abdulali025/andro-upscaler:0e16e36c – Run with an API on Replicate

Version

You're looking at a specific version of this model. Jump to the model overview.

abdulali025 /andro-upscaler:0e16e36c

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:0e16e36c18fc831bfdc4d18234e493151323a50749f4b4a1beb21765949ce5b4",
  {
    input: {
      prompt: "",
      input_image: "https://replicate.delivery/pbxt/Mf8s35ireThU5CzqbjQJgLxER0hQ8l5ExCtjoiqTr5nxgk0B/cat-2.jpeg",
      guidance_scale: 5,
      upscale_factor: 4,
      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:0e16e36c18fc831bfdc4d18234e493151323a50749f4b4a1beb21765949ce5b4",
    input={
        "prompt": "",
        "input_image": "https://replicate.delivery/pbxt/Mf8s35ireThU5CzqbjQJgLxER0hQ8l5ExCtjoiqTr5nxgk0B/cat-2.jpeg",
        "guidance_scale": 5,
        "upscale_factor": 4,
        "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": "abdulali025/andro-upscaler:0e16e36c18fc831bfdc4d18234e493151323a50749f4b4a1beb21765949ce5b4",
    "input": {
      "prompt": "",
      "input_image": "https://replicate.delivery/pbxt/Mf8s35ireThU5CzqbjQJgLxER0hQ8l5ExCtjoiqTr5nxgk0B/cat-2.jpeg",
      "guidance_scale": 5,
      "upscale_factor": 4,
      "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.

Output

{
  "completed_at": "2025-03-15T08:10:29.539012Z",
  "created_at": "2025-03-15T08:10:05.583000Z",
  "data_removed": false,
  "error": null,
  "id": "9yem0rb9hxrj40cnk3dbg78jmg",
  "input": {
    "prompt": "",
    "input_image": "https://replicate.delivery/pbxt/Mf8s35ireThU5CzqbjQJgLxER0hQ8l5ExCtjoiqTr5nxgk0B/cat-2.jpeg",
    "guidance_scale": 5,
    "upscale_factor": 4,
    "num_inference_steps": 28,
    "controlnet_conditioning_scale": 0.6
  },
  "logs": "Seed: 844857\nProcess input image dimensions\nGenerate upscaled image\n  0%|          | 0/28 [00:00<?, ?it/s]\n  4%|▎         | 1/28 [00:00<00:22,  1.21it/s]\n  7%|▋         | 2/28 [00:01<00:17,  1.48it/s]\n 11%|█         | 3/28 [00:02<00:18,  1.34it/s]\n 14%|█▍        | 4/28 [00:03<00:18,  1.28it/s]\n 18%|█▊        | 5/28 [00:03<00:18,  1.25it/s]\n 21%|██▏       | 6/28 [00:04<00:17,  1.24it/s]\n 25%|██▌       | 7/28 [00:05<00:17,  1.22it/s]\n 29%|██▊       | 8/28 [00:06<00:16,  1.22it/s]\n 32%|███▏      | 9/28 [00:07<00:15,  1.21it/s]\n 36%|███▌      | 10/28 [00:08<00:14,  1.21it/s]\n 39%|███▉      | 11/28 [00:08<00:14,  1.21it/s]\n 43%|████▎     | 12/28 [00:09<00:13,  1.21it/s]\n 46%|████▋     | 13/28 [00:10<00:12,  1.20it/s]\n 50%|█████     | 14/28 [00:11<00:11,  1.20it/s]\n 54%|█████▎    | 15/28 [00:12<00:10,  1.20it/s]\n 57%|█████▋    | 16/28 [00:13<00:09,  1.20it/s]\n 61%|██████    | 17/28 [00:13<00:09,  1.20it/s]\n 64%|██████▍   | 18/28 [00:14<00:08,  1.20it/s]\n 68%|██████▊   | 19/28 [00:15<00:07,  1.20it/s]\n 71%|███████▏  | 20/28 [00:16<00:06,  1.20it/s]\n 75%|███████▌  | 21/28 [00:17<00:05,  1.20it/s]\n 79%|███████▊  | 22/28 [00:18<00:05,  1.20it/s]\n 82%|████████▏ | 23/28 [00:18<00:04,  1.20it/s]\n 86%|████████▌ | 24/28 [00:19<00:03,  1.20it/s]\n 89%|████████▉ | 25/28 [00:20<00:02,  1.20it/s]\n 93%|█████████▎| 26/28 [00:21<00:01,  1.20it/s]\n 96%|█████████▋| 27/28 [00:22<00:00,  1.20it/s]\n100%|██████████| 28/28 [00:23<00:00,  1.20it/s]\n100%|██████████| 28/28 [00:23<00:00,  1.22it/s]",
  "metrics": {
    "predict_time": 23.945262026,
    "total_time": 23.956012
  },
  "output": "https://replicate.delivery/yhqm/TIx9pXuWkTqqLBhVvHJttB5LdsffJtRCwtXPmR4VESv1fcxoA/output.jpg",
  "started_at": "2025-03-15T08:10:05.593750Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://stream.replicate.com/v1/files/yswh-yh2gqja7lmuipojv6fqua6f33ocfx2kndshyzwivfnhkxehqkqoq",
    "get": "https://api.replicate.com/v1/predictions/9yem0rb9hxrj40cnk3dbg78jmg",
    "cancel": "https://api.replicate.com/v1/predictions/9yem0rb9hxrj40cnk3dbg78jmg/cancel"
  },
  "version": "0e16e36c18fc831bfdc4d18234e493151323a50749f4b4a1beb21765949ce5b4"
}

Generated in

23.9 seconds

Tweak it ShareReport