abdulali025/andro-upscaler:a237f480 – Run with an API on Replicate

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abdulali025 /andro-upscaler:a237f480

Playground API

Input

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

npx create-replicate --model=abdulali025/andro-upscaler

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 abdulali025/andro-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "abdulali025/andro-upscaler:a237f48001f2030d88f943d82e92266a365e9384b4c05ec81ba430977f742f56",
  {
    input: {
      prompt: "",
      guidance_scale: 5,
      upscale_factor: 4,
      apply_cc_preset: false,
      num_inference_steps: 28,
      controlnet_conditioning_scale: 0.6
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 abdulali025/andro-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "abdulali025/andro-upscaler:a237f48001f2030d88f943d82e92266a365e9384b4c05ec81ba430977f742f56",
    input={
        "prompt": "",
        "guidance_scale": 5,
        "upscale_factor": 4,
        "apply_cc_preset": False,
        "num_inference_steps": 28,
        "controlnet_conditioning_scale": 0.6
    }
)
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 abdulali025/andro-upscaler 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": "a237f48001f2030d88f943d82e92266a365e9384b4c05ec81ba430977f742f56",
    "input": {
      "prompt": "",
      "guidance_scale": 5,
      "upscale_factor": 4,
      "apply_cc_preset": false,
      "num_inference_steps": 28,
      "controlnet_conditioning_scale": 0.6
    }
  }' \
  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/abdulali025/andro-upscaler@sha256:a237f48001f2030d88f943d82e92266a365e9384b4c05ec81ba430977f742f56 \
  -i 'prompt=""' \
  -i 'guidance_scale=5' \
  -i 'upscale_factor=4' \
  -i 'apply_cc_preset=false' \
  -i 'num_inference_steps=28' \
  -i 'controlnet_conditioning_scale=0.6'

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/abdulali025/andro-upscaler@sha256:a237f48001f2030d88f943d82e92266a365e9384b4c05ec81ba430977f742f56
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "prompt": "",
      "guidance_scale": 5,
      "upscale_factor": 4,
      "apply_cc_preset": false,
      "num_inference_steps": 28,
      "controlnet_conditioning_scale": 0.6
    }
  }' \
  http://localhost:5000/predictions

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

Output

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