Denoising Diffusion Probabilistic Models Code | DDPM Pytorch Implementation

Summary

This video tutorial delves into the implementation of diffusion models, specifically DDPM, with a focus on training and sampling. It covers the mathematical foundations of diffusion processes, the architecture of the latest diffusion models, and the creation of a noise scheduler. The tutorial also details the construction of the model, including the use of sinusoidal position embeddings and the encoder-decoder architecture with residual blocks and self-attention. Practical aspects such as dataset preparation, training loop, and sampling method are discussed, with examples from training on MNIST and texture image datasets.

Takeaways

• 📊 The video covers the implementation of diffusion models, starting with DDPM and moving towards Stable Diffusion with text prompts.
• 🔍 The architecture used in the latest diffusion models is implemented, rather than the original one used in DDPM.
• 🧩 The video dives into the different blocks of the model architecture before coding, focusing on the training and sampling parts of DDPM.
• 🖼️ The diffusion model is trained on grayscale and RGB images, with the specific math of diffusion models covered as a refresher.
• ⏱️ The diffusion process involves a forward process that adds Gaussian noise to an image step by step, eventually making it equivalent to a sample of noise from a normal distribution.
• 🔄 The reverse diffusion process requires the model to learn to predict the mean and variance of the noise, aiming to minimize the KL Divergence between the ground truth and the model's prediction.
• 🎯 The training method involves sampling an image at a time step T, a noise sample, and feeding the model the noisy version of the image to learn the reverse process.
• 🛠️ The noise scheduler is implemented to handle the forward process of adding noise and the reverse process of sampling from a learned distribution.
• 🏗️ The model architecture for diffusion models is detailed, with a focus on the requirements for the input and output shape and the necessity of incorporating time step information.
• 🔧 The video concludes with an overview of the training and sampling code, showcasing the results of training the diffusion model on MNIST and texture images.

Q & A

What is the main focus of the video?

The video focuses on the implementation of diffusion models, specifically creating a Denoising Diffusion Probabilistic Model (DDPM) and discussing its training and sampling process.

What are the key components of a diffusion model covered in the video?

The video covers the architecture used in the latest diffusion models, the specific math required for implementation, the forward and reverse processes, and the training method involving sampling and loss computation.

How is the noise schedule implemented in the video?

The noise schedule is implemented using a linear noise schedule where beta linearly scales from 1 to 0.02 over a thousand time steps, and the alphas (1 - beta) and cumulative product terms are pre-computed for efficiency.

What is the role of the reverse process in diffusion models as explained in the video?

The reverse process in diffusion models is about learning to predict the original noise from a noisy image by minimizing the KL Divergence between the ground truth distribution and the model's predicted distribution.

How is the time step information incorporated into the model in the video?

Time step information is incorporated by using a time embedding block that converts integer time steps into a vector representation, which is then fused into the model via a linear layer after activation.

What architecture is used for the model in the video?

The model uses a U-Net architecture with downsampling blocks, mid blocks, and upsampling blocks, each containing ResNet blocks, self-attention blocks, and time step projection layers.

What is the significance of the sinusoidal position embedding in the video?

The sinusoidal position embedding is used to convert integer time steps into a fixed embedding space, which aids the model in predicting the original noise based on the current time step.

How is the training process of the DDPM described in the video?

The training process involves sampling an image at a random time step, adding noise based on the noise schedule, and then training the model to predict the original noise, using the mean squared error as the loss function.

What datasets are used for training the model as mentioned in the video?

The model is trained on the MNIST dataset for grayscale images and a dataset of texture images for RGB images.

How does the video demonstrate the sampling process from the learned model?

The video demonstrates the sampling process by starting with a noise sample and iteratively applying the reverse process using the model's noise predictions to gradually refine the image towards the original.

What are the computational requirements mentioned for training on larger images in the video?

The video mentions that training on larger images requires more patience and computational resources, suggesting the need for increased channels and longer training epochs for better results.