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

fofr / lcm-profiling

Cold

Public
271 runs
A100 (80GB)

Run with an API

Playground API Examples README Versions

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:ddfd01a91e00fb5c55c1f35907f921d2e7b3fc2ebe62d468ff8a000b317ae2c2",
  {
    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:ddfd01a91e00fb5c55c1f35907f921d2e7b3fc2ebe62d468ff8a000b317ae2c2",
    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
    }
)
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/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": "ddfd01a91e00fb5c55c1f35907f921d2e7b3fc2ebe62d468ff8a000b317ae2c2",
    "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.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/fofr/lcm-profiling@sha256:ddfd01a91e00fb5c55c1f35907f921d2e7b3fc2ebe62d468ff8a000b317ae2c2 \
  -i 'width=768' \
  -i 'height=768' \
  -i 'prompt="Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"' \
  -i 'num_images=1' \
  -i 'guidance_scale=8' \
  -i 'archive_outputs=false' \
  -i 'prompt_strength=0.8' \
  -i 'sizing_strategy="width/height"' \
  -i 'lcm_origin_steps=50' \
  -i 'canny_low_threshold=100' \
  -i 'num_inference_steps=8' \
  -i 'canny_high_threshold=200' \
  -i 'control_guidance_end=1' \
  -i 'control_guidance_start=0' \
  -i 'controlnet_conditioning_scale=2'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/fofr/lcm-profiling@sha256:ddfd01a91e00fb5c55c1f35907f921d2e7b3fc2ebe62d468ff8a000b317ae2c2
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "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
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model costs approximately $0.0014 to run on Replicate, or 714 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 1 seconds.

Readme

This model doesn't have a readme.