lightweight-ai/model1:3741e9d0 – Run with an API on Replicate

Version

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lightweight-ai /model1:3741e9d0

Input

prompt

string

Shift + Return to add a new line

A fluffy, orange tabby cat curled up asleep in a sunbeam streaming through a window, its soft fur glowing with the warmth of the light; highly detailed 8K UHD photorealistic rendering, natural lighting, warm and inviting atmosphere, focus on softness and texture.A fluffy, orange tabby cat curled up asleep in a sunbeam streaming through a window, its soft fur glowing with the warmth of the light; highly detailed 8K UHD photorealistic rendering, natural lighting, warm and inviting atmosphere, focus on softness and texture.

Prompt for generated image

Default: "A fluffy, orange tabby cat curled up asleep in a sunbeam streaming through a window, its soft fur glowing with the warmth of the light; highly detailed 8K UHD photorealistic rendering, natural lighting, warm and inviting atmosphere, focus on softness and texture."

image

string

Shift + Return to add a new line

image url

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

prompt_strength

number

(minimum: 0, maximum: 1)

Prompt strength (or denoising strength) when using image to image. 1.0 corresponds to full destruction of information in image.

Default: 0.8

num_outputs

integer

(minimum: 1, maximum: 4)

Number of images to output.

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 12)

Number of inference steps

Default: 4

loras

*string[]

Lora list

lora_scales

*number[]

Enter a string of numbers separated by commas

Lora scales

mask

string

Shift + Return to add a new line

Upload a mask image for inpainting. White areas (255) indicate regions to be inpainted, while black areas (0) will be preserved from the original image.

seed

integer

Random seed. Set for reproducible generation

output_format

string

Format of the output images

Default: "png"

output_quality

integer

(minimum: 0, maximum: 100)

Quality when saving the output images, from 0 to 100. 100 is best quality, 0 is lowest quality. Not relevant for .png outputs

Default: 100

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

npx create-replicate --model=lightweight-ai/model1

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 lightweight-ai/model1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lightweight-ai/model1:3741e9d0979f75975cd801c22a9e5fe12af6d0adc3c8ed55ed9876b120061690",
  {
    input: {
      width: 1024,
      height: 1024,
      prompt: "A fluffy, orange tabby cat curled up asleep in a sunbeam streaming through a window, its soft fur glowing with the warmth of the light; highly detailed 8K UHD photorealistic rendering, natural lighting, warm and inviting atmosphere, focus on softness and texture.",
      num_outputs: 1,
      output_format: "png",
      output_quality: 100,
      prompt_strength: 0.8,
      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 lightweight-ai/model1 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lightweight-ai/model1:3741e9d0979f75975cd801c22a9e5fe12af6d0adc3c8ed55ed9876b120061690",
    input={
        "width": 1024,
        "height": 1024,
        "prompt": "A fluffy, orange tabby cat curled up asleep in a sunbeam streaming through a window, its soft fur glowing with the warmth of the light; highly detailed 8K UHD photorealistic rendering, natural lighting, warm and inviting atmosphere, focus on softness and texture.",
        "num_outputs": 1,
        "output_format": "png",
        "output_quality": 100,
        "prompt_strength": 0.8,
        "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 lightweight-ai/model1 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": "3741e9d0979f75975cd801c22a9e5fe12af6d0adc3c8ed55ed9876b120061690",
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A fluffy, orange tabby cat curled up asleep in a sunbeam streaming through a window, its soft fur glowing with the warmth of the light; highly detailed 8K UHD photorealistic rendering, natural lighting, warm and inviting atmosphere, focus on softness and texture.",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "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/lightweight-ai/model1@sha256:3741e9d0979f75975cd801c22a9e5fe12af6d0adc3c8ed55ed9876b120061690 \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'prompt="A fluffy, orange tabby cat curled up asleep in a sunbeam streaming through a window, its soft fur glowing with the warmth of the light; highly detailed 8K UHD photorealistic rendering, natural lighting, warm and inviting atmosphere, focus on softness and texture."' \
  -i 'num_outputs=1' \
  -i 'output_format="png"' \
  -i 'output_quality=100' \
  -i 'prompt_strength=0.8' \
  -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/lightweight-ai/model1@sha256:3741e9d0979f75975cd801c22a9e5fe12af6d0adc3c8ed55ed9876b120061690
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "prompt": "A fluffy, orange tabby cat curled up asleep in a sunbeam streaming through a window, its soft fur glowing with the warmth of the light; highly detailed 8K UHD photorealistic rendering, natural lighting, warm and inviting atmosphere, focus on softness and texture.",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "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.