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CCSR Upscaler Image to Image

fal-ai/ccsr
SOTA Image Upscaler
Inference
Commercial use

About

Generate 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

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/ccsr", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/gallery/blue-bird.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"
});

3. Queue#

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/ccsr", {
  input: {
    image_url: "https://storage.googleapis.com/falserverless/gallery/blue-bird.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/ccsr", {
  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/ccsr", {
  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);

Read more about file handling in our file upload guide.

5. Schema#

Input#

image_url string* required

The text prompt you would like to convert to speech.

scale float

The scale of the output image. The higher the scale, the bigger the output image will be. Default value: 2

tile_diffusion TileDiffusionEnum

If specified, a patch-based sampling strategy will be used for sampling. Default value: "none"

Possible enum values: none, mix, gaussian

tile_diffusion_size integer

Size of patch. Default value: 1024

tile_diffusion_stride integer

Stride of sliding patch. Default value: 512

tile_vae boolean

If specified, a patch-based sampling strategy will be used for VAE decoding.

tile_vae_decoder_size integer

Size of VAE patch. Default value: 226

tile_vae_encoder_size integer

Size of latent image Default value: 1024

steps integer

The number of steps to run the model for. The higher the number the better the quality and longer it will take to generate. Default value: 50

t_max float

The ending point of uniform sampling strategy. Default value: 0.6667

t_min float

The starting point of uniform sampling strategy. Default value: 0.3333

color_fix_type ColorFixTypeEnum

Type of color correction for samples. Default value: "adain"

Possible enum values: none, wavelet, adain

seed integer

Seed for reproducibility. Different seeds will make slightly different results.

{
  "image_url": "https://storage.googleapis.com/falserverless/gallery/blue-bird.jpeg",
  "scale": 2,
  "tile_diffusion": "none",
  "tile_diffusion_size": 1024,
  "tile_diffusion_stride": 512,
  "tile_vae_decoder_size": 226,
  "tile_vae_encoder_size": 1024,
  "steps": 50,
  "t_max": 0.6667,
  "t_min": 0.3333,
  "color_fix_type": "adain"
}

Output#

image Image* required

The generated image file info.

seed integer* required

The seed used for the generation.

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  }
}

Other types#

Image#

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

width integer

The width of the image in pixels.

height integer

The height of the image in pixels.

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