fermatresearch/sdxl-controlnet-lora:a4fb8402 – Run with an API on Replicate

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fermatresearch /sdxl-controlnet-lora:a4fb8402

Playground API

Input

prompt

string

Shift + Return to add a new line

Input prompt

Default: "An astronaut riding a rainbow unicorn"

negative_prompt

string

Shift + Return to add a new line

Input Negative Prompt

Default: ""

image

file

Input image for img2img or inpaint mode

condition_scale

number

(minimum: 0, maximum: 1)

The bigger this number is, the more ControlNet interferes

Default: 0.5

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output

Default: 1

scheduler

string

scheduler

Default: "K_EULER"

num_inference_steps

integer

(minimum: 1, maximum: 500)

Number of denoising steps

Default: 50

guidance_scale

number

(minimum: 1, maximum: 50)

Scale for classifier-free guidance

Default: 7.5

seed

integer

Random seed. Leave blank to randomize the seed

refine

string

Whether to use refinement steps or not

Default: "base_image_refiner"

refine_steps

integer

For base_image_refiner, the number of steps to refine

Default: 10

apply_watermark

boolean

Applies a watermark to enable determining if an image is generated in downstream applications. If you have other provisions for generating or deploying images safely, you can use this to disable watermarking.

Default: true

lora_scale

number

(minimum: 0, maximum: 1)

LoRA additive scale. Only applicable on trained models.

Default: 0.6

lora_weights

string

Shift + Return to add a new line

Replicate LoRA weights to use. Leave blank to use the default weights.

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

npx create-replicate --model=fermatresearch/sdxl-controlnet-lora

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

const output = await replicate.run(
  "fermatresearch/sdxl-controlnet-lora:a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4",
  {
    input: {
      prompt: "An astronaut riding a rainbow unicorn",
      refine: "base_image_refiner",
      scheduler: "K_EULER",
      lora_scale: 0.6,
      num_outputs: 1,
      refine_steps: 10,
      guidance_scale: 7.5,
      apply_watermark: true,
      condition_scale: 0.5,
      negative_prompt: "",
      num_inference_steps: 50
    }
  }
);
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 fermatresearch/sdxl-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fermatresearch/sdxl-controlnet-lora:a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4",
    input={
        "prompt": "An astronaut riding a rainbow unicorn",
        "refine": "base_image_refiner",
        "scheduler": "K_EULER",
        "lora_scale": 0.6,
        "num_outputs": 1,
        "refine_steps": 10,
        "guidance_scale": 7.5,
        "apply_watermark": True,
        "condition_scale": 0.5,
        "negative_prompt": "",
        "num_inference_steps": 50
    }
)
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 fermatresearch/sdxl-controlnet-lora 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": "a4fb84022361602a2401d74435229e90da63ea4a2aab40ebf79afd7af5a081d4",
    "input": {
      "prompt": "An astronaut riding a rainbow unicorn",
      "refine": "base_image_refiner",
      "scheduler": "K_EULER",
      "lora_scale": 0.6,
      "num_outputs": 1,
      "refine_steps": 10,
      "guidance_scale": 7.5,
      "apply_watermark": true,
      "condition_scale": 0.5,
      "negative_prompt": "",
      "num_inference_steps": 50
    }
  }' \
  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.