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

anotherjesse / openjourney-v4-lora

Cold

Public
25 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-lora

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-lora 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-lora:3cc5ca7228908db26c66a551246c01c785a4506ece8908603451f1bbcbd28c42",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "a photo of <1> riding a horse on mars",
      lora_urls: "",
      scheduler: "DPMSolverMultistep",
      lora_scales: "0.5",
      num_outputs: 1,
      guidance_scale: 7.5,
      negative_prompt: "",
      prompt_strength: 0.8,
      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 anotherjesse/openjourney-v4-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/openjourney-v4-lora:3cc5ca7228908db26c66a551246c01c785a4506ece8908603451f1bbcbd28c42",
    input={
        "width": 512,
        "height": 512,
        "prompt": "a photo of <1> riding a horse on mars",
        "lora_urls": "",
        "scheduler": "DPMSolverMultistep",
        "lora_scales": "0.5",
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "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 anotherjesse/openjourney-v4-lora 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": "3cc5ca7228908db26c66a551246c01c785a4506ece8908603451f1bbcbd28c42",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "a photo of <1> riding a horse on mars",
      "lora_urls": "",
      "scheduler": "DPMSolverMultistep",
      "lora_scales": "0.5",
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "num_inference_steps": 50
    }
  }' \
  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 runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

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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