philz1337x/clarity-upscaler:3bb9d341 – Run with an API on Replicate

Version

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

philz1337x /clarity-upscaler:3bb9d341

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

(minimum: 0, maximum: 3)

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: ""

custom_sd_model

string

Shift + Return to add a new line

Link to a custom safetensors checkpoint file you want to use in your upscaling. Will overwrite sd_model checkpoint.

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:3bb9d3412f14261c2f8cfa15a56dd7b33f44af54777c517b860a3f678a5d7f3b",
  {
    input: {
      seed: 1337,
      image: "https://replicate.delivery/pbxt/KiDB5iqtTcxiTI17WASotG1Ei0TNJCztdU6J02pnMYAd8B1X/13_before-4.png",
      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,
      custom_sd_model: "",
      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:3bb9d3412f14261c2f8cfa15a56dd7b33f44af54777c517b860a3f678a5d7f3b",
    input={
        "seed": 1337,
        "image": "https://replicate.delivery/pbxt/KiDB5iqtTcxiTI17WASotG1Ei0TNJCztdU6J02pnMYAd8B1X/13_before-4.png",
        "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,
        "custom_sd_model": "",
        "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:3bb9d3412f14261c2f8cfa15a56dd7b33f44af54777c517b860a3f678a5d7f3b",
    "input": {
      "seed": 1337,
      "image": "https://replicate.delivery/pbxt/KiDB5iqtTcxiTI17WASotG1Ei0TNJCztdU6J02pnMYAd8B1X/13_before-4.png",
      "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,
      "custom_sd_model": "",
      "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

{
  "completed_at": "2024-04-08T15:34:51.385820Z",
  "created_at": "2024-04-08T15:34:38.558000Z",
  "data_removed": false,
  "error": null,
  "id": "snbeynewvsrgp0ceqsc8gjk6sm",
  "input": {
    "seed": 1337,
    "image": "https://replicate.delivery/pbxt/KiDB5iqtTcxiTI17WASotG1Ei0TNJCztdU6J02pnMYAd8B1X/13_before-4.png",
    "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,
    "custom_sd_model": "",
    "negative_prompt": "(worst quality, low quality, normal quality:2) JuggernautNegative-neg",
    "num_inference_steps": 18,
    "downscaling_resolution": 768
  },
  "logs": "Running prediction\n[Tiled Diffusion] upscaling image with 4x-UltraSharp...\n[Tiled Diffusion] ControlNet found, support is enabled.\n2024-04-08 15:34:42,215 - ControlNet - \u001b[0;32mINFO\u001b[0m - unit_separate = False, style_align = False\n2024-04-08 15:34:42,215 - ControlNet - \u001b[0;32mINFO\u001b[0m - Loading model from cache: control_v11f1e_sd15_tile\n2024-04-08 15:34:42,234 - ControlNet - \u001b[0;32mINFO\u001b[0m - Using preprocessor: tile_resample\n2024-04-08 15:34:42,234 - ControlNet - \u001b[0;32mINFO\u001b[0m - preprocessor resolution = 1536\n2024-04-08 15:34:42,310 - ControlNet - \u001b[0;32mINFO\u001b[0m - ControlNet Hooked - Time = 0.0993812084197998\nMultiDiffusion hooked into 'DPM++ 3M SDE Karras' sampler, Tile size: 144x112, Tile count: 4, Batch size: 4, Tile batches: 1 (ext: ContrlNet)\n[Tiled VAE]: the input size is tiny and unnecessary to tile.\nMultiDiffusion Sampling:   0%|          | 0/10 [00:00<?, ?it/s]\n  0%|          | 0/7 [00:00<?, ?it/s]\u001b[A\nTotal progress:   0%|          | 0/7 [00:00<?, ?it/s]\u001b[A\n 14%|█▍        | 1/7 [00:00<00:05,  1.10it/s]\u001b[A\nTotal progress:  29%|██▊       | 2/7 [00:00<00:02,  2.22it/s]\u001b[A\n 29%|██▊       | 2/7 [00:01<00:04,  1.11it/s]\u001b[A\nTotal progress:  43%|████▎     | 3/7 [00:01<00:02,  1.57it/s]\u001b[A\n 43%|████▎     | 3/7 [00:02<00:03,  1.11it/s]\u001b[A\nTotal progress:  57%|█████▋    | 4/7 [00:02<00:02,  1.36it/s]\u001b[A\n 57%|█████▋    | 4/7 [00:03<00:02,  1.11it/s]\u001b[A\nTotal progress:  71%|███████▏  | 5/7 [00:03<00:01,  1.26it/s]\u001b[A\n 71%|███████▏  | 5/7 [00:04<00:01,  1.11it/s]\u001b[A\nTotal progress:  86%|████████▌ | 6/7 [00:04<00:00,  1.21it/s]\u001b[A\n 86%|████████▌ | 6/7 [00:05<00:00,  1.11it/s]\u001b[A\n100%|██████████| 7/7 [00:06<00:00,  1.11it/s]\u001b[A\n100%|██████████| 7/7 [00:06<00:00,  1.11it/s]\nTotal progress: 100%|██████████| 7/7 [00:05<00:00,  1.18it/s]\u001b[A[Tiled VAE]: the input size is tiny and unnecessary to tile.\nTotal progress: 100%|██████████| 7/7 [00:06<00:00,  1.18it/s]\u001b[A\nTotal progress: 100%|██████████| 7/7 [00:06<00:00,  1.11it/s]\nPrediction took 11.742665767669678 seconds",
  "metrics": {
    "predict_time": 12.806754,
    "total_time": 12.82782
  },
  "output": [
    "https://replicate.delivery/pbxt/00paK5p2fY36XykEcte0skfqnSl3akRGTm4TC2l2wiU0E4QlA/1337-89fd2a6e-f5bd-11ee-87b9-bec9d6ff70a9.png"
  ],
  "started_at": "2024-04-08T15:34:38.579066Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/snbeynewvsrgp0ceqsc8gjk6sm",
    "cancel": "https://api.replicate.com/v1/predictions/snbeynewvsrgp0ceqsc8gjk6sm/cancel"
  },
  "version": "3bb9d3412f14261c2f8cfa15a56dd7b33f44af54777c517b860a3f678a5d7f3b"
}

