fofr/sdxl-lcm-multi-controlnet-lora – Run with an API on Replicate

fofr / sdxl-lcm-multi-controlnet-lora

SDXL LCM with multi-controlnet, lora loading, img2img, inpainting

Cold

Public
7.4K runs
A100 (80GB)
GitHub
License

Run with an API

Playground API Examples README Versions

Input

prompt

string

Shift + Return to add a new line

A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mmA TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm

Input prompt

Default: "An astronaut riding a rainbow unicorn"

negative_prompt

string

Shift + Return to add a new line

Negative Prompt

Default: ""

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: 768

height

integer

Height of output image

Default: 768

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

num_inference_steps

integer

(minimum: 1, maximum: 30)

Number of denoising steps

Default: 4

guidance_scale

number

(minimum: 0, maximum: 50)

Scale for classifier-free guidance

Default: 1.1

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

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

https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tarhttps://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar

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

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

const output = await replicate.run(
  "fofr/sdxl-lcm-multi-controlnet-lora:750b7665e4eb1c02f65f374317e3d8dfe690ee42f3101a9c686cfd6d533534b7",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
      refine: "no_refiner",
      lora_scale: 0.8,
      num_outputs: 1,
      controlnet_1: "soft_edge_hed",
      controlnet_2: "none",
      controlnet_3: "none",
      lora_weights: "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
      guidance_scale: 1.1,
      apply_watermark: false,
      negative_prompt: "rainbow, soft, blurry",
      prompt_strength: 0.9,
      sizing_strategy: "width_height",
      controlnet_1_end: 0.5,
      controlnet_2_end: 1,
      controlnet_3_end: 1,
      controlnet_1_image: "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
      controlnet_1_start: 0,
      controlnet_2_start: 0,
      controlnet_3_start: 0,
      num_inference_steps: 8,
      controlnet_1_conditioning_scale: 0.6,
      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 fofr/sdxl-lcm-multi-controlnet-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/sdxl-lcm-multi-controlnet-lora:750b7665e4eb1c02f65f374317e3d8dfe690ee42f3101a9c686cfd6d533534b7",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
        "refine": "no_refiner",
        "lora_scale": 0.8,
        "num_outputs": 1,
        "controlnet_1": "soft_edge_hed",
        "controlnet_2": "none",
        "controlnet_3": "none",
        "lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
        "guidance_scale": 1.1,
        "apply_watermark": False,
        "negative_prompt": "rainbow, soft, blurry",
        "prompt_strength": 0.9,
        "sizing_strategy": "width_height",
        "controlnet_1_end": 0.5,
        "controlnet_2_end": 1,
        "controlnet_3_end": 1,
        "controlnet_1_image": "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
        "controlnet_1_start": 0,
        "controlnet_2_start": 0,
        "controlnet_3_start": 0,
        "num_inference_steps": 8,
        "controlnet_1_conditioning_scale": 0.6,
        "controlnet_2_conditioning_scale": 0.75,
        "controlnet_3_conditioning_scale": 0.75
    }
)
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 fofr/sdxl-lcm-multi-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": "750b7665e4eb1c02f65f374317e3d8dfe690ee42f3101a9c686cfd6d533534b7",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
      "refine": "no_refiner",
      "lora_scale": 0.8,
      "num_outputs": 1,
      "controlnet_1": "soft_edge_hed",
      "controlnet_2": "none",
      "controlnet_3": "none",
      "lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
      "guidance_scale": 1.1,
      "apply_watermark": false,
