TopazTopaz Video Upscale API

Name: Topaz Video Upscale API
Author: Topaz

Video upscaling and enhancement with Topaz AI — Proteus, Artemis, Nyx, Gaia and Starlight models.

Source Video

Billed at this model’s published rate.

Result

Your generations will appear here

Other models from Topaz

Same vendor, same API call — the differences are below.

Model	Use case	Price	Resolution	Duration	Added
Topaz Image Upscale	Image → Image	No rate	—	—	Mar 2026

Install the SDK

TypeScript targets Node 20+ and ships with types; Python needs 3.10+ and is async. Over REST there is nothing to install: call https://api.picsart.com from any HTTP client.

bash

npm install @picsart/ai-sdk

Authenticate

Send your Picsart API key as a bearer token. The SDK does it for you once the client is created.

import { createClient } from '@picsart/ai-sdk';

const ai = createClient({
  apiKey: process.env.PICSART_API_KEY, // sent as: Authorization: Bearer <key>
  apiUrl: 'https://api.picsart.com',
});

python

import os

from picsart_ai_sdk import create_client

ai = create_client(
    api_key=os.environ["PICSART_API_KEY"],  # sent as: Authorization: Bearer <key>
    api_url="https://api.picsart.com",
)

Make a request

Call the model by its id. Over REST, one endpoint serves every model: POST /workflows/v1/models/execute with the model id and its input in the body. The outer params is the envelope every workflow takes; the inner one is this model's input, with the same names generate() takes.

import { createClient } from '@picsart/ai-sdk';

const ai = createClient({
  apiKey: process.env.PICSART_API_KEY,
  apiUrl: 'https://api.picsart.com',
});

const result = await ai.generate('topaz-upscale-video', {
  videoUrl: "https://cdn.example.com/input.mp4"
});

console.log(result.url); // video URL

python

import asyncio
import os

from picsart_ai_sdk import create_client


async def main() -> None:
    async with create_client(
        api_key=os.environ["PICSART_API_KEY"],
        api_url="https://api.picsart.com",
    ) as ai:
        result = await ai.generate("topaz-upscale-video", {
            "videoUrl": "https://cdn.example.com/input.mp4",
        })

        print(result.url)  # video URL


asyncio.run(main())

bash

curl -X POST 'https://api.picsart.com/workflows/v1/models/execute' \
  -H 'Authorization: Bearer $PICSART_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
  "params": {
    "model": "topaz-upscale-video",
    "params": {
      "videoUrl": "https://cdn.example.com/input.mp4"
    }
  }
}'

Async (submit & poll)

This model can run longer than the sync limit (~20s). generate() waits for you; to hold on to the job instead, submit it and poll. Over REST, send it to /workflows/v1/models/submit and poll /workflows/v1/models/{taskId}/result until response.status is COMPLETED or FAILED.

import { createClient } from '@picsart/ai-sdk';

const ai = createClient({
  apiKey: process.env.PICSART_API_KEY,
  apiUrl: 'https://api.picsart.com',
});

// Returns the generation id at once — store it to pick the job up later.
const generationId = await ai.submit('topaz-upscale-video', {
  videoUrl: "https://cdn.example.com/input.mp4"
});

// Polls until the job completes; throws if it fails or is cancelled.
const result = await ai.result('topaz-upscale-video', generationId);

console.log(result.url); // video URL

python

import asyncio
import os

from picsart_ai_sdk import create_client


async def main() -> None:
    async with create_client(
        api_key=os.environ["PICSART_API_KEY"],
        api_url="https://api.picsart.com",
    ) as ai:
        # Returns the generation id at once — store it to pick the job up later.
        generation_id = await ai.submit("topaz-upscale-video", {
            "videoUrl": "https://cdn.example.com/input.mp4",
        })

        # Polls until the job completes; raises ApiError if it fails or is cancelled.
        result = await ai.result("topaz-upscale-video", generation_id)

        print(result.url)  # video URL


asyncio.run(main())

bash

# 1. Submit the job
curl -X POST 'https://api.picsart.com/workflows/v1/models/submit' \
  -H 'Authorization: Bearer $PICSART_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
  "params": {
    "model": "topaz-upscale-video",
    "params": {
      "videoUrl": "https://cdn.example.com/input.mp4"
    }
  }
}'
# → { "status": "success", "response": { "id": "task_abc123" } }

# 2. Poll until response.status is COMPLETED
curl 'https://api.picsart.com/workflows/v1/models/task_abc123/result' \
  -H 'Authorization: Bearer $PICSART_API_KEY'

Parameters

2 parameters, the same for the SDK and for the inner params over REST. Required ones must be supplied; the rest fall back to their defaults.

