replicategithubwc/flux-dev-inpainting-controlnet

replicategithubwc / flux-dev-inpainting-controlnet

Cold

Public
19 runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

a photo of an astronaut riding a horse on marsa photo of an astronaut riding a horse on mars

Input prompt

Default: "a photo of an astronaut riding a horse on mars"

image

file

Input image for img2img or inpaint mode

mask

file

Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.

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: 50)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 7.5

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image

Default: 0.8

condition_scale

number

(minimum: 0, maximum: 1)

Condition scale

Default: 0.7

control_guidance_start

number

(minimum: 0, maximum: 1)

Condition scale

Default: 0.2

control_guidance_end

number

(minimum: 0, maximum: 1)

Condition scale

Default: 0.8

seed

integer

Random seed. Leave blank to randomize the seed

output_quality

integer

(minimum: 0, maximum: 100)

Quality of the output image, from 0 to 100. 100 is best quality, 0 is lowest quality.

Default: 80

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

npx create-replicate --model=replicategithubwc/flux-dev-inpainting-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";

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

Run replicategithubwc/flux-dev-inpainting-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-inpainting-controlnet:73c127656cbfe806bed18b3a1c0b3b2e6c5f18ca1f1e2958ed372bdab63e40c1",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "a photo of an astronaut riding a horse on mars",
      num_outputs: 1,
      guidance_scale: 7.5,
      output_quality: 80,
      condition_scale: 0.7,
      prompt_strength: 0.8,
      num_inference_steps: 50,
      control_guidance_end: 0.8,
      control_guidance_start: 0.2
    }
  }
);
console.log(output);

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

output = replicate.run(
    "replicategithubwc/flux-dev-inpainting-controlnet:73c127656cbfe806bed18b3a1c0b3b2e6c5f18ca1f1e2958ed372bdab63e40c1",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "a photo of an astronaut riding a horse on mars",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "output_quality": 80,
        "condition_scale": 0.7,
        "prompt_strength": 0.8,
        "num_inference_steps": 50,
        "control_guidance_end": 0.8,
        "control_guidance_start": 0.2
    }
)
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 replicategithubwc/flux-dev-inpainting-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": "73c127656cbfe806bed18b3a1c0b3b2e6c5f18ca1f1e2958ed372bdab63e40c1",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a photo of an astronaut riding a horse on mars",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "output_quality": 80,
      "condition_scale": 0.7,
      "prompt_strength": 0.8,
      "num_inference_steps": 50,
      "control_guidance_end": 0.8,
      "control_guidance_start": 0.2
    }
  }' \
  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-inpainting-controlnet@sha256:73c127656cbfe806bed18b3a1c0b3b2e6c5f18ca1f1e2958ed372bdab63e40c1 \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="a photo of an astronaut riding a horse on mars"' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'output_quality=80' \
  -i 'condition_scale=0.7' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=50' \
  -i 'control_guidance_end=0.8' \
  -i 'control_guidance_start=0.2'

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-inpainting-controlnet@sha256:73c127656cbfe806bed18b3a1c0b3b2e6c5f18ca1f1e2958ed372bdab63e40c1
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "a photo of an astronaut riding a horse on mars",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "output_quality": 80,
      "condition_scale": 0.7,
      "prompt_strength": 0.8,
      "num_inference_steps": 50,
      "control_guidance_end": 0.8,
      "control_guidance_start": 0.2
    }
  }' \
  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.