anotherjesse/openjourney-v4 – Run with an API on Replicate

anotherjesse / openjourney-v4

Cold

Public
451 runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=anotherjesse/openjourney-v4

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 anotherjesse/openjourney-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "anotherjesse/openjourney-v4:7abcebfcc4dd703ec7d64c9e335cb20924318d3f5744c2aadcf177ea60144398",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "photo of cjw person",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      low_threshold: 100,
      guidance_scale: 7.5,
      high_threshold: 200,
      prompt_strength: 0.8,
      num_inference_steps: 50,
      disable_safety_check: false,
      return_processed_control: false
    }
  }
);
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 anotherjesse/openjourney-v4 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/openjourney-v4:7abcebfcc4dd703ec7d64c9e335cb20924318d3f5744c2aadcf177ea60144398",
    input={
        "width": 512,
        "height": 512,
        "prompt": "photo of cjw person",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "low_threshold": 100,
        "guidance_scale": 7.5,
        "high_threshold": 200,
        "prompt_strength": 0.8,
        "num_inference_steps": 50,
        "disable_safety_check": False,
        "return_processed_control": False
    }
)

# The anotherjesse/openjourney-v4 model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/anotherjesse/openjourney-v4/api#output-schema
    print(item)

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 anotherjesse/openjourney-v4 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": "7abcebfcc4dd703ec7d64c9e335cb20924318d3f5744c2aadcf177ea60144398",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "photo of cjw person",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "low_threshold": 100,
      "guidance_scale": 7.5,
      "high_threshold": 200,
      "prompt_strength": 0.8,
      "num_inference_steps": 50,
      "disable_safety_check": false,
      "return_processed_control": false
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model costs approximately $0.0070 to run on Replicate, or 142 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 A100 (80GB) GPU hardware. Predictions typically complete within 6 seconds.

Readme

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Model description

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Intended use

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Ethical considerations

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Caveats and recommendations

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