      "negative_prompt": "rainbow, soft, blurry",
      "prompt_strength": 0.9,
      "sizing_strategy": "width_height",
      "controlnet_1_end": 0.5,
      "controlnet_2_end": 1,
      "controlnet_3_end": 1,
      "controlnet_1_image": "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
      "controlnet_1_start": 0,
      "controlnet_2_start": 0,
      "controlnet_3_start": 0,
      "num_inference_steps": 8,
      "controlnet_1_conditioning_scale": 0.6,
      "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": "2023-11-20T14:35:45.961164Z",
  "created_at": "2023-11-20T14:35:42.095349Z",
  "data_removed": false,
  "error": null,
  "id": "2ni5643b37sh5k2zbs6dy3rlkm",
  "input": {
    "width": 1024,
    "height": 1024,
    "prompt": "A TOK photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm",
    "refine": "no_refiner",
    "lora_scale": 0.8,
    "num_outputs": 1,
    "controlnet_1": "soft_edge_hed",
    "controlnet_2": "none",
    "controlnet_3": "none",
    "lora_weights": "https://replicate.delivery/pbxt/hKhpVe6O7EwXNCiWORev3OEDRCoWeMlqZMLQDEvwDyHV3hvjA/trained_model.tar",
    "guidance_scale": 1.1,
    "apply_watermark": false,
    "negative_prompt": "rainbow, soft, blurry",
    "prompt_strength": 0.9,
    "sizing_strategy": "width_height",
    "controlnet_1_end": 0.5,
    "controlnet_2_end": 1,
    "controlnet_3_end": 1,
    "controlnet_1_image": "https://replicate.delivery/pbxt/JuVFxyGs9YTy2ce3q8rSCrgYJKJNJdHKxpZUvbdlDTKSExpg/out-0-44.png",
    "controlnet_1_start": 0,
    "controlnet_2_start": 0,
    "controlnet_3_start": 0,
    "num_inference_steps": 8,
    "controlnet_1_conditioning_scale": 0.6,
    "controlnet_2_conditioning_scale": 0.75,
    "controlnet_3_conditioning_scale": 0.75
  },
  "logs": "Using seed: 12353\nUsing given dimensions\nskipping loading .. weights already loaded\nPrompt: A <s0><s1> photo, extreme macro photo of a golden astronaut riding a unicorn statue, in a museum, 50mm\nProcessing image with soft_edge_hed\nLoading pipeline components...:   0%|          | 0/7 [00:00<?, ?it/s]\nLoading pipeline components...: 100%|██████████| 7/7 [00:00<00:00, 10512.04it/s]\nThe config attributes {'skip_prk_steps': True} were passed to LCMScheduler, but are not expected and will be ignored. Please verify your scheduler_config.json configuration file.\nYou have 1 ControlNets and you have passed 1 prompts. The conditionings will be fixed across the prompts.\n  0%|          | 0/8 [00:00<?, ?it/s]\n 12%|█▎        | 1/8 [00:00<00:01,  4.80it/s]\n 25%|██▌       | 2/8 [00:00<00:01,  4.79it/s]\n 38%|███▊      | 3/8 [00:00<00:01,  4.78it/s]\n 50%|█████     | 4/8 [00:00<00:00,  4.78it/s]\n 62%|██████▎   | 5/8 [00:01<00:00,  4.78it/s]\n 75%|███████▌  | 6/8 [00:01<00:00,  4.78it/s]\n 88%|████████▊ | 7/8 [00:01<00:00,  4.78it/s]\n100%|██████████| 8/8 [00:01<00:00,  4.78it/s]\n100%|██████████| 8/8 [00:01<00:00,  4.78it/s]",
  "metrics": {
    "predict_time": 3.852684,
    "total_time": 3.865815
  },
  "output": [
    "https://replicate.delivery/pbxt/J0wRPpT11M75Bdq6af3ssqiG0dReK64NPNMcRRgWqL8BDS6RA/control-0.png",
    "https://replicate.delivery/pbxt/IDmeLjuK5txPbKzy7qzm6Ahrm1w77dYKwP9l1GsPlqwgBJ9IA/out-0.png"
  ],
  "started_at": "2023-11-20T14:35:42.108480Z",
  "status": "succeeded",
  "urls": {
    "get": "https://api.replicate.com/v1/predictions/2ni5643b37sh5k2zbs6dy3rlkm",
    "cancel": "https://api.replicate.com/v1/predictions/2ni5643b37sh5k2zbs6dy3rlkm/cancel"
  },
  "version": "abf7d7a5c8a18aba0876166ad4f33d4a0a07dda68512c289f35611bae32ea8e8"
}

Generated in

3.9 seconds

Tweak itReport

This output was created using a different version of the model, fofr/sdxl-lcm-multi-controlnet-lora:abf7d7a5.

Examples

View more examples

Run time and cost

This model costs approximately $0.14 to run on Replicate, or 7 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 A100 (80GB) GPU hardware. Predictions typically complete within 99 seconds. The predict time for this model varies significantly based on the inputs.

Readme

This model doesn't have a readme.