Parameter	Type	Required	Default	Details
`videoUrl` The video to work on, as a URL.	`file (video)`	yes	`—`	—
`model` Model	`enum`	no	`Proteus`	Proteus, Artemis HQ, Artemis MQ, Artemis LQ, Nyx, Nyx Fast, Nyx XL, Nyx HF, Gaia HQ, Gaia CG, Gaia 2, Starlight Precise 2.5, Starlight HQ, Starlight Mini, Starlight Sharp, Starlight Fast 2

Response

generate() resolves once the job completes. A video URL. Over REST the output is at response.result, inside the gateway's status envelope with the task id, its status and the amount charged.

json

{
  "items": [
    {
      "url": "https://cdn.picsart.com/…/result"
    }
  ],
  "model": "topaz-upscale-video"
}

json

{
  "status": "success",
  "response": {
    "id": "task_abc123",
    "status": "COMPLETED",
    "result": {
      "url": "https://cdn.picsart.com/…/result",
      "items": [
        {
          "url": "https://cdn.picsart.com/…/result"
        }
      ],
      "model": "topaz-upscale-video"
    }
  }
}

Overview

Alongside the unified model APIs, we expose compatibility APIs that take each vendor's original parameters exactly as the vendor defines them — nothing renamed, nothing reshaped on the way through.

That makes them the shortest path onto Picsart if you already work with the vendor directly: the request bodies you've already written keep working as they are. You keep their parameter names and defaults, and you get the vendor's full parameter set rather than the subset that is shared across every model.

The cost is that a request is written for one vendor — switching models later means rewriting it, and results come back in the vendor's own shape. When you'd rather write once and change models freely, use the unified API.

Make a request

Call the workflow with ai.apis.run() in TypeScript or Python, or hit /workflows/{workflow}/execute directly — params is passed through untouched either way.

These endpoints are addressed by workflow name rather than model id: the one in this model's header, since one model runs as one workflow. Authentication is unchanged — your Picsart API key as a bearer token (see Authentication).

import { createClient, ApiRunMode } from '@picsart/ai-sdk';

const ai = createClient({
  apiKey: process.env.PICSART_API_KEY,
  apiUrl: 'https://api.picsart.com',
});

// Calls the 'topaz/upscale/video' workflow directly — params are sent as-is.
const { result, usage } = await ai.apis.run('topaz/upscale/video', {
  video_url: "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4"
}, {
  mode: ApiRunMode.SYNC,
});

console.log(result); // workflow-specific output
console.log(usage?.credits); // amount charged, in $

python

import asyncio
import os

from picsart_ai_sdk import ApiRunMode, create_client


async def main() -> None:
    async with create_client(
        api_key=os.environ["PICSART_API_KEY"],
        api_url="https://api.picsart.com",
    ) as ai:
        # Calls the 'topaz/upscale/video' workflow directly — params are sent as-is.
        response = await ai.apis.run("topaz/upscale/video", {
            "video_url": "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4",
        }, mode=ApiRunMode.SYNC)

        print(response["result"])  # workflow-specific output
        print((response.get("usage") or {}).get("credits"))  # amount charged, in $


asyncio.run(main())

bash

curl -X POST 'https://api.picsart.com/workflows/topaz/upscale/video/execute' \
  -H 'Authorization: Bearer $PICSART_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
  "params": {
    "video_url": "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4"
  }
}'

Async (submit & poll)

This model can run longer than the sync limit (~20s). In the SDK, ai.apis.run() with ApiRunMode.ASYNC polls for you; over HTTP, submit and poll yourself.

import { createClient, ApiRunMode } from '@picsart/ai-sdk';

const ai = createClient({
  apiKey: process.env.PICSART_API_KEY,
  apiUrl: 'https://api.picsart.com',
});

