** (`api.topazlabs.com`) which exposes the same model families for
programmatic, batch, and pipeline use. This skill is about applying the *models* to
solve concrete footage problems and about driving the *API* inside an automated
pipeline. When a task is a one-off manual cleanup, the desktop app is usually faster;
when enhancement is a repeatable stage in a generate → enhance → deliver pipeline,
use the API.

Enhancement is a **repair and finishing** operation, not a creation operation. It
cannot add information that was never plausibly there; it can only reconstruct,
interpolate, or invent detail. Every model on this page trades some fidelity for some
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