lucataco/sdxl-lightning-multi-controlnet | Run with an API on Replicate

SDXL lightning mult-controlnet, img2img & inpainting

Cold

Public

9.6K runs

L40S

License

GitHub

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mmextreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm

Input prompt

Default: "A monkey making latte art"

negative_prompt

string

Shift + Return to add a new line

Negative Prompt

Default: "worst quality, low quality"

image

file

Input image for img2img or inpaint mode

mask

file

Input mask for inpaint mode. Black areas will be preserved, white areas will be inpainted.

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

sizing_strategy

string

Decide how to resize images – use width/height, resize based on input image or control image

Default: "width_height"

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: 4

guidance_scale

number

(minimum: 0, maximum: 50)

Scale for classifier-free guidance

Default: 0

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength when using img2img / inpaint. 1.0 corresponds to full destruction of information in image

Default: 0.8

seed

integer

Random seed. Leave blank to randomize the seed

refine

string

Which refine style to use

Default: "no_refiner"

refine_steps

integer

For base_image_refiner, the number of steps to refine, defaults to num_inference_steps

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

disable_safety_checker

boolean

Disable safety checker for generated images. This feature is only available through the API. See [https://replicate.com/docs/how-does-replicate-work#safety](https://replicate.com/docs/how-does-replicate-work#safety)

Default: false

controlnet_1

string

Controlnet

Default: "none"

controlnet_1_image

file

Preview

Input image for first controlnet

controlnet_1_conditioning_scale

number

(minimum: 0, maximum: 4)

How strong the controlnet conditioning is

Default: 0.75

controlnet_1_start

number

(minimum: 0, maximum: 1)

When controlnet conditioning starts

Default: 0

controlnet_1_end

number

(minimum: 0, maximum: 1)

When controlnet conditioning ends

Default: 1

controlnet_2

string

Controlnet

Default: "none"

controlnet_2_image

file

Input image for second controlnet

controlnet_2_conditioning_scale

number

(minimum: 0, maximum: 4)

How strong the controlnet conditioning is

Default: 0.75

controlnet_2_start

number

(minimum: 0, maximum: 1)

When controlnet conditioning starts

Default: 0

controlnet_2_end

number

(minimum: 0, maximum: 1)

When controlnet conditioning ends

Default: 1

controlnet_3

string

Controlnet

Default: "none"

controlnet_3_image

file

Input image for third controlnet

controlnet_3_conditioning_scale

number

(minimum: 0, maximum: 4)

How strong the controlnet conditioning is

Default: 0.75

controlnet_3_start

number

(minimum: 0, maximum: 1)

When controlnet conditioning starts

Default: 0

controlnet_3_end

number

(minimum: 0, maximum: 1)

When controlnet conditioning ends

Default: 1

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

npx create-replicate --model=lucataco/sdxl-lightning-multi-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 lucataco/sdxl-lightning-multi-controlnet using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
      refine: "no_refiner",
      scheduler: "K_EULER",
      num_outputs: 1,
      controlnet_1: "soft_edge_hed",
      controlnet_2: "none",
      controlnet_3: "none",
      guidance_scale: 0,
      apply_watermark: true,
      negative_prompt: "worst quality, low quality",
      prompt_strength: 0.8,
      sizing_strategy: "width_height",
      controlnet_1_end: 1,
      controlnet_2_end: 1,
      controlnet_3_end: 1,
      controlnet_1_image: "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
      controlnet_1_start: 0,
      controlnet_2_start: 0,
      controlnet_3_start: 0,
      num_inference_steps: 4,
      controlnet_1_conditioning_scale: 0.75,
      controlnet_2_conditioning_scale: 0.75,
      controlnet_3_conditioning_scale: 0.75
    }
  }
);

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

output = replicate.run(
    "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
        "refine": "no_refiner",
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "controlnet_1": "soft_edge_hed",
        "controlnet_2": "none",
        "controlnet_3": "none",
        "guidance_scale": 0,
        "apply_watermark": True,
        "negative_prompt": "worst quality, low quality",
        "prompt_strength": 0.8,
        "sizing_strategy": "width_height",
        "controlnet_1_end": 1,
        "controlnet_2_end": 1,
        "controlnet_3_end": 1,
        "controlnet_1_image": "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
        "controlnet_1_start": 0,
        "controlnet_2_start": 0,
        "controlnet_3_start": 0,
        "num_inference_steps": 4,
        "controlnet_1_conditioning_scale": 0.75,
        "controlnet_2_conditioning_scale": 0.75,
        "controlnet_3_conditioning_scale": 0.75
    }
)

