alexgenovese/autotrain-dreambooth | Run with an API on Replicate

Input

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

npx create-replicate --model=alexgenovese/autotrain-dreambooth

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";
import fs from "node:fs";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run alexgenovese/autotrain-dreambooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "alexgenovese/autotrain-dreambooth:e3e8ff8604f3101092341004f3cd9ebaf3fc92b9b1fc46725ac88f819c83a52f",
  {
    input: {
      lr: 0.0004,
      xl: true,
      seed: 42,
      model: "stabilityai/stable-diffusion-xl-base-1.0",
      prompt: "a photo of aisudhca bag",
      xformers: false,
      num_steps: 2000,
      scheduler: "constant",
      batch_size: 1,
      num_cycles: 1,
      resolution: 1024,
      center_crop: false,
      warmup_steps: 0,
      use_8bit_adam: false,
      mixed_precision: "bf16",
      train_text_encoder: true,
      checkpointing_steps: 500,
      gradient_accumulation: 1
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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 alexgenovese/autotrain-dreambooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "alexgenovese/autotrain-dreambooth:e3e8ff8604f3101092341004f3cd9ebaf3fc92b9b1fc46725ac88f819c83a52f",
    input={
        "lr": 0.0004,
        "xl": True,
        "seed": 42,
        "model": "stabilityai/stable-diffusion-xl-base-1.0",
        "prompt": "a photo of aisudhca bag",
        "xformers": False,
        "num_steps": 2000,
        "scheduler": "constant",
        "batch_size": 1,
        "num_cycles": 1,
        "resolution": 1024,
        "center_crop": False,
        "warmup_steps": 0,
        "use_8bit_adam": False,
        "mixed_precision": "bf16",
        "train_text_encoder": True,
        "checkpointing_steps": 500,
        "gradient_accumulation": 1
    }
)
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 alexgenovese/autotrain-dreambooth 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": "alexgenovese/autotrain-dreambooth:e3e8ff8604f3101092341004f3cd9ebaf3fc92b9b1fc46725ac88f819c83a52f",
    "input": {
      "lr": 0.0004,
      "xl": true,
      "seed": 42,
      "model": "stabilityai/stable-diffusion-xl-base-1.0",
      "prompt": "a photo of aisudhca bag",
      "xformers": false,
      "num_steps": 2000,
      "scheduler": "constant",
      "batch_size": 1,
      "num_cycles": 1,
      "resolution": 1024,
      "center_crop": false,
      "warmup_steps": 0,
      "use_8bit_adam": false,
      "mixed_precision": "bf16",
      "train_text_encoder": true,
      "checkpointing_steps": 500,
      "gradient_accumulation": 1
    }
  }' \
  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 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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