web-cardinalblue/piccollage-bg-lora – Run with an API on Replicate

web-cardinalblue / piccollage-bg-lora

Cold

Public
476 runs
A100 (80GB)
GitHub

Run with an API

Playground API Examples README Versions

Input

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

npx create-replicate --model=web-cardinalblue/piccollage-bg-lora

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 web-cardinalblue/piccollage-bg-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "web-cardinalblue/piccollage-bg-lora:88d86ab8b9c4472daf9581dd74966b500c477ed93d4e7842379fd7713b5ee9f4",
  {
    input: {
      seamless: true,
      batch_size: 8,
      lora_model: "Vector Style, 77MB_Rank100_Steps4000",
      num_images: 8,
      negative_prompt: "weird, blurred, low-quality, clustered, ugly"
    }
  }
);
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 web-cardinalblue/piccollage-bg-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "web-cardinalblue/piccollage-bg-lora:88d86ab8b9c4472daf9581dd74966b500c477ed93d4e7842379fd7713b5ee9f4",
    input={
        "seamless": True,
        "batch_size": 8,
        "lora_model": "Vector Style, 77MB_Rank100_Steps4000",
        "num_images": 8,
        "negative_prompt": "weird, blurred, low-quality, clustered, ugly"
    }
)
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 web-cardinalblue/piccollage-bg-lora 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": "88d86ab8b9c4472daf9581dd74966b500c477ed93d4e7842379fd7713b5ee9f4",
    "input": {
      "seamless": true,
      "batch_size": 8,
      "lora_model": "Vector Style, 77MB_Rank100_Steps4000",
      "num_images": 8,
      "negative_prompt": "weird, blurred, low-quality, clustered, ugly"
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

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

Run time and cost

This model costs approximately $0.0093 to run on Replicate, or 107 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 7 seconds.

Readme

This model generates a prompt in piccollage style

…

Model description

You can pick the LoRA model and it will attach the corresponding layers to the base model during inference time. It also automatically adds the following suffixes to the prompt you enter:

Vector Style, 77MB_Rank100_Steps4000: , background in sks style
Vector Style, 77MB_Rank100_Steps6000: , background in sks style
Vector Style, 7MB_Rank10_Steps4000: , piccollage style

This is because the larger models were trained with the prompt background in sks style while the smaller model was trained with the prompt piccollage style. But you don’t need to worry about it.