Professional photo upscaling powered by Topaz Labs. Gigapixel precision models (Standard V2, High Fidelity, Low Resolution, CGI, Text Refine) enlarge images faithfully up to 4x. Best for photos that must stay true to the original.

topaz/upscale/image/precision

Learn more about Upscale

Inference

Commercial use

Partner

Schema

LLMs

Playground API Examples

SandboxOpen in Sandbox

topaz/upscale/image/precision

Schema

SandboxOpen in Sandbox

topaz/upscale/image/precision

Learn more about Upscale

Inference

Commercial use

Partner

Schema

LLMs

Playground API Examples

SandboxOpen in Sandbox

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Topaz Precision Image Upscale (Gigapixel)

Classic (non-generative) upscaling that stays faithful to the source pixels.

Models

Standard V2 — general-purpose, fits most photos.
High Fidelity V2 — preserves fine detail in professional shots.
Low Resolution V2 — recovers compressed, low-quality sources.
CGI — artwork and rendered graphics.
Text Refine — keeps text and shapes crisp.
Faces — Face Recovery 3, restores facial detail.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("topaz/upscale/image/precision", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/model_tests/codeformer/codeformer_poor_1.jpeg"
  },
  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);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In an environment where you cannot set environment variables, you can configure the API key manually on the client.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("topaz/upscale/image/precision", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/model_tests/codeformer/codeformer_poor_1.jpeg"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("topaz/upscale/image/precision", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("topaz/upscale/image/precision", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

image_url string* required

Url of the image to be upscaled

model ModelEnum

Precision upscaling model. Standard V2 fits most photos; High Fidelity V3/V2 preserve detail in professional shots; Low Resolution V2 recovers compressed sources; CGI targets art and rendered graphics; Text Refine keeps text and shapes crisp; Faces restores facial detail with Face Recovery 3. Default value: "Standard V2"

Possible enum values: Standard V2, High Fidelity V3, High Fidelity V2, Low Resolution V2, CGI, Text Refine, Faces

upscale_factor float

Factor to upscale the image by (e.g. 2.0 doubles width and height) Default value: 2

crop_to_fill boolean

output_format OutputFormatEnum

Output format of the upscaled image. Default value: "jpeg"

Possible enum values: jpeg, png

subject_detection SubjectDetectionEnum

Subject detection mode for the image enhancement. Default value: "All"

Possible enum values: All, Foreground, Background

face_enhancement boolean

Whether to apply face enhancement to the image. Default value: true

face_enhancement_creativity float

Creativity level for face enhancement. 0.0 means no creativity, 1.0 means maximum creativity. Ignored if face enhancement is disabled.

face_enhancement_strength float

Strength of the face enhancement. 0.0 means no enhancement, 1.0 means maximum enhancement. Ignored if face enhancement is disabled. Default value: 0.8

sharpen float

Sharpening level (0.0-1.0). Default varies by model.

denoise float

Denoising level (0.0-1.0). Default varies by model.

fix_compression float

Compression artifact removal level (0.0-1.0). Not supported by the CGI model.

strength float

Enhancement strength (0.01-1.0). Applies to Text Refine model only.

enable_safety_checker boolean

Authorized callers may set false to request relaxed screening. Default value: true

{
  "image_url": "https://storage.googleapis.com/falserverless/model_tests/codeformer/codeformer_poor_1.jpeg",
  "model": "Standard V2",
  "upscale_factor": 2,
  "output_format": "jpeg",
  "subject_detection": "All",
  "face_enhancement": true,
  "face_enhancement_strength": 0.8,
  "enable_safety_checker": true
}

Output#

image File* required

The upscaled image.

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  }
}

Other types#

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

Related Models

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

About

Topaz Precision Image Upscale (Gigapixel)

Classic (non-generative) upscaling that stays faithful to the source pixels.

Models

Standard V2 — general-purpose, fits most photos.
High Fidelity V2 — preserves fine detail in professional shots.
Low Resolution V2 — recovers compressed, low-quality sources.
CGI — artwork and rendered graphics.
Text Refine — keeps text and shapes crisp.
Faces — Face Recovery 3, restores facial detail.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("topaz/upscale/image/precision", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/model_tests/codeformer/codeformer_poor_1.jpeg"
  },
  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);

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In an environment where you cannot set environment variables, you can configure the API key manually on the client.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("topaz/upscale/image/precision", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/model_tests/codeformer/codeformer_poor_1.jpeg"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("topaz/upscale/image/precision", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("topaz/upscale/image/precision", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

image_url string* required

Url of the image to be upscaled

model ModelEnum

Possible enum values: Standard V2, High Fidelity V3, High Fidelity V2, Low Resolution V2, CGI, Text Refine, Faces

upscale_factor float

Factor to upscale the image by (e.g. 2.0 doubles width and height) Default value: 2

crop_to_fill boolean

output_format OutputFormatEnum

Output format of the upscaled image. Default value: "jpeg"

Possible enum values: jpeg, png

subject_detection SubjectDetectionEnum

Subject detection mode for the image enhancement. Default value: "All"

Possible enum values: All, Foreground, Background

face_enhancement boolean

Whether to apply face enhancement to the image. Default value: true

face_enhancement_creativity float

Creativity level for face enhancement. 0.0 means no creativity, 1.0 means maximum creativity. Ignored if face enhancement is disabled.

face_enhancement_strength float

Strength of the face enhancement. 0.0 means no enhancement, 1.0 means maximum enhancement. Ignored if face enhancement is disabled. Default value: 0.8

sharpen float

Sharpening level (0.0-1.0). Default varies by model.

denoise float

Denoising level (0.0-1.0). Default varies by model.

fix_compression float

Compression artifact removal level (0.0-1.0). Not supported by the CGI model.

strength float

Enhancement strength (0.01-1.0). Applies to Text Refine model only.

enable_safety_checker boolean

Authorized callers may set false to request relaxed screening. Default value: true

{
  "image_url": "https://storage.googleapis.com/falserverless/model_tests/codeformer/codeformer_poor_1.jpeg",
  "model": "Standard V2",
  "upscale_factor": 2,
  "output_format": "jpeg",
  "subject_detection": "All",
  "face_enhancement": true,
  "face_enhancement_strength": 0.8,
  "enable_safety_checker": true
}

Output#

image File* required

The upscaled image.

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019
  }
}

Other types#

File#

url string* required

The URL where the file can be downloaded from.

content_type string

The mime type of the file.

file_name string

The name of the file. It will be auto-generated if not provided.

file_size integer

The size of the file in bytes.

topaz/upscale/image/precision

Table of contents

Table of contents

topaz/upscale/image/precision

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

Topaz Precision Image Upscale (Gigapixel)

Models

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

File#

Related Models

Table of contents

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

Topaz Precision Image Upscale (Gigapixel)

Models

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

File#

Related Models