zenaivn/dreambooth-training | Run with an API on Replicate

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L40S

Run with an API

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Playground API Examples README Versions

Input

train_dreambooth

boolean

Whether to train dreambooth or not.

Default: true

train_lora

boolean

Whether to train lora or not.

Default: false

train_lora_sliders

boolean

Whether to train lora sliders or not.

Default: false

model_replicate_link

string

Shift + Return to add a new line

(Optional) Enter your model replicate link for lora sliders training. In case you don't have your own, here is a test link: https://replicate.delivery/pbxt/mu6jfnIIdQWIMKpBXRX3AkRltbjmwEX7kERNInQptcSM3VIJA/output.zip

breast

boolean

(Optional) Choose if you want to train lora slider for breast

Default: true

butt

boolean

(Optional) Choose if you want to train lora slider for butt

Default: false

weight

boolean

(Optional) Choose if you want to train lora slider for weight

Default: false

class_name

*string

Shift + Return to add a new line

Specify the class name (for example: man/woman/style/...).

identifier

string

Shift + Return to add a new line

Unique identifier for the model instance.

Default: "ohwx"

num_repeats

string

Shift + Return to add a new line

Number of times the input images should be repeated.

Default: "1"

output_name

*string

Shift + Return to add a new line

Name of the model's output file. (for example: output_model)

instance_data

*string

Shift + Return to add a new line

A ZIP file containing your training images (JPG, PNG, etc. size not restricted). These images contain your 'subject' that you want the trained model to embed in the output domain for later generating customized scenes beyond the training images. For best results, use images without noise or unrelated objects in the background.

class_data

file

An optional ZIP file containing the training data of class images. This corresponds to `class_prompt` above, also with the purpose of keeping the model generalizable. By default, the pretrained stable-diffusion model will generate N images (determined by the `num_class_images` you set) based on the `class_prompt` provided. But to save time or to have your preferred specific set of `class_data`, you can also provide them in a ZIP file.

using_crop_images

boolean

Whether to use crop images or not.

Default: true

train_batch_size

integer

Batch size for training data loader, applied per device.

Default: 1

learning_rate

number

Initial learning rate (after the potential warmup period) to use. For training LoRA, use 0.0005.

Default: 0.000001

learning_rate_te

number

Initial learning rate te (after the potential warmup period) to use. For training LoRA, use 0.0005

Default: 0.000001

lr_scheduler

string

The scheduler type to use. For training LoRA, use constant

Default: "cosine"

seed

integer

Your seed for training script.

Default: 1337

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

npx create-replicate --model=zenaivn/dreambooth-training

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

const output = await replicate.run(
  "zenaivn/dreambooth-training:ea941161e7c9d4c8f787b667740b4d6c0804ade957d87259d429a45bdc9713b3",
  {
    input: {
      butt: false,
      seed: 1337,
      breast: true,
      weight: false,
      identifier: "ohwx",
      train_lora: false,
      num_repeats: "1",
      lr_scheduler: "cosine",
      learning_rate: 0.000001,
      learning_rate_te: 0.000001,
      train_batch_size: 1,
      train_dreambooth: true,
      using_crop_images: true,
      train_lora_sliders: false
    }
  }
);

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

output = replicate.run(
    "zenaivn/dreambooth-training:ea941161e7c9d4c8f787b667740b4d6c0804ade957d87259d429a45bdc9713b3",
    input={
        "butt": False,
        "seed": 1337,
        "breast": True,
        "weight": False,
        "identifier": "ohwx",
        "train_lora": False,
        "num_repeats": "1",
        "lr_scheduler": "cosine",
        "learning_rate": 0.000001,
        "learning_rate_te": 0.000001,
        "train_batch_size": 1,
        "train_dreambooth": True,
        "using_crop_images": True,
        "train_lora_sliders": False
    }
)

# To access the file URL:
print(output.url())
#=> "http://example.com"

# To write the file to disk:
with open("my-image.png", "wb") as file:
    file.write(output.read())

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 zenaivn/dreambooth-training 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": "zenaivn/dreambooth-training:ea941161e7c9d4c8f787b667740b4d6c0804ade957d87259d429a45bdc9713b3",
    "input": {
      "butt": false,
      "seed": 1337,
      "breast": true,
      "weight": false,
      "identifier": "ohwx",
      "train_lora": false,
      "num_repeats": "1",
      "lr_scheduler": "cosine",
      "learning_rate": 0.000001,
      "learning_rate_te": 0.000001,
      "train_batch_size": 1,
      "train_dreambooth": true,
      "using_crop_images": true,
      "train_lora_sliders": false
    }
  }' \
  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

This model doesn't have a readme.