replicategithubwc/flux-dev-controlnet | Run with an API on Replicate

replicategithubwc / flux-dev-controlnet

Cold

Public
62 runs
A100 (80GB)

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

image

file

Input image for img2img or inpaint mode

prompt

string

Shift + Return to add a new line

aerial view, a futuristic research complex in a bright foggy jungle, hard lightingaerial view, a futuristic research complex in a bright foggy jungle, hard lighting

Input prompt

Default: "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"

model_type

string

ControlNet model type to use

Default: "canny"

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 3.5

condition_scale

number

(minimum: 0, maximum: 1)

controlnet conditioning scale for generalization

Default: 0.7

low_threshold

integer

(minimum: 1, maximum: 255)

Canny line detection low threshold

Default: 100

high_threshold

integer

(minimum: 1, maximum: 255)

Canny line detection high threshold

Default: 200

seed

integer

Random seed. Set to 0 to randomize the seed

Default: 0

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: 100

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

npx create-replicate --model=replicategithubwc/flux-dev-controlnet

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 replicategithubwc/flux-dev-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "replicategithubwc/flux-dev-controlnet:48fa2d298bf9bf083e31f60956f2960133b469995912a62241e2c10540f475d4",
  {
    input: {
      seed: 0,
      width: 1024,
      height: 1024,
      prompt: "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      model_type: "canny",
      num_outputs: 1,
      low_threshold: 100,
      guidance_scale: 3.5,
      high_threshold: 200,
      output_quality: 100,
      condition_scale: 0.7,
      num_inference_steps: 50
    }
  }
);

// 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 replicategithubwc/flux-dev-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "replicategithubwc/flux-dev-controlnet:48fa2d298bf9bf083e31f60956f2960133b469995912a62241e2c10540f475d4",
    input={
        "seed": 0,
        "width": 1024,
        "height": 1024,
        "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
        "model_type": "canny",
        "num_outputs": 1,
        "low_threshold": 100,
        "guidance_scale": 3.5,
        "high_threshold": 200,
        "output_quality": 100,
        "condition_scale": 0.7,
        "num_inference_steps": 50
    }
)

# 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 replicategithubwc/flux-dev-controlnet 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": "replicategithubwc/flux-dev-controlnet:48fa2d298bf9bf083e31f60956f2960133b469995912a62241e2c10540f475d4",
    "input": {
      "seed": 0,
      "width": 1024,
      "height": 1024,
      "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      "model_type": "canny",
      "num_outputs": 1,
      "low_threshold": 100,
      "guidance_scale": 3.5,
      "high_threshold": 200,
      "output_quality": 100,
      "condition_scale": 0.7,
      "num_inference_steps": 50
    }
  }' \
  https://api.replicate.com/v1/predictions

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

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/replicategithubwc/flux-dev-controlnet@sha256:48fa2d298bf9bf083e31f60956f2960133b469995912a62241e2c10540f475d4 \
  -i 'seed=0' \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"' \
  -i 'model_type="canny"' \
  -i 'num_outputs=1' \
  -i 'low_threshold=100' \
  -i 'guidance_scale=3.5' \
  -i 'high_threshold=200' \
  -i 'output_quality=100' \
  -i 'condition_scale=0.7' \
  -i 'num_inference_steps=50'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/replicategithubwc/flux-dev-controlnet@sha256:48fa2d298bf9bf083e31f60956f2960133b469995912a62241e2c10540f475d4
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 0,
      "width": 1024,
      "height": 1024,
      "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      "model_type": "canny",
      "num_outputs": 1,
      "low_threshold": 100,
      "guidance_scale": 3.5,
      "high_threshold": 200,
      "output_quality": 100,
      "condition_scale": 0.7,
      "num_inference_steps": 50
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

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.