Video Upscale

Increase a video's resolution and restore lost detail, up to 4K. Use when the user says "upscale this video", "make this clip higher-res / sharper", "enhance this footage", "remaster this old video", "4K this", "denoise this video", or "clean up this compressed clip". Covers video super-resolution and temporal detail restoration, not still images.

Take a low-resolution or soft video and return a sharper, higher-resolution one, up to 4K, with detail recovered and compression noise reduced. The lever is a dedicated video-to-video model that upscales every frame while holding temporal consistency, so the result stays stable across frames instead of shimmering. This is a thin capability: only three models cover it today, so routing is simple but options are limited.

Inputs to collect

• The source video. A URL or upload. (Ask only if none provided.)
• Target resolution or factor. A target like 1080p / 2K / 4K, or a 2x / 4x factor. What each model accepts differs, so confirm against the schema before promising one.
• What needs fixing: just too small/soft, or also noisy, compression-artifacted, or aliased. This nudges the model choice.
• Frame rate, if it should change (Topaz can resample fps). Otherwise keep the input's rate.

Models

• Default, highest quality: Topaz Starlight Precise 2.5 (topazlabs:starlight-precise@2.5). Diffusion-based enhancement that upscales, denoises, de-aliases, and sharpens with full temporal consistency and natural faces/skin. Best when detail recovery and a clean, photoreal result matter. Most expensive of the three.
• Enterprise 4K restoration: ByteDance Video Upscaler (bytedance:50@1). Boosts to 1080p, 2K, or 4K with denoising and motion enhancement, restores color, and cuts compression artifacts. Strong general pick for legacy films, UGC, and short clips. Cheapest per second.
• 2x / 4x with detail preservation: Bria Video Increase Resolution (bria:50@1). Upscales 2x or 4x while preserving detail and transparency. Good for product footage and design workflows that need clean output without rework.
• All three are video-to-video (op:upscale), not still-image upscalers. For images, use restore-and-upscale.
• Confirm the live model + its schema via the runware-models and runware-run skills before calling. Never hardcode a stale choice.

Workflow

1. Pick the model from the routing above based on quality bar, target resolution, and cost. Confirm it is live via runware-models.
2. Resolve the schema (runware-run) and confirm the input field and the resolution controls for that specific model. They differ (target width/height/fps on Topaz, upscaleFactor on Bria, server-chosen target on ByteDance).
3. Upload the source into inputs.video (required on all three).
4. Run async and poll. The taskType is upscale. Video upscaling is time-based, so it runs asynchronously: the task returns a taskUUID, then poll getResponse until terminal. Do not block a single sync call on a minutes-long job.
5. Read the result. It returns a videoURL. Inspect it at full resolution before returning.

Technique

• This is a one-pass operation, not a prompt. None of the three takes a positivePrompt. You are not directing content, you are enhancing the existing frames. Pick the model and the target, run once, and judge the output. Don't try to steer it with text.
• Don't chain upscalers. Upscale once at the target you actually want. Running 2x then 2x again, or feeding one model's output into another, compounds artifacts and temporal flicker instead of improving sharpness.
• Match the model to the footage. Genuinely degraded or noisy capture where detail must be recovered and faces must stay natural, lean Topaz (diffusion recovers and sharpens detail). Legacy film / UGC / compressed clips where the goal is a clean 1080p-to-4K bump at low cost, lean ByteDance. A straightforward 2x or 4x on already-decent footage (product, design), Bria is the lean pick.
• Temporal consistency is the whole point. A good result stays stable frame to frame. If you see shimmer, crawling edges, or detail that pops in and out between frames, that's the model struggling with this footage, not a tuning knob you missed. Switch model class and rerun rather than re-running the same one.
• Resolution is capped at 4K (3840x2160). Asking for more than the model supports either clamps or fails. Confirm the ceiling per model before promising a target.

Parameters that matter

• inputs.video is the source clip. Required on all three. Confirm the exact field against the live schema, never guess.
• Topaz (topazlabs:starlight-precise@2.5): width and height are the target output size and both are required, max 3840x2160. Aspect ratio is preserved by computing the factor from the shorter edge. fps is optional, 15 to 120, and must be at least the input's frame rate.
• Bria (bria:50@1): upscaleFactor is the only control, enum 2 or 4 (default 2). No width/height target.
• ByteDance (bytedance:50@1): no resolution parameters in the schema. The service targets 1080p / 2K / 4K from the input. Confirm how the target is selected via the live schema before promising a specific output size.
• outputType / outputFormat / deliveryMethod are video-output settings shared across all three. Defaults are fine for most runs.

Quality bar

• Output is genuinely sharper and higher-resolution and still looks like the original footage. No plastic over-smoothing, no invented detail that wasn't there.
• Temporal stability holds: no shimmer, edge crawl, or flicker introduced by the upscale. If it appears, switch model class and rerun.
• The output hit the requested resolution/factor and the schema accepted the target (Topaz width/height within 3840x2160, Bria upscaleFactor in {2,4}).
• The job was run async and the videoURL was retrieved, not blocked on a sync call.

Related skills

runware-run, runware-models, runware-prompting; restore-and-upscale (the still-image sibling: super-resolution and restoration for photos, where Topaz also appears as the video escape hatch).