jagilley/controlnet-canny:02a11802 – Run with an API on Replicate

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jagilley /controlnet-canny:02a11802

Playground API

Input

image

*file

Input image

prompt

*string

Shift + Return to add a new line

Prompt for the model

num_samples

string

Number of samples (higher values may OOM)

Default: "1"

image_resolution

string

Image resolution to be generated

Default: "512"

low_threshold

integer

(minimum: 1, maximum: 255)

Canny line detection low threshold (only applicable when model type is 'canny')

Default: 100

high_threshold

integer

(minimum: 1, maximum: 255)

Canny line detection high threshold (only applicable when model type is 'canny')

Default: 200

ddim_steps

integer

Steps

Default: 20

scale

number

(minimum: 0.1, maximum: 30)

Scale for classifier-free guidance

Default: 9

seed

integer

Seed

eta

number

Controls the amount of noise that is added to the input data during the denoising diffusion process. Higher value -> more noise

Default: 0

a_prompt

string

Shift + Return to add a new line

Added Prompt to be appended

Default: "best quality, extremely detailed"

n_prompt

string

Shift + Return to add a new line

longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low qualitylongbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality

Negative Prompt

Default: "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"

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

npx create-replicate --model=jagilley/controlnet-canny

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

const output = await replicate.run(
  "jagilley/controlnet-canny:02a11802691b4733143bb9f343758671e4c98ae36f06de0a01a1ee79d68f8487",
  {
    input: {
      eta: 0,
      image: "https://replicate.delivery/pbxt/IMjSG2JLxIRfTr5dPhKGPeGrMFnn9ilWQ4tD0dwxQOHtYlUd/bird.png",
      scale: 9,
      prompt: "bird",
      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",
      low_threshold: 100,
      high_threshold: 200,
      image_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

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

output = replicate.run(
    "jagilley/controlnet-canny:02a11802691b4733143bb9f343758671e4c98ae36f06de0a01a1ee79d68f8487",
    input={
        "eta": 0,
        "image": "https://replicate.delivery/pbxt/IMjSG2JLxIRfTr5dPhKGPeGrMFnn9ilWQ4tD0dwxQOHtYlUd/bird.png",
        "scale": 9,
        "prompt": "bird",
        "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",
        "low_threshold": 100,
        "high_threshold": 200,
        "image_resolution": "512"
    }
)
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 jagilley/controlnet-canny 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-canny:02a11802691b4733143bb9f343758671e4c98ae36f06de0a01a1ee79d68f8487",
    "input": {
      "eta": 0,
      "image": "https://replicate.delivery/pbxt/IMjSG2JLxIRfTr5dPhKGPeGrMFnn9ilWQ4tD0dwxQOHtYlUd/bird.png",
      "scale": 9,
      "prompt": "bird",
      "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",
      "low_threshold": 100,
      "high_threshold": 200,
      "image_resolution": "512"
    }
  }' \
  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/jagilley/controlnet-canny@sha256:02a11802691b4733143bb9f343758671e4c98ae36f06de0a01a1ee79d68f8487 \
  -i 'eta=0' \
  -i 'image="https://replicate.delivery/pbxt/IMjSG2JLxIRfTr5dPhKGPeGrMFnn9ilWQ4tD0dwxQOHtYlUd/bird.png"' \
  -i 'scale=9' \
  -i 'prompt="bird"' \
  -i 'a_prompt="best quality, extremely detailed"' \
  -i 'n_prompt="longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"' \
  -i 'ddim_steps=20' \
  -i 'num_samples="1"' \
  -i 'low_threshold=100' \
  -i 'high_threshold=200' \
  -i 'image_resolution="512"'

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/jagilley/controlnet-canny@sha256:02a11802691b4733143bb9f343758671e4c98ae36f06de0a01a1ee79d68f8487
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "eta": 0,
      "image": "https://replicate.delivery/pbxt/IMjSG2JLxIRfTr5dPhKGPeGrMFnn9ilWQ4tD0dwxQOHtYlUd/bird.png",
      "scale": 9,
      "prompt": "bird",
      "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",
      "low_threshold": 100,
      "high_threshold": 200,
      "image_resolution": "512"
    }
  }' \
  http://localhost:5000/predictions

