espressotechie/lcm-dreamshaper8:fc367d0b – Run with an API on Replicate

Version

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espressotechie /lcm-dreamshaper8:fc367d0b

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

Input prompt

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

negative_prompt

string

Shift + Return to add a new line

Negative prompt

width

integer

Width of output image. Lower if out of memory

Default: 512

height

integer

Height of output image. Lower if out of memory

Default: 512

num_images

integer

(minimum: 1, maximum: 50)

Number of images to output

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 20)

Number of denoising steps. Recommend 1 to 8 steps.

Default: 4

guidance_scale

number

(minimum: 0, maximum: 20)

Scale for classifier-free guidance

Default: 0

seed

integer

Random seed. Leave blank to randomize the seed

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

npx create-replicate --model=espressotechie/lcm-dreamshaper8

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 espressotechie/lcm-dreamshaper8 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "espressotechie/lcm-dreamshaper8:fc367d0b29168ee2ad6eb234251e44eaab51b2ca592195b248ac82265c303158",
  {
    input: {
      width: 512,
      height: 512,
      prompt: "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      num_images: 1,
      guidance_scale: 0,
      num_inference_steps: 4
    }
  }
);

// 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 espressotechie/lcm-dreamshaper8 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "espressotechie/lcm-dreamshaper8:fc367d0b29168ee2ad6eb234251e44eaab51b2ca592195b248ac82265c303158",
    input={
        "width": 512,
        "height": 512,
        "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
        "num_images": 1,
        "guidance_scale": 0,
        "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 espressotechie/lcm-dreamshaper8 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": "fc367d0b29168ee2ad6eb234251e44eaab51b2ca592195b248ac82265c303158",
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      "num_images": 1,
      "guidance_scale": 0,
      "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/espressotechie/lcm-dreamshaper8@sha256:fc367d0b29168ee2ad6eb234251e44eaab51b2ca592195b248ac82265c303158 \
  -i 'width=512' \
  -i 'height=512' \
  -i 'prompt="Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"' \
  -i 'num_images=1' \
  -i 'guidance_scale=0' \
  -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/espressotechie/lcm-dreamshaper8@sha256:fc367d0b29168ee2ad6eb234251e44eaab51b2ca592195b248ac82265c303158
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 512,
      "height": 512,
      "prompt": "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k",
      "num_images": 1,
      "guidance_scale": 0,
      "num_inference_steps": 4
    }
  }' \
  http://localhost:5000/predictions

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

Output

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