// mode: ASYNC submits the job and polls under the hood — you just await.
const { result } = await ai.apis.run('topaz/upscale/video', {
  video_url: "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4"
}, {
  mode: ApiRunMode.ASYNC,
});

console.log(result);

python

import asyncio
import os

from picsart_ai_sdk import ApiRunMode, create_client


async def main() -> None:
    async with create_client(
        api_key=os.environ["PICSART_API_KEY"],
        api_url="https://api.picsart.com",
    ) as ai:
        # mode=ASYNC submits the job and polls under the hood — you just await.
        response = await ai.apis.run("topaz/upscale/video", {
            "video_url": "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4",
        }, mode=ApiRunMode.ASYNC)

        print(response["result"])


asyncio.run(main())

bash

# 1. Submit the job
curl -X POST 'https://api.picsart.com/workflows/topaz/upscale/video/submit' \
  -H 'Authorization: Bearer $PICSART_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
  "params": {
    "video_url": "https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4"
  }
}'
# → { "status": "success", "response": { "id": "wf_abc123" } }

# 2. Poll until response.status is COMPLETED
curl 'https://api.picsart.com/workflows/topaz/upscale/video/wf_abc123/result' \
  -H 'Authorization: Bearer $PICSART_API_KEY'

Parameters

12 parameters, sent inside params. These are the vendor's own names, so they line up one-for-one with the vendor's documentation. Required ones must be supplied; the rest fall back to their defaults.

Parameter	Type	Required	Default	Details
`target_fps` Target FPS for frame interpolation. If set, frame interpolation will be enabled.	`integer`	no	`—`	16–60
`upscale_factor` Factor to upscale the video by (e.g. 2.0 doubles width and height)	`number`	no	`2`	1–4
`enable_safety_checker`	`boolean`	no	`true`	—
`H264_output` Whether to use H264 codec for output video. Default is H265.	`boolean`	no	`false`	—
`video_url` URL of the video to upscale	`string`	yes	`—`	e.g. `https://v3.fal.media/files/kangaroo/y5-1YTGpun17eSeggZMzX_video-1733468228.mp4`
`compression` Compression artifact removal level (0.0-1.0). Default varies by model.	`number`	no	`—`	0–1
`halo` Halo reduction level (0.0-1.0). Default varies by model.	`number`	no	`—`	0–1
`recover_detail` Recover original detail level (0.0-1.0). Higher values preserve more original detail.	`number`	no	`—`	0–1
`noise` Noise reduction level (0.0-1.0). Default varies by model.	`number`	no	`—`	0–1
`grain` Film grain amount (0.0-0.1). Default varies by model.	`number`	no	`—`	0–0.1 · multiple of 0.01
`model` 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.	`string`	no	`Proteus`	`ProteusArtemis HQArtemis MQArtemis LQGaia HQGaia CGGaia 2NyxNyx FastNyx XLNyx HFStarlight Precise 2.5Starlight HQStarlight MiniStarlight SharpStarlight Fast 2Starlight Precise 1Starlight Precise 2Starlight Fast 1`
`options` Platform-specific options for safety checks and Picsart Drive integration.	`object`	no	`—`	—
└`safety_checks` Safety check configuration.	`object`	no	`—`	—
└`enabled` Enable or disable text safety checks. Defaults to true.	`boolean`	no	`true`	—
└`drive` Picsart Drive storage options. When provided, the result will be saved to the user's Drive.	`object`	no	`—`	—
└`name` Filename to save the result as in Picsart Drive.	`string`	yes	`—`	—
└`attributes` Additional file metadata attributes.	`object`	no	`—`	—
└`folder` Destination folder in Picsart Drive.	`object`	no	`—`	—

Response

Over HTTP the output arrives inside a status envelope, at response.result. ai.apis.run() unwraps that envelope for you and resolves to { result, usage } instead. Either way the result itself is the vendor's own shape.

json

{
  "result": {
    "video": {
      "file_size": 4404019,
      "file_name": "z9RV14K95DvU.png",
      "url": "https://cdn.picsart.com/…/result.mp4",
      "content_type": "image/png"
    }
  }
}

json

{
  "status": "success",
  "response": {
    "id": "wf_abc123",
    "status": "COMPLETED",
    "result": {
      "video": {
        "file_size": 4404019,
        "file_name": "z9RV14K95DvU.png",
        "url": "https://cdn.picsart.com/…/result.mp4",
        "content_type": "image/png"
      }
    }
  }
}

TopazTopaz Video Upscale API

Params

Result

Other models from Topaz

Install the SDK

Authenticate

Make a request

Async (submit & poll)

Parameters

Response

Overview

Make a request

Async (submit & poll)

Parameters

Response

Rates