anotherjesse/dreambooth-hf | Run with an API on Replicate

anotherjesse

dreambooth-hf

Public

18 runs

A100 (80GB)

Run with an API

Pricing

Playground API Examples README Versions

Input

pretrained_model

string

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Model identifier from huggingface.co/models

Default: "runwayml/stable-diffusion-v1-5"

revision

string

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Revision of pretrained model identifier from huggingface.co/models

Default: "fp16"

instance_prompt

*string

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The prompt you use to describe your training images, in the format: `a [identifier] [class noun]`, where the `[identifier]` should be a rare token. Relatively short sequences with 1-3 letters work the best (e.g. `sks`, `xjy`). `[class noun]` is a coarse class descriptor of the subject (e.g. cat, dog, watch, etc.). For example, your `instance_prompt` can be: `a sks dog`, or with some extra description `a photo of a sks dog`. The trained model will learn to bind a unique identifier with your specific subject in the `instance_data`.

class_prompt

*string

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The prompt or description of the coarse class of your training images, in the format of `a [class noun]`, optionally with some extra description. `class_prompt` is used to alleviate overfitting to your customised images (the trained model should still keep the learnt prior so that it can still generate different dogs when the `[identifier]` is not in the prompt). Corresponding to the examples of the `instant_prompt` above, the `class_prompt` can be `a dog` or `a photo of a dog`.

instance_data

*file

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.

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

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The prompt used to generate sample outputs to save.

save_sample_negative_prompt

string

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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

ckpt_base

file

A ckpt file with existing training base

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

npx create-replicate --model=anotherjesse/dreambooth-hf

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

const output = await replicate.run(
  "anotherjesse/dreambooth-hf:de0866d111b584905eacdf883b470a5ccffdc0d07701cca147d193334762adbe",
  {
    input: {
      seed: 1337,
      revision: "fp16",
      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,
      pretrained_model: "runwayml/stable-diffusion-v1-5",
      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 anotherjesse/dreambooth-hf using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "anotherjesse/dreambooth-hf:de0866d111b584905eacdf883b470a5ccffdc0d07701cca147d193334762adbe",
    input={
        "seed": 1337,
        "revision": "fp16",
        "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,
        "pretrained_model": "runwayml/stable-diffusion-v1-5",
        "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:
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 anotherjesse/dreambooth-hf 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": "anotherjesse/dreambooth-hf:de0866d111b584905eacdf883b470a5ccffdc0d07701cca147d193334762adbe",
    "input": {
      "seed": 1337,
      "revision": "fp16",
      "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,
      "pretrained_model": "runwayml/stable-diffusion-v1-5",
      "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 A100 (80GB) 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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