Are you ready to dive into the world of Custom Diffusion? This powerful method enables you to fine-tune text-to-image diffusion models, such as Stable Diffusion, using just a few images. Fear not! This guide will walk you through the process step by step, ensuring you start your journey without a hitch. Let’s embark on this adventure!

What is Custom Diffusion?

Custom Diffusion allows you to adapt existing diffusion models by introducing new concepts, using as few as 4 to 20 images. It’s like teaching an old dog new tricks—by feeding it new images, you can get it to generate fascinating imagery reflecting those concepts!

Getting Started Steps

1. Clone the Necessary Repositories: First, you need to clone the Custom Diffusion GitHub repository and the Stable Diffusion model.
2. Set up the Environment: Use Conda to create the environment and install the required packages.
3. Download the Model Checkpoint: Fetch the stable-diffusion model checkpoint needed for fine-tuning.

Step-by-Step Instructions:

1. Clone the Repositories:

Open your terminal and run:

git clone https://github.com/adobe-research/custom-diffusion.git 
cd custom-diffusion 
git clone https://github.com/CompVis/stable-diffusion.git 
cd stable-diffusion

2. Set Up the Environment:

Next, create the conda environment and install the necessary libraries:

conda env create -f environment.yaml 
conda activate ldm 
pip install clip-retrieval tqdm

3. Download the Model Checkpoint:

Finally, download the Stable Diffusion model checkpoint by running:

wget https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt

Fine-tuning the Model

To fine-tune your model, you’ll need to prepare your images. You can either use real images or generated images as regularization. This means you can teach your model using real-world snippets or synthetically generated visuals!

Single-Concept Fine-tuning:

Using Real Images:

1. Download the dataset:

wget https://www.cs.cmu.edu/~custom-diffusion/assets/data.zip 
    unzip data.zip

2. Run the training script:

bash scripts/finetune_real.sh cat data/cat real_reg_samples_cat cat finetune_addtoken.yaml pretrained-model-path

Multi-Concept Fine-tuning:

This allows even more flexibility, combining two or more concepts in one training session! The command looks something like this:

bash scripts/finetune_joint.sh wooden pot data/wooden_pot real_reg_samples_wooden_pot data/cat real_reg_samples_cat wooden_pot+cat finetune_joint.yaml pretrained-model-path

Results to Expect

Once you’ve trained your models, the results might include unique combinations that reflect the concepts you’ve input. Expect to see creative outputs like “a new cat sculpture in the style of a wooden pot”!

Troubleshooting Tips

Should you encounter issues along the way, here are a few suggestions:

• Training Does Not Start: Ensure your checkpoints are properly linked and your datasets are correctly set up.
• Memory Errors: If the memory is running low, consider resizing your input images or reducing your batch size.
• Unexpected Results: Review images used for training; ensure they are representative of the concepts you want to emulate.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.