lightweight-ai/model2:55b54a53 – Run with an API on Replicate

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lightweight-ai /model2:55b54a53

Playground API

Input

prompt

*string

Shift + Return to add a new line

Prompt for generated image

width

integer

Width of output image

Default: 1024

height

integer

Height of output image

Default: 1024

image

file

Input image for image to image mode. The aspect ratio of your output will match this image

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_outputs

integer

(minimum: 1, maximum: 4)

Number of outputs to generate

Default: 1

num_inference_steps

integer

(minimum: 1, maximum: 50)

Number of denoising steps. Recommended range is 28-50

Default: 28

guidance

number

(minimum: 1, maximum: 10)

Guidance for generated image

Default: 3

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

disable_safety_checker

boolean

Disable safety checker for generated images.

Default: false

go_fast

boolean

Run faster predictions with model optimized for speed (currently fp8 quantized); disable to run in original bf16

Default: true

lora_weights

string

Shift + Return to add a new line

Load LoRA weights. Supports Replicate models in the format <owner>/<username> or <owner>/<username>/<version>, HuggingFace URLs in the format huggingface.co/<owner>/<model-name>, CivitAI URLs in the format civitai.com/models/<id>[/<model-name>], or arbitrary .safetensors URLs from the Internet. For example, 'fofr/flux-pixar-cars'

lora_scale

number

(minimum: -1, maximum: 3)

Determines how strongly the main LoRA should be applied. Sane results between 0 and 1 for base inference. For go_fast we apply a 1.5x multiplier to this value; we've generally seen good performance when scaling the base value by that amount. You may still need to experiment to find the best value for your particular lora.

Default: 1

megapixels

string

Approximate number of megapixels for generated image

Default: "1"

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

npx create-replicate --model=lightweight-ai/model2

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/model2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "lightweight-ai/model2:55b54a5347c6cbd18b480c6b3ce19a0a8d2790ea8becd50c773e4c8b2e2e039d",
  {
    input: {
      width: 1024,
      height: 1024,
      go_fast: true,
      guidance: 3,
      lora_scale: 1,
      megapixels: "1",
      num_outputs: 1,
      output_format: "png",
      output_quality: 100,
      prompt_strength: 0.8,
      num_inference_steps: 28
    }
  }
);
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 lightweight-ai/model2 using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "lightweight-ai/model2:55b54a5347c6cbd18b480c6b3ce19a0a8d2790ea8becd50c773e4c8b2e2e039d",
    input={
        "width": 1024,
        "height": 1024,
        "go_fast": True,
        "guidance": 3,
        "lora_scale": 1,
        "megapixels": "1",
        "num_outputs": 1,
        "output_format": "png",
        "output_quality": 100,
        "prompt_strength": 0.8,
        "num_inference_steps": 28
    }
)
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/model2 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": "55b54a5347c6cbd18b480c6b3ce19a0a8d2790ea8becd50c773e4c8b2e2e039d",
    "input": {
      "width": 1024,
      "height": 1024,
      "go_fast": true,
      "guidance": 3,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "num_inference_steps": 28
    }
  }' \
  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/model2@sha256:55b54a5347c6cbd18b480c6b3ce19a0a8d2790ea8becd50c773e4c8b2e2e039d \
  -i 'width=1024' \
  -i 'height=1024' \
  -i 'go_fast=true' \
  -i 'guidance=3' \
  -i 'lora_scale=1' \
  -i 'megapixels="1"' \
  -i 'num_outputs=1' \
  -i 'output_format="png"' \
  -i 'output_quality=100' \
  -i 'prompt_strength=0.8' \
  -i 'num_inference_steps=28'

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/model2@sha256:55b54a5347c6cbd18b480c6b3ce19a0a8d2790ea8becd50c773e4c8b2e2e039d
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "width": 1024,
      "height": 1024,
      "go_fast": true,
      "guidance": 3,
      "lora_scale": 1,
      "megapixels": "1",
      "num_outputs": 1,
      "output_format": "png",
      "output_quality": 100,
      "prompt_strength": 0.8,
      "num_inference_steps": 28
    }
  }' \
  http://localhost:5000/predictions

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

Output

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