CCSR Upscaler

fal-ai/ccsr
Inference
Commercial use

1. Calling the API#

Install the client#

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

npm install --save @fal-ai/serverless-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 * as fal from "@fal-ai/serverless-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);
    }
  },
});

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 * as fal from "@fal-ai/serverless-client";

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

3. 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 * as fal from "@fal-ai/serverless-client";

// Upload a file (you can get a file reference from an input element or a drag-and-drop event)
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

// Use the URL in your request
const result = await fal.subscribe("fal-ai/ccsr", { image_url: url });

Read more about file handling in our file upload guide.

4. Schema#

Input#

image_url*string

The text prompt you would like to convert to speech.

scalefloat

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

tile_diffusionTileDiffusionEnum

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

Possible values: "none", "mix", "gaussian"

tile_diffusion_sizeinteger

Size of patch. Default value: 1024

tile_diffusion_strideinteger

Stride of sliding patch. Default value: 512

tile_vaeboolean

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

tile_vae_decoder_sizeinteger

Size of VAE patch. Default value: 226

tile_vae_encoder_sizeinteger

Size of latent image Default value: 1024

stepsinteger

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_maxfloat

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

t_minfloat

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

color_fix_typeColorFixTypeEnum

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

Possible values: "none", "wavelet", "adain"

seedinteger

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

The generated image file info.

seed*integer

The seed used for the generation.

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