The world of generative models has advanced significantly, and with that comes the powerful technique known as Stable Diffusion. In this blog, we will explore how to implement Stable Diffusion using PyTorch, guiding you step-by-step through the setup, training, and inference processes.

Getting Started

To jumpstart your journey, here’s what you’ll be tackling with the Stable Diffusion implementation:

• Training and Inference on Unconditional Latent Diffusion Models
• Training a Class Conditional Latent Diffusion Model
• Training a Text Conditioned Latent Diffusion Model
• Training a Semantic Mask Conditioned Latent Diffusion Model
• Combining the above models for customized applications

Setup Requirements

Let’s prepare the environment for the implementation:

# Create and activate a new conda environment
conda create -n stable_diffusion python=3.8
conda activate stable_diffusion

# Clone the repository
git clone https://github.com/explainingai-code/StableDiffusion-PyTorch.git
cd StableDiffusion-PyTorch

# Install required dependencies
pip install -r requirements.txt

# Download necessary weights
wget https://github.com/richzhang/PerceptualSimilarity/blob/master/lpips/weights/v0.1/vgg.pth -O models/weights/v0.1/vgg.pth

Data Preparation

You’ll need to prepare your datasets to train Stable Diffusion. Here’s a guide on setting up the MNIST and CelebHQ datasets:

MNIST Dataset

Follow these steps to set up the MNIST dataset:

• Clone the repository from here
• Ensure the directory structure is as follows:

StableDiffusion-PyTorch
|-- data
|   |-- mnist
|       |-- train
|           |-- images
|               |-- *.png
|       |-- test
|           |-- images
|               |-- *.png

CelebHQ Dataset

For the CelebHQ dataset, the setup varies based on the conditions you want to utilize:

• Unconditional: Simply download images from CelebMASK HQ and prepare the directory accordingly.
• Mask Conditional: Follow additional setup to create mask images. You’ll run the command:

python -m utils.create_celeb_mask

• Text Conditional: Download captions from this repository and adjust your directory structure accordingly.

Training the Model

Once everything is set up, you’ll want to train your model. Here’s a brief overview of training steps:

Training Scripts

• To train the autoencoder:

python -m tools.train_vqvae --config configmnist.yaml

• For unconditional LDM:

python -m tools.train_ddpm_vqvae --config configmnist.yaml

• For conditional training, modify your dataset class and then:

python -m tools.train_ddpm_cond --config configmnist_class_cond.yaml

Troubleshooting Common Issues

If you encounter issues during setup or training, consider the following troubleshooting tips:

• Ensure that your conda environment is activated before running any scripts.
• Check your directory structure to confirm that it matches the expected configuration.
• Install any missing dependencies by referring back to the repository’s README.
• If you run into errors related to missing weights, ensure that the weights are correctly downloaded and placed.

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Conclusion

Implementing Stable Diffusion in PyTorch may seem daunting, but with structured guidance, it’s achievable. This technique offers remarkable applications in generating and conditioning images. 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.