philz1337x/clarity-upscaler:99c3e8b7 – Run with an API on Replicate

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philz1337x /clarity-upscaler:99c3e8b7

Playground API

Input

image

*file

input image

prompt

string

Shift + Return to add a new line

masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>

Prompt

Default: "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>"

negative_prompt

string

Shift + Return to add a new line

(worst quality, low quality, normal quality:2) JuggernautNegative-neg(worst quality, low quality, normal quality:2) JuggernautNegative-neg

Negative Prompt

Default: "(worst quality, low quality, normal quality:2) JuggernautNegative-neg"

scale_factor

number

Scale factor

Default: 2

dynamic

number

(minimum: 1, maximum: 50)

HDR, try from 3 - 9

Default: 6

creativity

number

(minimum: 0, maximum: 1)

Creativity, try from 0.3 - 0.9

Default: 0.35

resemblance

number

Resemblance, try from 0.3 - 1.6

Default: 0.6

tiling_width

integer

Fractality, set lower tile width for a high Fractality

Default: 112

tiling_height

integer

Fractality, set lower tile height for a high Fractality

Default: 144

sd_model

string

Stable Diffusion model checkpoint

Default: "juggernaut_reborn.safetensors [338b85bc4f]"

scheduler

string

scheduler

Default: "DPM++ 3M SDE Karras"

num_inference_steps

integer

(minimum: 1, maximum: 100)

Number of denoising steps

Default: 18

seed

integer

Random seed. Leave blank to randomize the seed

Default: 1337

downscaling

boolean

Downscale the image before upscaling. Can improve quality and speed for images with high resolution but lower quality

Default: false

downscaling_resolution

integer

Downscaling resolution

Default: 768

lora_links

string

Shift + Return to add a new line

Link to a lora file you want to use in your upscaling. Multiple links possible, seperated by comma

Default: ""

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

npx create-replicate --model=philz1337x/clarity-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 philz1337x/clarity-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "philz1337x/clarity-upscaler:99c3e8b7d14d698e6c5485143c3c3fdf92d3c5e80e9c8f1b76b4ad41a0325a14",
  {
    input: {
      seed: 1337,
      prompt: "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>",
      dynamic: 6,
      sd_model: "juggernaut_reborn.safetensors [338b85bc4f]",
      scheduler: "DPM++ 3M SDE Karras",
      creativity: 0.35,
      lora_links: "",
      downscaling: false,
      resemblance: 0.6,
      scale_factor: 2,
      tiling_width: 112,
      tiling_height: 144,
      negative_prompt: "(worst quality, low quality, normal quality:2) JuggernautNegative-neg",
      num_inference_steps: 18,
      downscaling_resolution: 768
    }
  }
);

// 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 philz1337x/clarity-upscaler using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "philz1337x/clarity-upscaler:99c3e8b7d14d698e6c5485143c3c3fdf92d3c5e80e9c8f1b76b4ad41a0325a14",
    input={
        "seed": 1337,
        "prompt": "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>",
        "dynamic": 6,
        "sd_model": "juggernaut_reborn.safetensors [338b85bc4f]",
        "scheduler": "DPM++ 3M SDE Karras",
        "creativity": 0.35,
        "lora_links": "",
        "downscaling": False,
        "resemblance": 0.6,
        "scale_factor": 2,
        "tiling_width": 112,
        "tiling_height": 144,
        "negative_prompt": "(worst quality, low quality, normal quality:2) JuggernautNegative-neg",
        "num_inference_steps": 18,
        "downscaling_resolution": 768
    }
)
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 philz1337x/clarity-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": "philz1337x/clarity-upscaler:99c3e8b7d14d698e6c5485143c3c3fdf92d3c5e80e9c8f1b76b4ad41a0325a14",
    "input": {
      "seed": 1337,
      "prompt": "masterpiece, best quality, highres, <lora:more_details:0.5> <lora:SDXLrender_v2.0:1>",
      "dynamic": 6,
      "sd_model": "juggernaut_reborn.safetensors [338b85bc4f]",
      "scheduler": "DPM++ 3M SDE Karras",
      "creativity": 0.35,
      "lora_links": "",
      "downscaling": false,
      "resemblance": 0.6,
      "scale_factor": 2,
      "tiling_width": 112,
      "tiling_height": 144,
      "negative_prompt": "(worst quality, low quality, normal quality:2) JuggernautNegative-neg",
      "num_inference_steps": 18,
      "downscaling_resolution": 768
    }
  }' \
  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.