cjwbw/blipdiffusion-controlnet:6deffaf7 – Run with an API on Replicate

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cjwbw /blipdiffusion-controlnet:6deffaf7

Playground API

Input

prompt

string

Shift + Return to add a new line

The prompt to guide the image generation.

Default: "on a marble table"

negative_prompt

string

Shift + Return to add a new line

over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, ugly, bad anatomy, bad proportions, deformed, blurryover-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, ugly, bad anatomy, bad proportions, deformed, blurry

The prompt or prompts not to guide the image generation.

Default: "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, ugly, bad anatomy, bad proportions, deformed, blurry"

style_image

*file

The reference style image to condition the generation on.

condtioning_image

*file

The conditioning canny edge image to condition the generation on.

controlnet_type

string

Choose a control net

Default: "canny"

style_subject_category

string

Shift + Return to add a new line

The source subject category (subject that defines the style).

Default: "flower"

target_subject_category

string

Shift + Return to add a new line

The target subject category (subject to geenrate).

Default: "teapot"

num_inference_steps

integer

(minimum: 1, maximum: 500)

The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.

Default: 25

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance.

Default: 7.5

width

integer

The width of the generated image.

Default: 512

height

integer

The height of the generated image.

Default: 512

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

npx create-replicate --model=cjwbw/blipdiffusion-controlnet

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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

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

const output = await replicate.run(
  "cjwbw/blipdiffusion-controlnet:6deffaf765c99d5ecbbef16c50c8ff253266d572ff62d48d91ff84cc684a5d12",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "on a marble table",
      guidance_scale: 7.5,
      controlnet_type: "canny",
      negative_prompt: "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, ugly, bad anatomy, bad proportions, deformed, blurry",
      num_inference_steps: 25,
      style_subject_category: "flower",
      target_subject_category: "teapot"
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 cjwbw/blipdiffusion-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "cjwbw/blipdiffusion-controlnet:6deffaf765c99d5ecbbef16c50c8ff253266d572ff62d48d91ff84cc684a5d12",
    input={
        "width": 512,
        "height": 512,
        "prompt": "on a marble table",
        "guidance_scale": 7.5,
        "controlnet_type": "canny",
        "negative_prompt": "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, ugly, bad anatomy, bad proportions, deformed, blurry",
        "num_inference_steps": 25,
        "style_subject_category": "flower",
        "target_subject_category": "teapot"
    }
)
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 cjwbw/blipdiffusion-controlnet 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": "cjwbw/blipdiffusion-controlnet:6deffaf765c99d5ecbbef16c50c8ff253266d572ff62d48d91ff84cc684a5d12",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "on a marble table",
      "guidance_scale": 7.5,
      "controlnet_type": "canny",
      "negative_prompt": "over-exposure, under-exposure, saturated, duplicate, out of frame, lowres, cropped, worst quality, low quality, jpeg artifacts, morbid, mutilated, ugly, bad anatomy, bad proportions, deformed, blurry",
      "num_inference_steps": 25,
      "style_subject_category": "flower",
      "target_subject_category": "teapot"
    }
  }' \
  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.