Generated in

12.8 seconds

Tweak it ShareReport

Running prediction
[Tiled Diffusion] upscaling image with 4x-UltraSharp...
[Tiled Diffusion] ControlNet found, support is enabled.
2024-04-08 15:34:42,215 - ControlNet - INFO - unit_separate = False, style_align = False
2024-04-08 15:34:42,215 - ControlNet - INFO - Loading model from cache: control_v11f1e_sd15_tile
2024-04-08 15:34:42,234 - ControlNet - INFO - Using preprocessor: tile_resample
2024-04-08 15:34:42,234 - ControlNet - INFO - preprocessor resolution = 1536
2024-04-08 15:34:42,310 - ControlNet - INFO - ControlNet Hooked - Time = 0.0993812084197998
MultiDiffusion hooked into 'DPM++ 3M SDE Karras' sampler, Tile size: 144x112, Tile count: 4, Batch size: 4, Tile batches: 1 (ext: ContrlNet)
[Tiled VAE]: the input size is tiny and unnecessary to tile.
MultiDiffusion Sampling:   0%|          | 0/10 [00:00<?, ?it/s]
  0%|          | 0/7 [00:00<?, ?it/s]
Total progress:   0%|          | 0/7 [00:00<?, ?it/s]
 14%|█▍        | 1/7 [00:00<00:05,  1.10it/s]
Total progress:  29%|██▊       | 2/7 [00:00<00:02,  2.22it/s]
 29%|██▊       | 2/7 [00:01<00:04,  1.11it/s]
Total progress:  43%|████▎     | 3/7 [00:01<00:02,  1.57it/s]
 43%|████▎     | 3/7 [00:02<00:03,  1.11it/s]
Total progress:  57%|█████▋    | 4/7 [00:02<00:02,  1.36it/s]
 57%|█████▋    | 4/7 [00:03<00:02,  1.11it/s]
Total progress:  71%|███████▏  | 5/7 [00:03<00:01,  1.26it/s]
 71%|███████▏  | 5/7 [00:04<00:01,  1.11it/s]
Total progress:  86%|████████▌ | 6/7 [00:04<00:00,  1.21it/s]
 86%|████████▌ | 6/7 [00:05<00:00,  1.11it/s]
100%|██████████| 7/7 [00:06<00:00,  1.11it/s]
100%|██████████| 7/7 [00:06<00:00,  1.11it/s]
Total progress: 100%|██████████| 7/7 [00:05<00:00,  1.18it/s][Tiled VAE]: the input size is tiny and unnecessary to tile.
Total progress: 100%|██████████| 7/7 [00:06<00:00,  1.18it/s]
Total progress: 100%|██████████| 7/7 [00:06<00:00,  1.11it/s]
Prediction took 11.742665767669678 seconds