jagilley / controlnet-seg

image
scale: 9
prompt: A modernist house in a nice landscape
a_prompt: best quality, extremely detailed
n_prompt: longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality
ddim_steps: 20
num_samples: 1
image_resolution: 512
detect_resolution: 512

{
  "image": "https://replicate.delivery/pbxt/IJYtXSDZ6sxDVWj3tcrf4JvNHT4f9LH5BAQhVSjJWf9BU3v4/house.png",
  "scale": 9,
  "prompt": "A modernist house in a nice landscape",
  "a_prompt": "best quality, extremely detailed",
  "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
  "ddim_steps": 20,
  "num_samples": "1",
  "image_resolution": "512",
  "detect_resolution": 512
}

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

const output = await replicate.run(
  "jagilley/controlnet-seg:f967b165f4cd2e151d11e7450a8214e5d22ad2007f042f2f891ca3981dbfba0d",
  {
    input: {
      image: "https://replicate.delivery/pbxt/IJYtXSDZ6sxDVWj3tcrf4JvNHT4f9LH5BAQhVSjJWf9BU3v4/house.png",
      scale: 9,
      prompt: "A modernist house in a nice landscape",
      a_prompt: "best quality, extremely detailed",
      n_prompt: "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      ddim_steps: 20,
      num_samples: "1",
      image_resolution: "512",
      detect_resolution: 512
    }
  }
);

// 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

Import the client:

import replicate

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

output = replicate.run(
    "jagilley/controlnet-seg:f967b165f4cd2e151d11e7450a8214e5d22ad2007f042f2f891ca3981dbfba0d",
    input={
        "image": "https://replicate.delivery/pbxt/IJYtXSDZ6sxDVWj3tcrf4JvNHT4f9LH5BAQhVSjJWf9BU3v4/house.png",
        "scale": 9,
        "prompt": "A modernist house in a nice landscape",
        "a_prompt": "best quality, extremely detailed",
        "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
        "ddim_steps": 20,
        "num_samples": "1",
        "image_resolution": "512",
        "detect_resolution": 512
    }
)
print(output)

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

Run jagilley/controlnet-seg 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": "jagilley/controlnet-seg:f967b165f4cd2e151d11e7450a8214e5d22ad2007f042f2f891ca3981dbfba0d",
    "input": {
      "image": "https://replicate.delivery/pbxt/IJYtXSDZ6sxDVWj3tcrf4JvNHT4f9LH5BAQhVSjJWf9BU3v4/house.png",
      "scale": 9,
      "prompt": "A modernist house in a nice landscape",
      "a_prompt": "best quality, extremely detailed",
      "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
      "ddim_steps": 20,
      "num_samples": "1",
      "image_resolution": "512",
      "detect_resolution": 512
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2023-02-14T20:53:33.321081Z",
  "created_at": "2023-02-14T20:46:52.659530Z",
  "data_removed": false,
  "error": null,
  "id": "ywxddjn4xbbp5i73roshr6bbyy",
  "input": {
    "image": "https://replicate.delivery/pbxt/IJYtXSDZ6sxDVWj3tcrf4JvNHT4f9LH5BAQhVSjJWf9BU3v4/house.png",
    "scale": 9,
    "prompt": "A modernist house in a nice landscape",
    "a_prompt": "best quality, extremely detailed",
    "n_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality",
    "ddim_steps": 20,
    "num_samples": "1",
    "image_resolution": "512",
    "detect_resolution": 512
  },
  "logs": "/src/annotator/uniformer/mmseg/models/segmentors/base.py:271: UserWarning: show==False and out_file is not specified, only result image will be returned\nwarnings.warn('show==False and out_file is not specified, only '\nGlobal seed set to 88655\nData shape for DDIM sampling is (1, 4, 64, 64), eta 0.0\nRunning DDIM Sampling with 20 timesteps\nDDIM Sampler:   0%|          | 0/20 [00:00<?, ?it/s]\nDDIM Sampler:   5%|▌         | 1/20 [00:00<00:17,  1.08it/s]\nDDIM Sampler:  10%|█         | 2/20 [00:01<00:14,  1.24it/s]\nDDIM Sampler:  15%|█▌        | 3/20 [00:02<00:13,  1.30it/s]\nDDIM Sampler:  20%|██        | 4/20 [00:03<00:12,  1.33it/s]\nDDIM Sampler:  25%|██▌       | 5/20 [00:03<00:11,  1.35it/s]\nDDIM Sampler:  30%|███       | 6/20 [00:04<00:10,  1.36it/s]\nDDIM Sampler:  35%|███▌      | 7/20 [00:05<00:09,  1.36it/s]\nDDIM Sampler:  40%|████      | 8/20 [00:06<00:08,  1.37it/s]\nDDIM Sampler:  45%|████▌     | 9/20 [00:06<00:08,  1.37it/s]\nDDIM Sampler:  50%|█████     | 10/20 [00:07<00:07,  1.37it/s]\nDDIM Sampler:  55%|█████▌    | 11/20 [00:08<00:06,  1.37it/s]\nDDIM Sampler:  60%|██████    | 12/20 [00:08<00:05,  1.37it/s]\nDDIM Sampler:  65%|██████▌   | 13/20 [00:09<00:05,  1.37it/s]\nDDIM Sampler:  70%|███████   | 14/20 [00:10<00:04,  1.37it/s]\nDDIM Sampler:  75%|███████▌  | 15/20 [00:11<00:03,  1.37it/s]\nDDIM Sampler:  80%|████████  | 16/20 [00:11<00:02,  1.37it/s]\nDDIM Sampler:  85%|████████▌ | 17/20 [00:12<00:02,  1.36it/s]\nDDIM Sampler:  90%|█████████ | 18/20 [00:13<00:01,  1.36it/s]\nDDIM Sampler:  95%|█████████▌| 19/20 [00:14<00:00,  1.36it/s]\nDDIM Sampler: 100%|██████████| 20/20 [00:14<00:00,  1.36it/s]\nDDIM Sampler: 100%|██████████| 20/20 [00:14<00:00,  1.35it/s]",
  "metrics": {
    "predict_time": 21.184482,
    "total_time": 400.661551
  },
  "output": [
    "https://replicate.delivery/pbxt/mBNpEeiNkrSNBypupIfKvNLrQcUaBYfh6of4wahaHj9ysp5BB/output_0.png",
    "https://replicate.delivery/pbxt/YKVetNkuroyWJC4mxzrgvQbJ2vS5eFOS1B8xkJMkAiXMbaegA/output_1.png"
  ],
  "started_at": "2023-02-14T20:53:12.136599Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/ywxddjn4xbbp5i73roshr6bbyy",
    "cancel": "https://api.replicate.com/v1/predictions/ywxddjn4xbbp5i73roshr6bbyy/cancel"
  },
  "version": "f967b165f4cd2e151d11e7450a8214e5d22ad2007f042f2f891ca3981dbfba0d"
}

Generated in

21.2 seconds

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