anotherjesse/controlnet-1.5-canny-template – Run with an API on Replicate

anotherjesse / controlnet-1.5-canny-template

Template Version for use with Dreambooth API

Cold

Public
22 runs
T4
GitHub

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=anotherjesse/controlnet-1.5-canny-template

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 anotherjesse/controlnet-1.5-canny-template using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "anotherjesse/controlnet-1.5-canny-template:3c60cbfce253b1d82fea02c7692d13c1e96b36a22da784470fcbedc603a1ed4b",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "photo of cjw person",
      scheduler: "DPMSolverMultistep",
      num_outputs: 1,
      low_threshold: 100,
      guidance_scale: 7.5,
      high_threshold: 200,
      prompt_strength: 0.8,
      num_inference_steps: 50,
      disable_safety_check: false,
      return_processed_control: false
    }
  }
);
console.log(output);

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 anotherjesse/controlnet-1.5-canny-template using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/controlnet-1.5-canny-template:3c60cbfce253b1d82fea02c7692d13c1e96b36a22da784470fcbedc603a1ed4b",
    input={
        "width": 512,
        "height": 512,
        "prompt": "photo of cjw person",
        "scheduler": "DPMSolverMultistep",
        "num_outputs": 1,
        "low_threshold": 100,
        "guidance_scale": 7.5,
        "high_threshold": 200,
        "prompt_strength": 0.8,
        "num_inference_steps": 50,
        "disable_safety_check": False,
        "return_processed_control": False
    }
)

# The anotherjesse/controlnet-1.5-canny-template model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/anotherjesse/controlnet-1.5-canny-template/api#output-schema
    print(item)

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 anotherjesse/controlnet-1.5-canny-template 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": "3c60cbfce253b1d82fea02c7692d13c1e96b36a22da784470fcbedc603a1ed4b",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "photo of cjw person",
      "scheduler": "DPMSolverMultistep",
      "num_outputs": 1,
      "low_threshold": 100,
      "guidance_scale": 7.5,
      "high_threshold": 200,
      "prompt_strength": 0.8,
      "num_inference_steps": 50,
      "disable_safety_check": false,
      "return_processed_control": false
    }
  }' \
  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.

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

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Model description

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Intended use

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Ethical considerations

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Caveats and recommendations

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