anotherjesse/sdxl-lcm-testing – Run with an API on Replicate

anotherjesse / sdxl-lcm-testing

Cold

Public
362 runs
L40S

Run with an API

Playground API Examples Train README Versions

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: ""

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

scheduler

string

noise scheduler - only applies if LCM is disabled

Default: "K_EULER"

num_inference_steps

integer

(minimum: 1, maximum: 100)

Number of denoising steps

Default: 4

guidance_scale

number

(minimum: 0, maximum: 20)

Scale for classifier-free guidance

Default: 2

seed

integer

Random seed. Leave blank to randomize the seed

refine

string

Which refine style to use

Default: "no_refiner"

high_noise_frac

number

(minimum: 0, maximum: 1)

For expert_ensemble_refiner, the fraction of noise to use

Default: 0.8

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

lcm_scale

number

Scale for LCM, if 0, the DDIM scheduler is used

Default: 1

style_scale

number

Scale for style LoRA

Default: 0.8

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

npx create-replicate --model=anotherjesse/sdxl-lcm-testing

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

const output = await replicate.run(
  "anotherjesse/sdxl-lcm-testing:7050701ac789f535cd507847777404a4afaaae02827e96b78392847e71173a03",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "frankenstein monster whippet",
      refine: "no_refiner",
      lcm_scale: 1,
      scheduler: "K_EULER",
      num_outputs: 1,
      style_scale: 0.8,
      guidance_scale: 2,
      apply_watermark: true,
      high_noise_frac: 0.8,
      negative_prompt: "",
      num_inference_steps: 4
    }
  }
);
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 anotherjesse/sdxl-lcm-testing using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/sdxl-lcm-testing:7050701ac789f535cd507847777404a4afaaae02827e96b78392847e71173a03",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "frankenstein monster whippet",
        "refine": "no_refiner",
        "lcm_scale": 1,
        "scheduler": "K_EULER",
        "num_outputs": 1,
        "style_scale": 0.8,
        "guidance_scale": 2,
        "apply_watermark": True,
        "high_noise_frac": 0.8,
        "negative_prompt": "",
        "num_inference_steps": 4
    }
)
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 anotherjesse/sdxl-lcm-testing 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": "7050701ac789f535cd507847777404a4afaaae02827e96b78392847e71173a03",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "frankenstein monster whippet",
      "refine": "no_refiner",
      "lcm_scale": 1,
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "style_scale": 0.8,
      "guidance_scale": 2,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "num_inference_steps": 4
    }
  }' \
  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/anotherjesse/sdxl-lcm-testing@sha256:7050701ac789f535cd507847777404a4afaaae02827e96b78392847e71173a03 \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="frankenstein monster whippet"' \
  -i 'refine="no_refiner"' \
  -i 'lcm_scale=1' \
  -i 'scheduler="K_EULER"' \
  -i 'num_outputs=1' \
  -i 'style_scale=0.8' \
  -i 'guidance_scale=2' \
  -i 'apply_watermark=true' \
  -i 'high_noise_frac=0.8' \
  -i 'negative_prompt=""' \
  -i 'num_inference_steps=4'

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/anotherjesse/sdxl-lcm-testing@sha256:7050701ac789f535cd507847777404a4afaaae02827e96b78392847e71173a03
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "frankenstein monster whippet",
      "refine": "no_refiner",
      "lcm_scale": 1,
      "scheduler": "K_EULER",
      "num_outputs": 1,
      "style_scale": 0.8,
      "guidance_scale": 2,
      "apply_watermark": true,
      "high_noise_frac": 0.8,
      "negative_prompt": "",
      "num_inference_steps": 4
    }
  }' \
  http://localhost:5000/predictions

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

Output

Generated in

3.5 seconds

Tweak itReport

This example was created by a different version, anotherjesse/sdxl-lcm-testing:33df0b6c.

Examples

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Run time and cost

This model runs on Nvidia L40S GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

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