# Topaz Video Upscale > Professional-grade video upscaling using Topaz technology. Enhance your videos with high-quality upscaling. ## Overview - **Endpoint**: `https://fal.run/fal-ai/topaz/upscale/video` - **Model ID**: `fal-ai/topaz/upscale/video` - **Category**: video-to-video - **Kind**: inference **Tags**: upscaling, high-res ## Pricing For every second a video your request will cost **$0.01** for up to **720p**, **$0.02** for **720p** to **1080p**, and **$0.08** for above **1080p** output. Price doubles for **60fps** output. For **Gaia 2** output costs half of the prices. For more details, see [fal.ai pricing](https://fal.ai/pricing). ## API Information This model can be used via our HTTP API or more conveniently via our client libraries. See the input and output schema below, as well as the usage examples. ### Input Schema The API accepts the following input parameters: - **`video_url`** (`string`, _required_): URL of the video to upscale - Examples: "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4" - **`model`** (`ModelEnum`, _optional_): Video enhancement model, grouped by family. Precision — Proteus fits most footage, Artemis denoises and sharpens degraded sources, Gaia HQ/CG refine rendered content, Gaia 2 handles animation and motion graphics at 2x. Denoise — Nyx models are dedicated noise reduction. Generative — Starlight models use diffusion for restoration and upscaling. Starlight Precise 1, Starlight Precise 2 and Starlight Fast 1 are deprecated by Topaz; prefer Starlight Precise 2.5 or Starlight Fast 2. Default value: `"Proteus"` - Default: `"Proteus"` - Options: `"Proteus"`, `"Artemis HQ"`, `"Artemis MQ"`, `"Artemis LQ"`, `"Gaia HQ"`, `"Gaia CG"`, `"Gaia 2"`, `"Nyx"`, `"Nyx Fast"`, `"Nyx XL"`, `"Nyx HF"`, `"Starlight Precise 2.5"`, `"Starlight HQ"`, `"Starlight Mini"`, `"Starlight Sharp"`, `"Starlight Fast 2"`, `"Starlight Precise 1"`, `"Starlight Precise 2"`, `"Starlight Fast 1"` - **`upscale_factor`** (`float`, _optional_): Factor to upscale the video by (e.g. 2.0 doubles width and height) Default value: `2` - Default: `2` - Range: `1` to `4` - **`target_fps`** (`integer`, _optional_): Target FPS for frame interpolation. If set, frame interpolation will be enabled. - Range: `16` to `60` - **`compression`** (`float`, _optional_): Compression artifact removal level (0.0-1.0). Default varies by model. - Range: `0` to `1` - **`noise`** (`float`, _optional_): Noise reduction level (0.0-1.0). Default varies by model. - Range: `0` to `1` - **`halo`** (`float`, _optional_): Halo reduction level (0.0-1.0). Default varies by model. - Range: `0` to `1` - **`grain`** (`float`, _optional_): Film grain amount (0.0-0.1). Default varies by model. - Range: `0` to `0.1`, step: `0.01` - **`recover_detail`** (`float`, _optional_): Recover original detail level (0.0-1.0). Higher values preserve more original detail. - Range: `0` to `1` - **`H264_output`** (`boolean`, _optional_): Whether to use H264 codec for output video. Default is H265. - Default: `false` **Required Parameters Example**: ```json { "video_url": "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4" } ``` **Full Example**: ```json { "video_url": "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4", "model": "Proteus", "upscale_factor": 2 } ``` ### Output Schema The API returns the following output format: - **`video`** (`File`, _required_): The upscaled video file - Examples: {"url":"https://v3.fal.media/files/penguin/ztj_LB4gQlW6HIfVs8zX4_upscaled.mp4"} **Example Response**: ```json { "video": { "url": "https://v3.fal.media/files/penguin/ztj_LB4gQlW6HIfVs8zX4_upscaled.mp4" } } ``` ## Usage Examples ### cURL ```bash curl --request POST \ --url https://fal.run/fal-ai/topaz/upscale/video \ --header "Authorization: Key $FAL_KEY" \ --header "Content-Type: application/json" \ --data '{ "video_url": "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4" }' ``` ### Python Ensure you have the Python client installed: ```bash pip install fal-client ``` Then use the API client to make requests: ```python import fal_client def on_queue_update(update): if isinstance(update, fal_client.InProgress): for log in update.logs: print(log["message"]) result = fal_client.subscribe( "fal-ai/topaz/upscale/video", arguments={ "video_url": "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4" }, with_logs=True, on_queue_update=on_queue_update, ) print(result) ``` ### JavaScript Ensure you have the JavaScript client installed: ```bash npm install --save @fal-ai/client ``` Then use the API client to make requests: ```javascript import { fal } from "@fal-ai/client"; const result = await fal.subscribe("fal-ai/topaz/upscale/video", { input: { video_url: "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4" }, logs: true, onQueueUpdate: (update) => { if (update.status === "IN_PROGRESS") { update.logs.map((log) => log.message).forEach(console.log); } }, }); console.log(result.data); console.log(result.requestId); ``` ## Additional Resources ### Documentation - [Model Playground](https://fal.ai/models/fal-ai/topaz/upscale/video) - [API Documentation](https://fal.ai/models/fal-ai/topaz/upscale/video/api) - [OpenAPI Schema](https://fal.ai/api/openapi/queue/openapi.json?endpoint_id=fal-ai/topaz/upscale/video) ### fal.ai Platform - [Platform Documentation](https://fal.ai/docs/documentation) - [Python Client](https://fal.ai/docs/api-reference/client-libraries/python) - [JavaScript Client](https://fal.ai/docs/api-reference/client-libraries/javascript) ### Other agent-readable surfaces This file covers one model. To find anything else: - [Platform overview](https://fal.ai/llms.txt): Entry points and representative endpoint IDs - [Documentation index](https://fal.ai/docs/llms.txt): Every documentation page - [Full documentation text](https://fal.ai/docs/llms-full.txt): The whole documentation inlined - Any other model: `https://fal.ai/models//llms.txt`