andreasjansson/flux-schnell-redux-layers | Run with an API on Replicate

Input

prompt

*string

Shift + Return to add a new line

Prompt for generated image

redux_image

file

Input image to condition your output on. This replaces prompt for FLUX.1 Redux models

aspect_ratio

string

Aspect ratio for the generated image

Default: "1:1"

num_outputs

integer

(minimum: 1, maximum: 4)

Number of outputs to generate

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 4)

Number of denoising steps. 4 is recommended, and lower number of steps produce lower quality outputs, faster.

Default: 4

seed

integer

Random seed. Set for reproducible generation

output_format

string

Format of the output images

Default: "webp"

output_quality

integer

(minimum: 0, maximum: 100)

Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs

Default: 80

disable_safety_checker

boolean

Disable safety checker for generated images.

Default: false

megapixels

string

Approximate number of megapixels for generated image

Default: "1"

redux_double_layers

number[]

Enter a string of numbers separated by commas

Double stream block layers where redux image is enabled. 0-18

Default: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18]

redux_single_layers

number[]

Enter a string of numbers separated by commas

Single stream block layers where redux image is enabled. 0-37

Default: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37]

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

npx create-replicate --model=andreasjansson/flux-schnell-redux-layers

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 andreasjansson/flux-schnell-redux-layers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "andreasjansson/flux-schnell-redux-layers:f6f727a0b9b86a45eed5497a8a8877858dc9bc8710ea7e044b0c277b4e875e65",
  {
    input: {
      prompt: "A watercolor painting",
      megapixels: "1",
      num_outputs: 4,
      redux_image: "https://replicate.delivery/pbxt/M1lKeCcsHPvzb5wNNsajPvKD4l87ESgJv0a6EmEWzHl9QUdk/0_1.webp",
      aspect_ratio: "1:1",
      output_format: "webp",
      output_quality: 80,
      num_inference_steps: 4,
      redux_double_layers: [18],
      redux_single_layers: [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37]
    }
  }
);

// 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 andreasjansson/flux-schnell-redux-layers using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "andreasjansson/flux-schnell-redux-layers:f6f727a0b9b86a45eed5497a8a8877858dc9bc8710ea7e044b0c277b4e875e65",
    input={
        "prompt": "A watercolor painting",
        "megapixels": "1",
        "num_outputs": 4,
        "redux_image": "https://replicate.delivery/pbxt/M1lKeCcsHPvzb5wNNsajPvKD4l87ESgJv0a6EmEWzHl9QUdk/0_1.webp",
        "aspect_ratio": "1:1",
        "output_format": "webp",
        "output_quality": 80,
        "num_inference_steps": 4,
        "redux_double_layers": [18],
        "redux_single_layers": [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37]
    }
)

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

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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 andreasjansson/flux-schnell-redux-layers 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": "andreasjansson/flux-schnell-redux-layers:f6f727a0b9b86a45eed5497a8a8877858dc9bc8710ea7e044b0c277b4e875e65",
    "input": {
      "prompt": "A watercolor painting",
      "megapixels": "1",
      "num_outputs": 4,
      "redux_image": "https://replicate.delivery/pbxt/M1lKeCcsHPvzb5wNNsajPvKD4l87ESgJv0a6EmEWzHl9QUdk/0_1.webp",
      "aspect_ratio": "1:1",
      "output_format": "webp",
      "output_quality": 80,
      "num_inference_steps": 4,
      "redux_double_layers": [18],
      "redux_single_layers": [18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37]
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-11-24T09:40:06.088083Z",
  "created_at": "2024-11-24T09:40:01.134000Z",
  "data_removed": false,
  "error": null,
  "id": "ezd9x3psxsrmc0ckbp596aqrpm",
  "input": {
    "prompt": "A watercolor painting",
    "megapixels": "1",
    "num_outputs": 4,
    "redux_image": "https://replicate.delivery/pbxt/M1lKeCcsHPvzb5wNNsajPvKD4l87ESgJv0a6EmEWzHl9QUdk/0_1.webp",
    "aspect_ratio": "1:1",
    "output_format": "webp",
    "output_quality": 80,
    "num_inference_steps": 4,
    "redux_double_layers": [
      18
    ],
    "redux_single_layers": [
      18,
      19,
      20,
      21,
      22,
      23,
      24,
      25,
      26,
      27,
      28,
      29,
      30,
      31,
      32,
      33,
      34,
      35,
      36,
      37
    ]
  },
  "logs": "running bf16 model, fp8 disabled\nUsing seed: 21492\n0it [00:00, ?it/s]\n1it [00:00,  6.57it/s]\n2it [00:00,  4.78it/s]\n3it [00:00,  4.40it/s]\n4it [00:00,  4.24it/s]\n4it [00:00,  4.45it/s]\n0it [00:00, ?it/s]\n1it [00:00,  3.98it/s]\n2it [00:00,  3.99it/s]\n3it [00:00,  4.00it/s]\n4it [00:01,  4.00it/s]\n4it [00:01,  4.00it/s]\n0it [00:00, ?it/s]\n1it [00:00,  3.99it/s]\n2it [00:00,  3.99it/s]\n3it [00:00,  3.99it/s]\n4it [00:01,  4.00it/s]\n4it [00:01,  3.99it/s]\n0it [00:00, ?it/s]\n1it [00:00,  3.99it/s]\n2it [00:00,  4.00it/s]\n3it [00:00,  3.99it/s]\n4it [00:01,  3.99it/s]\n4it [00:01,  3.99it/s]\nTotal safe images: 4 out of 4",
  "metrics": {
    "predict_time": 4.946641349,
    "total_time": 4.954083
  },
  "output": [
    "https://replicate.delivery/xezq/ShiMfqpnk3Wxe0NgDsErp8mft7cdGS0fbIsbwwZUKCeyOTheE/out-0.webp",
    "https://replicate.delivery/xezq/9JMZmtlXFfxPJqeihP6hQj8UccMjfJYJ7FPJo69wOwSszUonA/out-1.webp",
    "https://replicate.delivery/xezq/DEShubetRwUnHKzoiuxvjsIcMCseK4DOre7fJkviVH4ZnpQPB/out-2.webp",
    "https://replicate.delivery/xezq/lSrNGH5SZE4xJpY3i7WDwcwynzeGYzbFCMnUYCqpUKW7MF6JA/out-3.webp"
  ],
  "started_at": "2024-11-24T09:40:01.141441Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/bcwr-zr25doetrnzhvc7gef4s27qpf5yr73evqufepcfglaesgqjcd7hq",
    "get": "https://api.replicate.com/v1/predictions/ezd9x3psxsrmc0ckbp596aqrpm",
    "cancel": "https://api.replicate.com/v1/predictions/ezd9x3psxsrmc0ckbp596aqrpm/cancel"
  },
  "version": "f6f727a0b9b86a45eed5497a8a8877858dc9bc8710ea7e044b0c277b4e875e65"
}

Generated in

5.0 seconds

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Examples

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Run time and cost

This model costs approximately $0.32 to run on Replicate, or 3 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia H100 GPU hardware. Predictions typically complete within 4 minutes.