qr2ai/img2paint_controlnet:1ee999c2 – Run with an API on Replicate

Version

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qr2ai /img2paint_controlnet:1ee999c2

Playground API

Input

qr_data

string

Shift + Return to add a new line

QR Code Data

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"

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: "low quality, bad quality, sketches"

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

condition_scale

number

(minimum: 0, maximum: 1)

controlnet conditioning scale for generalization

Default: 0.5

seed

integer

Random seed. Set to 0 to randomize the seed

Default: 0

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

npx create-replicate --model=qr2ai/img2paint_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 qr2ai/img2paint_controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "qr2ai/img2paint_controlnet:1ee999c2e1882ec9815de071fa1dd49a30c0c79d9ff2b5642a7dc0a1eabb3bf9",
  {
    input: {
      seed: 0,
      prompt: "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      condition_scale: 0.5,
      negative_prompt: "low quality, bad quality, sketches",
      num_inference_steps: 50
    }
  }
);
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 qr2ai/img2paint_controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "qr2ai/img2paint_controlnet:1ee999c2e1882ec9815de071fa1dd49a30c0c79d9ff2b5642a7dc0a1eabb3bf9",
    input={
        "seed": 0,
        "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
        "condition_scale": 0.5,
        "negative_prompt": "low quality, bad quality, sketches",
        "num_inference_steps": 50
    }
)
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 qr2ai/img2paint_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": "1ee999c2e1882ec9815de071fa1dd49a30c0c79d9ff2b5642a7dc0a1eabb3bf9",
    "input": {
      "seed": 0,
      "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      "condition_scale": 0.5,
      "negative_prompt": "low quality, bad quality, sketches",
      "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/qr2ai/img2paint_controlnet@sha256:1ee999c2e1882ec9815de071fa1dd49a30c0c79d9ff2b5642a7dc0a1eabb3bf9 \
  -i 'seed=0' \
  -i 'prompt="aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"' \
  -i 'condition_scale=0.5' \
  -i 'negative_prompt="low quality, bad quality, sketches"' \
  -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/qr2ai/img2paint_controlnet@sha256:1ee999c2e1882ec9815de071fa1dd49a30c0c79d9ff2b5642a7dc0a1eabb3bf9
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 0,
      "prompt": "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting",
      "condition_scale": 0.5,
      "negative_prompt": "low quality, bad quality, sketches",
      "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.