Dream Booth: How to Bring Your Ideas to Life with Text-to-Image Generation

Harnessing DreamBooth: A New Era of Personalized Text-to-Image Models

Table of Contents

  1. Introduction
  2. DreamBooth Demystified
  3. Setting Up the Environment
  4. Fine-Tuning the Model
  5. Running Inference
  6. Conclusion

Personalizing Text-to-Image Models with DreamBooth

Welcome to our tutorial on DreamBooth, a method that allows you to personalize text-to-image models with just a few images of a subject. In this guide, we’ll be using the CompVis/stable-diffusion-v1-4 model to generate images of famous people like Albert Einstein and Marilyn Monroe in various scenes and poses.

What is DreamBooth?

DreamBooth is a tool that allows us to train a model to generate images based on text prompts. Given a few images of a subject, it can generate contextualized images of that subject in different scenes, poses, and views.

Requirements and Setting Up the Environment

Before we dive into the code, let’s make sure we have everything we need:

  1. Python: I recommend using Python 3.10.10. You can download it from the official website.

  2. Git: Git is a version control system that we’ll use to clone the diffusers library. You can download it from the official website.

  3. Conda or any Python environment of your choice: We’ll be using a Python environment to manage our dependencies. If you’re using Conda, you can create a new environment with Python 3.7 using the following command:

conda create -n "DreamBooth" python=3.10.10
# Then, activate the environment:
conda activate DreamBooth
# Working Conda ENV:
(DreamBooth) PS E:DreamBoothdiffusers>
  1. Clone the diffusers library and install dependencies: With Git installed, you can clone the diffusers library and install the necessary dependencies:
(DreamBooth) PS E:DreamBoothdiffusers> git clone https://github.com/huggingface/diffusers.git
(DreamBooth) PS E:DreamBoothdiffusers> cd diffusers
(DreamBooth) PS E:DreamBoothdiffusers> pip install -r examples/dreambooth/requirements.txt --user
  1. Download xFormers: While not a requirement for training, we recommend installing xFormers as it can make your training faster and less memory intensive. You can install it using pip:
(DreamBooth) PS E:DreamBoothdiffusers> pip install xformers
  1. Hugging Face Accelerate: We’ll be using Hugging Face’s Accelerate library to simplify the usage of hardware accelerators. You can initialize an Accelerate environment with the following command:
(DreamBooth) PS E:DreamBoothdiffusers> accelerate config
# Your answers may vary depending on your system configuration. I am running a System with a single GPU and CPU so I selected the following options:
-------------------------------------------------------------------------------------------------------------------------------In which compute environment are you running?
This machine
-------------------------------------------------------------------------------------------------------------------------------Which type of machine are you using?
No distributed training
Do you want to run your training on CPU only (even if a GPU / Apple Silicon device is available)? [yes/NO]:NO
Do you wish to optimize your script with torch dynamo?[yes/NO]:NO
Do you want to use DeepSpeed? [yes/NO]: NO
What GPU(s) (by id) should be used for training on this machine as a comma-seperated list? [all]:all
-------------------------------------------------------------------------------------------------------------------------------Do you wish to use FP16 or BF16 (mixed precision)?
accelerate configuration saved at C:Users\nick/.cachehuggingfaceacceleratedefault_config.yaml


Fine-tuning: The process of taking a pre-trained model and adjusting it to better suit a specific task.

Text-to-Image Diffusion Model: A type of AI model that generates images based on text prompts.

Unique Identifier: A specific term or phrase used to refer to a particular subject in the text prompts.

Class-Specific Prior Preservation Loss: A loss function used during training to encourage the model to generate diverse instances of the subject’s class.

Semantic Prior: The model’s existing knowledge or understanding of a particular class.

Super-Resolution Components: Parts of the model responsible for generating high-resolution images.

Inference: The process of using a trained model to make predictions.

In the next section, we prepare and gather our image data for training!

Preparing the Data

Before we can start fine-tuning the model, we need to prepare our data. For this tutorial, we’ll be using images of Skysrappers and a few well known famous photos to train the model. You can use any images of your choice, just make sure they are clear and high-quality. Once you have your images, you’ll need to upload them to a directory in your local environment. For example, I have created a directory called skyscrappers and another called famous_photos and upload your images there.

(DreamBooth) PS E:AI_WorkspaceDreamBoothdiffusers> mkdir famous_photos
(DreamBooth) PS E:AI_WorkspaceDreamBoothdiffusers> mkdir skyscrappers

Fine-Tuning the Model

Now that we have our data, we can start fine-tuning the model. We’ll be using the CompVis/stable-diffusion-v1-4 model for this tutorial. Here’s how you can fine-tune the model:

# First we Define the model, instance directory, and output directory and store them in variables:
export MODEL_NAME="CompVis/stable-diffusion-v1-4"
export INSTANCE_DIR="./einstein" # replace with your directory
export OUTPUT_DIR="path_to_saved_model"