Topaz | Image to Image

fal-ai/topaz/upscale/image

Use the powerful and accurate topaz image enhancer to enhance your images.

Inference

Commercial use

Schema

LLMs

Playground API

About

Upscale Image

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. Please check the migration guide for more information.

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("fal-ai/topaz/upscale/image", {
  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 case your app is running in an environment where you cannot set environment variables, you can set the API Key manually as a client configuration.

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("fal-ai/topaz/upscale/image", {
  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("fal-ai/topaz/upscale/image", {
  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("fal-ai/topaz/upscale/image", {
  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#

model ModelEnum

Model to use for image enhancement. Default value: "Standard V2"

Possible enum values: Low Resolution V2, Standard V2, CGI, High Fidelity V2, Text Refine, Recovery, Redefine, Recovery V2

upscale_factor float

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

crop_to_fill boolean

image_url string* required

Url of the image to be upscaled

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 ehnancement is disabled.

face_enhancement_strength float

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

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

Output#

image File* required

The upscaled image.

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

Other types#

SlowMotionRequest#

video_url string* required

URL of the video to apply slow motion to

slowdown_factor integer

Factor to slow down the video by (e.g. 4 means 4x slower) Default value: 4

upscale_factor float

Optional factor to upscale the video by (e.g. 2.0 doubles width and height)

StrongAIUpscaleRequest#

video_url string* required

URL of the AI-generated video to upscale

target_width integer

Target width of the output video Default value: 5120

target_height integer

Target height of the output video Default value: 2880

target_fps integer

Target FPS for the output video. Defaults to source FPS if not specified.

VideoUpscaleRequest#

video_url string* required

URL of the video to upscale

upscale_factor float

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

target_fps integer

Target FPS for frame interpolation. If set, frame interpolation will be enabled.

H264_output boolean

Whether to use H264 codec for output video. Default is H265.

RecoverUpscaleRequest#

video_url string* required

URL of the low-quality video to upscale and recover

upscale_factor float

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

target_fps integer

Target FPS for the output video. Defaults to source FPS if not specified.

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.

file_data string

File data

Topaz Image to Image

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

About

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#

SlowMotionRequest#

StrongAIUpscaleRequest#

VideoUpscaleRequest#

RecoverUpscaleRequest#

File#