# To access the file URL:
print(output[0].url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output[0].read())

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 lucataco/sdxl-lightning-multi-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": "lucataco/sdxl-lightning-multi-controlnet:d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
      "refine": "no_refiner",
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "controlnet_1": "soft_edge_hed",
      "controlnet_2": "none",
      "controlnet_3": "none",
      "guidance_scale": 0,
      "apply_watermark": true,
      "negative_prompt": "worst quality, low quality",
      "prompt_strength": 0.8,
      "sizing_strategy": "width_height",
      "controlnet_1_end": 1,
      "controlnet_2_end": 1,
      "controlnet_3_end": 1,
      "controlnet_1_image": "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
      "controlnet_1_start": 0,
      "controlnet_2_start": 0,
      "controlnet_3_start": 0,
      "num_inference_steps": 4,
      "controlnet_1_conditioning_scale": 0.75,
      "controlnet_2_conditioning_scale": 0.75,
      "controlnet_3_conditioning_scale": 0.75
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

{
  "completed_at": "2024-02-27T06:47:01.532833Z",
  "created_at": "2024-02-27T06:46:58.234118Z",
  "data_removed": false,
  "error": null,
  "id": "56rxbylbcjgp6fkozzwcp75dx4",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm",
    "refine": "no_refiner",
    "scheduler": "K_EULER",
    "num_outputs": 1,
    "controlnet_1": "soft_edge_hed",
    "controlnet_2": "none",
    "controlnet_3": "none",
    "guidance_scale": 0,
    "apply_watermark": true,
    "negative_prompt": "worst quality, low quality",
    "prompt_strength": 0.8,
    "sizing_strategy": "width_height",
    "controlnet_1_end": 1,
    "controlnet_2_end": 1,
    "controlnet_3_end": 1,
    "controlnet_1_image": "https://replicate.delivery/pbxt/KTWobEZiPRKJanaeqp2FqDfppvecWkJIZARLqRU3F0U54dtl/astro-on-horse.png",
    "controlnet_1_start": 0,
    "controlnet_2_start": 0,
    "controlnet_3_start": 0,
    "num_inference_steps": 4,
    "controlnet_1_conditioning_scale": 0.75,
    "controlnet_2_conditioning_scale": 0.75,
    "controlnet_3_conditioning_scale": 0.75
  },
  "logs": "Using seed: 3924493178\nUsing given dimensions\nresize took: 0.03s\nPrompt: extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, bokeh, 50mm\nProcessing image with soft_edge_hed\ncontrolnet preprocess took: 0.58s\nUsing txt2img + controlnet pipeline\nLoading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 15134.09it/s]\nYou have 1 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n  0%|          | 0/4 [00:00<?, ?it/s]\n 25%|██▌       | 1/4 [00:00<00:00,  6.19it/s]\n 50%|█████     | 2/4 [00:00<00:00,  6.16it/s]\n 75%|███████▌  | 3/4 [00:00<00:00,  6.14it/s]\n100%|██████████| 4/4 [00:00<00:00,  6.14it/s]\n100%|██████████| 4/4 [00:00<00:00,  6.14it/s]\ninference took: 1.25s\nprediction took: 2.34s",
  "metrics": {
    "predict_time": 3.286964,
    "total_time": 3.298715
  },
  "output": [
    "https://replicate.delivery/pbxt/uWqiRRbTlezCGyfu5ziSaIpsiuzmLGqukIJQeWGztkRJ7m1kA/control-0.png",
    "https://replicate.delivery/pbxt/X0rtJUsEeXyOekN1yAEqeJLvSQegmKQoeroNC9Hzef6myuZNJA/out-0.png"
  ],
  "started_at": "2024-02-27T06:46:58.245869Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/56rxbylbcjgp6fkozzwcp75dx4",
    "cancel": "https://api.replicate.com/v1/predictions/56rxbylbcjgp6fkozzwcp75dx4/cancel"
  },
  "version": "d5116b11698b41d34c322cbd7b0bf068015e47831af0527de7a178dc59c5f2ee"
}

Generated in

3.3 seconds

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Examples

View more examples

Run time and cost

This model costs approximately $0.011 to run on Replicate, or 90 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia L40S GPU hardware. Predictions typically complete within 12 seconds. The predict time for this model varies significantly based on the inputs.

Readme

SDXL with:

img2img
inpainting
up to 3 simultaneous controlnets with different images
img2img plus controlnet
inpainting plus controlnet
controlnet conditioning strengths
controlnet start and end controls
SDXL refiner
Image resizing based on width/height, input image or a control image
Disable safety checker via API

Controlnets included:

canny
midas depth
leres depth
soft edge hed
soft edge pidi
openpose
QR Monster (illusions)
lineart
lineart anime