devinmancuso/tester-push | Run with an API on Replicate

Input

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

npx create-replicate --model=devinmancuso/tester-push

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 devinmancuso/tester-push using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "devinmancuso/tester-push:76f20f7fce4e7bc35aed440523dd713dce3606a6162c4819c2ad7ea6c70f3fdc",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "An astronaut riding a rainbow unicorn",
      lora_id: "galverse/mama-1.5",
      scheduler: "DPMSolverMultistep",
      lora_scale: 1,
      num_outputs: 1,
      guidance_scale: 7.5,
      apply_watermark: true,
      negative_prompt: "",
      prompt_strength: 0.8,
      num_inference_steps: 50
    }
  }
);

// 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 devinmancuso/tester-push using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "devinmancuso/tester-push:76f20f7fce4e7bc35aed440523dd713dce3606a6162c4819c2ad7ea6c70f3fdc",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "An astronaut riding a rainbow unicorn",
        "lora_id": "galverse/mama-1.5",
        "scheduler": "DPMSolverMultistep",
        "lora_scale": 1,
        "num_outputs": 1,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "negative_prompt": "",
        "prompt_strength": 0.8,
        "num_inference_steps": 50
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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 devinmancuso/tester-push 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": "devinmancuso/tester-push:76f20f7fce4e7bc35aed440523dd713dce3606a6162c4819c2ad7ea6c70f3fdc",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "An astronaut riding a rainbow unicorn",
      "lora_id": "galverse/mama-1.5",
      "scheduler": "DPMSolverMultistep",
      "lora_scale": 1,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "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.

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/devinmancuso/tester-push@sha256:76f20f7fce4e7bc35aed440523dd713dce3606a6162c4819c2ad7ea6c70f3fdc \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="An astronaut riding a rainbow unicorn"' \
  -i 'lora_id="galverse/mama-1.5"' \
  -i 'scheduler="DPMSolverMultistep"' \
  -i 'lora_scale=1' \
  -i 'num_outputs=1' \
  -i 'guidance_scale=7.5' \
  -i 'apply_watermark=true' \
  -i 'negative_prompt=""' \
  -i 'prompt_strength=0.8' \
  -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/devinmancuso/tester-push@sha256:76f20f7fce4e7bc35aed440523dd713dce3606a6162c4819c2ad7ea6c70f3fdc
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "An astronaut riding a rainbow unicorn",
      "lora_id": "galverse/mama-1.5",
      "scheduler": "DPMSolverMultistep",
      "lora_scale": 1,
      "num_outputs": 1,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "negative_prompt": "",
      "prompt_strength": 0.8,
      "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.

Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This model doesn't have a readme.