impetusdesign/vhx-gzp | Run with an API on Replicate

Input

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

npx create-replicate --model=impetusdesign/vhx-gzp

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 impetusdesign/vhx-gzp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "impetusdesign/vhx-gzp:662bb0370f4f6f6eb94cfc50c3c0b3a06dc15deeddd4a1e81c6a120a0135132c",
  {
    input: {
      seed: 92,
      image: "https://replicate.delivery/pbxt/MYqna52R9iABrM3HEJUKJwCe1lYaXPMJcwmviuhwtTZtvChs/0.png",
      prompt: "",
      num_outputs: 1,
      guidance_scale: 7,
      controlnet_scale: 0.5,
      ip_adapter_scale: 0.5,
      num_inference_steps: 10
    }
  }
);

// 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 impetusdesign/vhx-gzp using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "impetusdesign/vhx-gzp:662bb0370f4f6f6eb94cfc50c3c0b3a06dc15deeddd4a1e81c6a120a0135132c",
    input={
        "seed": 92,
        "image": "https://replicate.delivery/pbxt/MYqna52R9iABrM3HEJUKJwCe1lYaXPMJcwmviuhwtTZtvChs/0.png",
        "prompt": "",
        "num_outputs": 1,
        "guidance_scale": 7,
        "controlnet_scale": 0.5,
        "ip_adapter_scale": 0.5,
        "num_inference_steps": 10
    }
)
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 impetusdesign/vhx-gzp 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": "impetusdesign/vhx-gzp:662bb0370f4f6f6eb94cfc50c3c0b3a06dc15deeddd4a1e81c6a120a0135132c",
    "input": {
      "seed": 92,
      "image": "https://replicate.delivery/pbxt/MYqna52R9iABrM3HEJUKJwCe1lYaXPMJcwmviuhwtTZtvChs/0.png",
      "prompt": "",
      "num_outputs": 1,
      "guidance_scale": 7,
      "controlnet_scale": 0.5,
      "ip_adapter_scale": 0.5,
      "num_inference_steps": 10
    }
  }' \
  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/impetusdesign/vhx-gzp@sha256:662bb0370f4f6f6eb94cfc50c3c0b3a06dc15deeddd4a1e81c6a120a0135132c \
  -i 'seed=92' \
  -i 'image="https://replicate.delivery/pbxt/MYqna52R9iABrM3HEJUKJwCe1lYaXPMJcwmviuhwtTZtvChs/0.png"' \
  -i 'prompt=""' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7' \
  -i 'controlnet_scale=0.5' \
  -i 'ip_adapter_scale=0.5' \
  -i 'num_inference_steps=10'

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/impetusdesign/vhx-gzp@sha256:662bb0370f4f6f6eb94cfc50c3c0b3a06dc15deeddd4a1e81c6a120a0135132c
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "seed": 92,
      "image": "https://replicate.delivery/pbxt/MYqna52R9iABrM3HEJUKJwCe1lYaXPMJcwmviuhwtTZtvChs/0.png",
      "prompt": "",
      "num_outputs": 1,
      "guidance_scale": 7,
      "controlnet_scale": 0.5,
      "ip_adapter_scale": 0.5,
      "num_inference_steps": 10
    }
  }' \
  http://localhost:5000/predictions

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