lucataco / rivers-stable-diffusion-upscaler

{
  "eta": 1,
  "image": "https://replicate.delivery/pbxt/JC0Wu2il27rHPIWbtVxx0xaZu9IAD11PZMarH9Drc6pFC4rX/demo.jpeg",
  "steps": 50,
  "prompt": "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas",
  "sampler": "k_dpm_adaptive",
  "tol_scale": 0.25,
  "guidance_scale": 1,
  "noise_aug_type": "gaussian",
  "noise_aug_level": 0
}

Install Replicate’s Node.js client library:

npm install replicate

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 lucataco/rivers-stable-diffusion-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/rivers-stable-diffusion-upscaler:d70507adad3ca5f6723bbd289706b6ef9460bc0cd24d3f91db729a7695800cc4",
  {
    input: {
      eta: 1,
      image: "https://replicate.delivery/pbxt/JC0Wu2il27rHPIWbtVxx0xaZu9IAD11PZMarH9Drc6pFC4rX/demo.jpeg",
      steps: 50,
      prompt: "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas",
      sampler: "k_dpm_adaptive",
      tol_scale: 0.25,
      guidance_scale: 1,
      noise_aug_type: "gaussian",
      noise_aug_level: 0
    }
  }
);

// 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

Import the client:

import replicate

Run lucataco/rivers-stable-diffusion-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lucataco/rivers-stable-diffusion-upscaler:d70507adad3ca5f6723bbd289706b6ef9460bc0cd24d3f91db729a7695800cc4",
    input={
        "eta": 1,
        "image": "https://replicate.delivery/pbxt/JC0Wu2il27rHPIWbtVxx0xaZu9IAD11PZMarH9Drc6pFC4rX/demo.jpeg",
        "steps": 50,
        "prompt": "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas",
        "sampler": "k_dpm_adaptive",
        "tol_scale": 0.25,
        "guidance_scale": 1,
        "noise_aug_type": "gaussian",
        "noise_aug_level": 0
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Run lucataco/rivers-stable-diffusion-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": "lucataco/rivers-stable-diffusion-upscaler:d70507adad3ca5f6723bbd289706b6ef9460bc0cd24d3f91db729a7695800cc4",
    "input": {
      "eta": 1,
      "image": "https://replicate.delivery/pbxt/JC0Wu2il27rHPIWbtVxx0xaZu9IAD11PZMarH9Drc6pFC4rX/demo.jpeg",
      "steps": 50,
      "prompt": "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas",
      "sampler": "k_dpm_adaptive",
      "tol_scale": 0.25,
      "guidance_scale": 1,
      "noise_aug_type": "gaussian",
      "noise_aug_level": 0
    }
  }' \
  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/lucataco/rivers-stable-diffusion-upscaler@sha256:d70507adad3ca5f6723bbd289706b6ef9460bc0cd24d3f91db729a7695800cc4 \
  -i 'eta=1' \
  -i 'image="https://replicate.delivery/pbxt/JC0Wu2il27rHPIWbtVxx0xaZu9IAD11PZMarH9Drc6pFC4rX/demo.jpeg"' \
  -i 'steps=50' \
  -i 'prompt="the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas"' \
  -i 'sampler="k_dpm_adaptive"' \
  -i 'tol_scale=0.25' \
  -i 'guidance_scale=1' \
  -i 'noise_aug_type="gaussian"' \
  -i 'noise_aug_level=0'

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/lucataco/rivers-stable-diffusion-upscaler@sha256:d70507adad3ca5f6723bbd289706b6ef9460bc0cd24d3f91db729a7695800cc4
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "eta": 1,
      "image": "https://replicate.delivery/pbxt/JC0Wu2il27rHPIWbtVxx0xaZu9IAD11PZMarH9Drc6pFC4rX/demo.jpeg",
      "steps": 50,
      "prompt": "the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas",
      "sampler": "k_dpm_adaptive",
      "tol_scale": 0.25,
      "guidance_scale": 1,
      "noise_aug_type": "gaussian",
      "noise_aug_level": 0
    }
  }' \
  http://localhost:5000/predictions

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

Output

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