fofr/lcm-profiling:a3dee214 – Run with an API on Replicate

Version

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fofr /lcm-profiling:a3dee214

Input

prompt

string

Shift + Return to add a new line

Self-portrait oil painting, a beautiful cyborg with golden hair, 8kSelf-portrait oil painting, a beautiful cyborg with golden hair, 8k

For multiple prompts, enter each on a new line.

Default: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"

width

integer

Width of output image. Lower if out of memory

Default: 768

height

integer

Height of output image. Lower if out of memory

Default: 768

sizing_strategy

string

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

Default: "width/height"

image

file

Input image for img2img

prompt_strength

number

(minimum: 0, maximum: 1)

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

Default: 0.8

num_images

integer

(minimum: 1, maximum: 50)

Number of images per prompt

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 50)

Number of denoising steps. Recommend 1 to 8 steps.

Default: 8

guidance_scale

number

(minimum: 1, maximum: 20)

Scale for classifier-free guidance

Default: 8

lcm_origin_steps

integer

(minimum: 1)

Default: 50

seed

integer

Random seed. Leave blank to randomize the seed

control_image

file

Image for controlnet conditioning

controlnet_conditioning_scale

number

(minimum: 0.1, maximum: 4)

Controlnet conditioning scale

Default: 2

control_guidance_start

number

(minimum: 0, maximum: 1)

Controlnet start

Default: 0

control_guidance_end

number

(minimum: 0, maximum: 1)

Controlnet end

Default: 1

canny_low_threshold

number

(minimum: 1, maximum: 255)

Canny low threshold

Default: 100

canny_high_threshold

number

(minimum: 1, maximum: 255)

Canny high threshold

Default: 200

archive_outputs

boolean

Option to archive the output images

Default: false

disable_safety_checker

boolean

Disable safety checker for generated images. This feature is only available through the API

Default: false

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

npx create-replicate --model=fofr/lcm-profiling

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/lcm-profiling using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "fofr/lcm-profiling:a3dee2146d983074635e5cef45f8c3cd41f89c1d5caef603d225ace6ea51583c",
  {
    input: {
      width: 768,
      height: 768,
      prompt: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      num_images: 1,
      guidance_scale: 8,
      archive_outputs: false,
      prompt_strength: 0.8,
      sizing_strategy: "width/height",
      lcm_origin_steps: 50,
      canny_low_threshold: 100,
      num_inference_steps: 8,
      canny_high_threshold: 200,
      control_guidance_end: 1,
      control_guidance_start: 0,
      controlnet_conditioning_scale: 2
    }
  }
);

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 fofr/lcm-profiling using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "fofr/lcm-profiling:a3dee2146d983074635e5cef45f8c3cd41f89c1d5caef603d225ace6ea51583c",
    input={
        "width": 768,
        "height": 768,
        "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
        "num_images": 1,
        "guidance_scale": 8,
        "archive_outputs": False,
        "prompt_strength": 0.8,
        "sizing_strategy": "width/height",
        "lcm_origin_steps": 50,
        "canny_low_threshold": 100,
        "num_inference_steps": 8,
        "canny_high_threshold": 200,
        "control_guidance_end": 1,
        "control_guidance_start": 0,
        "controlnet_conditioning_scale": 2
    }
)

# The fofr/lcm-profiling 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/fofr/lcm-profiling/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 fofr/lcm-profiling 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": "a3dee2146d983074635e5cef45f8c3cd41f89c1d5caef603d225ace6ea51583c",
    "input": {
      "width": 768,
      "height": 768,
      "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      "num_images": 1,
      "guidance_scale": 8,
      "archive_outputs": false,
      "prompt_strength": 0.8,
      "sizing_strategy": "width/height",
      "lcm_origin_steps": 50,
      "canny_low_threshold": 100,
      "num_inference_steps": 8,
      "canny_high_threshold": 200,
      "control_guidance_end": 1,
      "control_guidance_start": 0,
      "controlnet_conditioning_scale": 2
    }
  }' \
  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.