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

Output

{
  "completed_at": "2023-02-23T19:08:53.887924Z",
  "created_at": "2023-02-23T19:08:21.252513Z",
  "data_removed": false,
  "error": null,
  "id": "jhvyxcao4jedhbc5irzecdd6wy",
  "input": {
    "image": "https://replicate.delivery/pbxt/IMjSG2JLxIRfTr5dPhKGPeGrMFnn9ilWQ4tD0dwxQOHtYlUd/bird.png",
    "scale": 9,
    "prompt": "bird",
    "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",
    "low_threshold": 100,
    "high_threshold": 200,
    "image_resolution": "512"
  },
  "logs": "Global seed set to 318892\nData shape for DDIM sampling is (1, 4, 96, 64), eta 0.0\nRunning DDIM Sampling with 20 timesteps\nDDIM Sampler:   0%|          | 0/20 [00:00<?, ?it/s]\nDDIM Sampler:   5%|▌         | 1/20 [00:01<00:27,  1.46s/it]\nDDIM Sampler:  10%|█         | 2/20 [00:02<00:26,  1.45s/it]\nDDIM Sampler:  15%|█▌        | 3/20 [00:04<00:24,  1.45s/it]\nDDIM Sampler:  20%|██        | 4/20 [00:05<00:23,  1.45s/it]\nDDIM Sampler:  25%|██▌       | 5/20 [00:07<00:21,  1.45s/it]\nDDIM Sampler:  30%|███       | 6/20 [00:08<00:20,  1.45s/it]\nDDIM Sampler:  35%|███▌      | 7/20 [00:10<00:18,  1.46s/it]\nDDIM Sampler:  40%|████      | 8/20 [00:11<00:17,  1.47s/it]\nDDIM Sampler:  45%|████▌     | 9/20 [00:13<00:16,  1.47s/it]\nDDIM Sampler:  50%|█████     | 10/20 [00:14<00:14,  1.48s/it]\nDDIM Sampler:  55%|█████▌    | 11/20 [00:16<00:13,  1.49s/it]\nDDIM Sampler:  60%|██████    | 12/20 [00:17<00:11,  1.49s/it]\nDDIM Sampler:  65%|██████▌   | 13/20 [00:19<00:10,  1.50s/it]\nDDIM Sampler:  70%|███████   | 14/20 [00:20<00:09,  1.50s/it]\nDDIM Sampler:  75%|███████▌  | 15/20 [00:22<00:07,  1.51s/it]\nDDIM Sampler:  80%|████████  | 16/20 [00:23<00:06,  1.52s/it]\nDDIM Sampler:  85%|████████▌ | 17/20 [00:25<00:04,  1.53s/it]\nDDIM Sampler:  90%|█████████ | 18/20 [00:26<00:03,  1.53s/it]\nDDIM Sampler:  95%|█████████▌| 19/20 [00:28<00:01,  1.53s/it]\nDDIM Sampler: 100%|██████████| 20/20 [00:29<00:00,  1.54s/it]\nDDIM Sampler: 100%|██████████| 20/20 [00:29<00:00,  1.50s/it]",
  "metrics": {
    "predict_time": 32.556173,
    "total_time": 32.635411
  },
  "output": [
    "https://replicate.delivery/pbxt/OeiJh1REUflXZEq8sGxXYdQ64wosoglQXTPf7rnQAwhJeaFCB/output_0.png",
    "https://replicate.delivery/pbxt/P7bf4e9I8InIlkBeawkZ96N3aeFeJpcXaIHf1bjNROtcxrVIE/output_1.png"
  ],
  "started_at": "2023-02-23T19:08:21.331751Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/jhvyxcao4jedhbc5irzecdd6wy",
    "cancel": "https://api.replicate.com/v1/predictions/jhvyxcao4jedhbc5irzecdd6wy/cancel"
  },
  "version": "02a11802691b4733143bb9f343758671e4c98ae36f06de0a01a1ee79d68f8487"
}

Generated in

32.6 seconds

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