fofr/controlnet-preprocessors – Run with an API on Replicate

Input

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

npx create-replicate --model=fofr/controlnet-preprocessors

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 fofr/controlnet-preprocessors using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/controlnet-preprocessors:f6584ef76cf07a2014ffe1e9bdb1a5cfa714f031883ab43f8d4b05506625988e",
  {
    input: {
      hed: true,
      sam: true,
      mlsd: true,
      pidi: true,
      canny: true,
      image: "https://replicate.delivery/pbxt/JrCCMJ7WAdjQSIkZUQrUkOmyDeFhkRkgQgH1S3QvryX8Iypg/IMG_7845.jpeg",
      leres: true,
      midas: true,
      content: true,
      lineart: true,
      open_pose: true,
      normal_bae: true,
      face_detector: true,
      lineart_anime: true
    }
  }
);

// 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 fofr/controlnet-preprocessors using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/controlnet-preprocessors:f6584ef76cf07a2014ffe1e9bdb1a5cfa714f031883ab43f8d4b05506625988e",
    input={
        "hed": True,
        "sam": True,
        "mlsd": True,
        "pidi": True,
        "canny": True,
        "image": "https://replicate.delivery/pbxt/JrCCMJ7WAdjQSIkZUQrUkOmyDeFhkRkgQgH1S3QvryX8Iypg/IMG_7845.jpeg",
        "leres": True,
        "midas": True,
        "content": True,
        "lineart": True,
        "open_pose": True,
        "normal_bae": True,
        "face_detector": True,
        "lineart_anime": True
    }
)
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 fofr/controlnet-preprocessors 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": "fofr/controlnet-preprocessors:f6584ef76cf07a2014ffe1e9bdb1a5cfa714f031883ab43f8d4b05506625988e",
    "input": {
      "hed": true,
      "sam": true,
      "mlsd": true,
      "pidi": true,
      "canny": true,
      "image": "https://replicate.delivery/pbxt/JrCCMJ7WAdjQSIkZUQrUkOmyDeFhkRkgQgH1S3QvryX8Iypg/IMG_7845.jpeg",
      "leres": true,
      "midas": true,
      "content": true,
      "lineart": true,
      "open_pose": true,
      "normal_bae": true,
      "face_detector": true,
      "lineart_anime": true
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

{
  "completed_at": "2023-11-11T06:58:25.102627Z",
  "created_at": "2023-11-11T06:58:08.625273Z",
  "data_removed": false,
  "error": null,
  "id": "54kd4flb5sdiepnoqgs7raf2wa",
  "input": {
    "hed": true,
    "sam": true,
    "mlsd": true,
    "pidi": true,
    "canny": true,
    "image": "https://replicate.delivery/pbxt/JrCCMJ7WAdjQSIkZUQrUkOmyDeFhkRkgQgH1S3QvryX8Iypg/IMG_7845.jpeg",
    "leres": true,
    "midas": true,
    "content": true,
    "lineart": true,
    "open_pose": true,
    "normal_bae": true,
    "face_detector": true,
    "lineart_anime": true
  },
  "logs": "Processing image with canny\nProcessing image with content\nProcessing image with face_detector\nE0000 00:00:1699685889.055007     259 gl_context.cc:408] INTERNAL: ; RET_CHECK failure (mediapipe/gpu/gl_context_egl.cc:303) successeglMakeCurrent() returned error 0x3008;  (entering GL context)\nE0000 00:00:1699685889.055045     259 gl_context.cc:408] INTERNAL: ; RET_CHECK failure (mediapipe/gpu/gl_context_egl.cc:303) successeglMakeCurrent() returned error 0x3008;  (entering GL context)\nE0000 00:00:1699685889.055053     259 gl_context.cc:408] INTERNAL: ; RET_CHECK failure (mediapipe/gpu/gl_context_egl.cc:303) successeglMakeCurrent() returned error 0x3008;  (entering GL context)\nProcessing image with hed\nProcessing image with midas\nProcessing image with mlsd\nProcessing image with open_pose\nProcessing image with pidi\nProcessing image with normal_bae\nProcessing image with lineart\nProcessing image with lineart_anime\nProcessing image with sam\nProcessing image with leres",
  "metrics": {
    "predict_time": 16.501693,
    "total_time": 16.477354
  },
  "output": [
    "https://replicate.delivery/pbxt/uw93lneIWuUTMCo5rrKKTTDUAu6XbNImxkZ4hfuwZVMLgN3RA/canny.png",
    "https://replicate.delivery/pbxt/zs4XRbOlkxZeZaoZ52xnXe48s18KyBTVf4bfZRJhtfokBs5OC/content.png",
    "https://replicate.delivery/pbxt/QhUeh3vdAuU2H6ykifMReU14F6zm4IoeqRfOrCOfDTydDYzdE/face_detector.png",
    "https://replicate.delivery/pbxt/hwMZ86nAfbxWXKxAemdIOz6umijHKz7HTUXcVlqnuuuNgN3RA/hed.png",
    "https://replicate.delivery/pbxt/JyyRt4DuftzAbyghuqaRjSjhsaNqQF5fbdizkqAdaNQNgN3RA/midas.png",
    "https://replicate.delivery/pbxt/IpkTd18fexldvkpJWhYNJQZ5EnukEfIeukLTi1JfpE7wBs5OC/mlsd.png",
    "https://replicate.delivery/pbxt/T5HOBsOKHHqMNVTrLeOKci4jGfkyfeSsstGYVwgSglc5A2cHB/open_pose.png",
    "https://replicate.delivery/pbxt/hMP1a8Y41vIoHBDEtN02eE6gCT41LXXebtcJh108fwbcAbujA/pidi.png",
    "https://replicate.delivery/pbxt/2J93cnvbxxpdMhQbFLWUvsEZcSStPahCj3Ni73GkQhyDYzdE/normal_bae.png",
    "https://replicate.delivery/pbxt/lf9Y6I3bBPVRN6a6ffVKlvjqrcxBVoQZnckerKNfnVM9Bs5OC/lineart.png",
    "https://replicate.delivery/pbxt/dCeKSeaACpgEuUdemacT4S7YfEBxf44vBE1xsueeg61zHwm7IA/lineart_anime.png",
    "https://replicate.delivery/pbxt/fdBSo7qeUFodVU4IP6cnyPOEULcKogpk2DCCTkUZcZUQgN3RA/sam.png",
    "https://replicate.delivery/pbxt/Y5S0q7BS7zZ8OxOLTNQ67QBhFsPjiaOzuPRRPcAJoXIEYzdE/leres.png"
  ],
  "started_at": "2023-11-11T06:58:08.600934Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/54kd4flb5sdiepnoqgs7raf2wa",
    "cancel": "https://api.replicate.com/v1/predictions/54kd4flb5sdiepnoqgs7raf2wa/cancel"
  },
  "version": "f6584ef76cf07a2014ffe1e9bdb1a5cfa714f031883ab43f8d4b05506625988e"
}

Generated in

16.5 seconds

Tweak it Report

Run time and cost

This model costs approximately $0.017 to run on Replicate, or 58 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 18 seconds. The predict time for this model varies significantly based on the inputs.