fater-ai/icl-bg-v1 | Run with an API on Replicate

fater-ai / icl-bg-v1

Cold

Public
13 runs
A100 (80GB)

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

subject_image

*file

The main foreground image to be relighted

background_image

*file

The background image that will be used to relight the main foreground image

prompt

*string

Shift + Return to add a new line

A text description guiding the relighting and generation process

appended_prompt

string

Shift + Return to add a new line

Additional text to be appended to the main prompt, enhancing image quality

Default: "best quality"

negative_prompt

string

Shift + Return to add a new line

lowres, bad anatomy, bad hands, cropped, worst qualitylowres, bad anatomy, bad hands, cropped, worst quality

A text description of attributes to avoid in the generated images

Default: "lowres, bad anatomy, bad hands, cropped, worst quality"

width

integer

The width of the generated images in pixels

Default: 1024

height

integer

The height of the generated images in pixels

Default: 1024

steps

integer

(minimum: 1, maximum: 100)

The number of diffusion steps to perform during generation (more steps generally improves image quality but increases processing time)

Default: 25

cfg

number

(minimum: 1, maximum: 32)

Classifier-Free Guidance scale - higher values encourage adherence to prompt, lower values encourage more creative interpretation

Default: 2

highres_scale

number

(minimum: 1, maximum: 3)

The multiplier for the final output resolution relative to the initial latent resolution

Default: 1.5

highres_denoise

number

(minimum: 0.1, maximum: 1)

Controls the amount of denoising applied when refining the high resolution output (higher = more adherence to the upscaled latent, lower = more creative details added)

Default: 0.5

light_source

string

The type and position of lighting to apply to the initial background latent

Default: "Use Background Image"

seed

integer

A fixed random seed for reproducible results (omit this parameter for a randomized seed)

number_of_images

integer

(minimum: 1, maximum: 12)

The number of unique images to generate from the given input and settings

Default: 1

output_format

string

The image file format of the generated output images

Default: "webp"

output_quality

integer

(minimum: 0, maximum: 100)

The image compression quality (for lossy formats like JPEG and WebP). 100 = best quality, 0 = lowest quality.

Default: 80

Run this model in Node.js with one line of code:

npx create-replicate --model=fater-ai/icl-bg-v1

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run fater-ai/icl-bg-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fater-ai/icl-bg-v1:477a8b434d14cfa77fce125263fee46e9dd52f1fabf2b18f0394cfd95703ce99",
  {
    input: {
      cfg: 2,
      steps: 25,
      width: 1024,
      height: 1024,
      light_source: "Use Background Image",
      highres_scale: 1.5,
      output_format: "webp",
      output_quality: 80,
      appended_prompt: "best quality",
      highres_denoise: 0.5,
      negative_prompt: "lowres, bad anatomy, bad hands, cropped, worst quality",
      number_of_images: 1
    }
  }
);

// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", output[0]);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run fater-ai/icl-bg-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fater-ai/icl-bg-v1:477a8b434d14cfa77fce125263fee46e9dd52f1fabf2b18f0394cfd95703ce99",
    input={
        "cfg": 2,
        "steps": 25,
        "width": 1024,
        "height": 1024,
        "light_source": "Use Background Image",
        "highres_scale": 1.5,
        "output_format": "webp",
        "output_quality": 80,
        "appended_prompt": "best quality",
        "highres_denoise": 0.5,
        "negative_prompt": "lowres, bad anatomy, bad hands, cropped, worst quality",
        "number_of_images": 1
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run fater-ai/icl-bg-v1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "fater-ai/icl-bg-v1:477a8b434d14cfa77fce125263fee46e9dd52f1fabf2b18f0394cfd95703ce99",
    "input": {
      "cfg": 2,
      "steps": 25,
      "width": 1024,
      "height": 1024,
      "light_source": "Use Background Image",
      "highres_scale": 1.5,
      "output_format": "webp",
      "output_quality": 80,
      "appended_prompt": "best quality",
      "highres_denoise": 0.5,
      "negative_prompt": "lowres, bad anatomy, bad hands, cropped, worst quality",
      "number_of_images": 1
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.