zeke/dreambooth – Run with an API on Replicate

zeke / dreambooth

A copy of replicate/dreambooth for testing

Cold

Public
1 run
T4

Run with an API

Playground API Examples README Versions

Input

instance_prompt

*string

Shift + Return to add a new line

The prompt with identifier specifying the instance

class_prompt

*string

Shift + Return to add a new line

The prompt to specify images in the same class as provided instance images.

instance_data

*file

A ZIP file containing the training data of instance images

class_data

file

A ZIP file containing the training data of class images. Images will be generated if you do not provide.

num_class_images

integer

Minimal class images for prior preservation loss. If not enough images are provided in class_data, additional images will be sampled with class_prompt.

Default: 50

save_sample_prompt

string

Shift + Return to add a new line

The prompt used to generate sample outputs to save.

save_sample_negative_prompt

string

Shift + Return to add a new line

The negative prompt used to generate sample outputs to save.

n_save_sample

integer

The number of samples to save.

Default: 4

save_guidance_scale

number

CFG for save sample.

Default: 7.5

save_infer_steps

integer

The number of inference steps for save sample.

Default: 50

pad_tokens

boolean

Flag to pad tokens to length 77.

Default: false

with_prior_preservation

boolean

Flag to add prior preservation loss.

Default: true

prior_loss_weight

number

Weight of prior preservation loss.

Default: 1

seed

integer

A seed for reproducible training

Default: 1337

resolution

integer

The resolution for input images. All the images in the train/validation dataset will be resized to this resolution.

Default: 512

center_crop

boolean

Whether to center crop images before resizing to resolution

Default: false

train_text_encoder

boolean

Whether to train the text encoder

Default: true

train_batch_size

integer

Batch size (per device) for the training dataloader.

Default: 1

sample_batch_size

integer

Batch size (per device) for sampling images.

Default: 4

num_train_epochs

integer

Default: 1

max_train_steps

integer

Total number of training steps to perform. If provided, overrides num_train_epochs.

Default: 2000

gradient_accumulation_steps

integer

Number of updates steps to accumulate before performing a backward/update pass.

Default: 1

gradient_checkpointing

boolean

Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.

Default: false

learning_rate

number

Initial learning rate (after the potential warmup period) to use.

Default: 0.000001

scale_lr

boolean

Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.

Default: false

lr_scheduler

string

The scheduler type to use

Default: "constant"

lr_warmup_steps

integer

Number of steps for the warmup in the lr scheduler.

Default: 0

use_8bit_adam

boolean

Whether or not to use 8-bit Adam from bitsandbytes.

Default: false

adam_beta1

number

The beta1 parameter for the Adam optimizer.

Default: 0.9

adam_beta2

number

The beta2 parameter for the Adam optimizer.

Default: 0.999

adam_weight_decay

number

Weight decay to use

Default: 0.01

adam_epsilon

number

Epsilon value for the Adam optimizer

Default: 1e-8

max_grad_norm

number

Max gradient norm.

Default: 1

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

npx create-replicate --model=zeke/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";

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

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

const output = await replicate.run(
  "zeke/dreambooth:151fe0593a5abe96a753d41b64b92a19ff45fd391455e3022a2f9de0a98671b1",
  {
    input: {
      seed: 1337,
      scale_lr: false,
      adam_beta1: 0.9,
      adam_beta2: 0.999,
      pad_tokens: false,
      resolution: 512,
      center_crop: false,
      adam_epsilon: 1e-8,
      lr_scheduler: "constant",
      learning_rate: 0.000001,
      max_grad_norm: 1,
      n_save_sample: 4,
      use_8bit_adam: false,
      lr_warmup_steps: 0,
      max_train_steps: 2000,
      num_class_images: 50,
      num_train_epochs: 1,
      save_infer_steps: 50,
      train_batch_size: 1,
      adam_weight_decay: 0.01,
      prior_loss_weight: 1,
      sample_batch_size: 4,
      train_text_encoder: true,
      save_guidance_scale: 7.5,
      gradient_checkpointing: false,
      with_prior_preservation: true,
      gradient_accumulation_steps: 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 zeke/dreambooth using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "zeke/dreambooth:151fe0593a5abe96a753d41b64b92a19ff45fd391455e3022a2f9de0a98671b1",
    input={
        "seed": 1337,
        "scale_lr": False,
        "adam_beta1": 0.9,
        "adam_beta2": 0.999,
        "pad_tokens": False,
        "resolution": 512,
        "center_crop": False,
        "adam_epsilon": 1e-8,
        "lr_scheduler": "constant",
        "learning_rate": 0.000001,
        "max_grad_norm": 1,
        "n_save_sample": 4,
        "use_8bit_adam": False,
        "lr_warmup_steps": 0,
        "max_train_steps": 2000,
        "num_class_images": 50,
        "num_train_epochs": 1,
        "save_infer_steps": 50,
        "train_batch_size": 1,
        "adam_weight_decay": 0.01,
        "prior_loss_weight": 1,
        "sample_batch_size": 4,
        "train_text_encoder": True,
        "save_guidance_scale": 7.5,
        "gradient_checkpointing": False,
        "with_prior_preservation": True,
        "gradient_accumulation_steps": 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 zeke/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": "zeke/dreambooth:151fe0593a5abe96a753d41b64b92a19ff45fd391455e3022a2f9de0a98671b1",
    "input": {
      "seed": 1337,
      "scale_lr": false,
      "adam_beta1": 0.9,
      "adam_beta2": 0.999,
      "pad_tokens": false,
      "resolution": 512,
      "center_crop": false,
      "adam_epsilon": 1e-8,
      "lr_scheduler": "constant",
      "learning_rate": 0.000001,
      "max_grad_norm": 1,
      "n_save_sample": 4,
      "use_8bit_adam": false,
      "lr_warmup_steps": 0,
      "max_train_steps": 2000,
      "num_class_images": 50,
      "num_train_epochs": 1,
      "save_infer_steps": 50,
      "train_batch_size": 1,
      "adam_weight_decay": 0.01,
      "prior_loss_weight": 1,
      "sample_batch_size": 4,
      "train_text_encoder": true,
      "save_guidance_scale": 7.5,
      "gradient_checkpointing": false,
      "with_prior_preservation": true,
      "gradient_accumulation_steps": 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 T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

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Model description

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Intended use

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Ethical considerations

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Caveats and recommendations

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