What is the Forward Process Stable Diffusion?

Introduction

Have you ever wondered how AI can create stunning images from scratch? That’s where Stable Diffusion comes in! It’s a fascinating concept in machine learning and generative AI, falling under the umbrella of generative models.

In this article, we’ll dive into the magic behind Stable Diffusion. We’ll explore its theoretical foundations, practical implementation, and some of its exciting applications. So, whether you’re a seasoned AI enthusiast or just curious about how machines can craft art, stick around! This is going to be a fun and enlightening journey.

Overview

• Stable Diffusion is a generative AI technique that creates images by systematically adding and then reversing noise.
• The diffusion model involves a forward process that converts an image into noise and a reverse process that reconstructs the image from the noise.
• The forward process progressively adds Gaussian noise to an image, eventually transforming it into pure noise.
• A linear schedule for noise addition can be inefficient, so a more effective cosine schedule must be developed.
• The forward process in Stable Diffusion is essential for applications like image generation, inpainting, super-resolution imaging, and data augmentation.
• Key considerations for implementing the forward process include choosing the appropriate noise schedule, ensuring computational efficiency, and maintaining numerical stability.

What are Diffusion Models?

The idea of the diffusion model is not that old. In the 2015 paper called “Deep Unsupervised Learning using Nonequilibrium Thermodynamics”, the Authors described it like this:

The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data.

Here, the diffusion process is split into forward and reverse diffusion processes. The forward diffusion process turns an image into noise, and the reverse diffusion process is supposed to turn that noise into the image again. 

Forward process in diffusion models

In forward diffusion, we take an image with a non-random distribution. We do not know the distribution, but our goal is to destroy it by adding noise to it. At the end of the process, we should have noise that is similar to pure noise.

Let’s look into an example, we will take the below image

Our goal is to destroy the above image’s distribution so that it becomes pure noise like below.

Step-by-step Forward Process

Here is the forward process:

• Step 1: Take the image and generate some noise. 
• Step 2: Add that noise to the image to destroy the distribution using a linear scheduler. 

• Step 3: These steps are repeated according to the linear scheduler until the image is destroyed and looks like pure noise. 

The below image represents noise being added t+1 times. 

After iterating through our steps 11 times, we get a completely destroyed image. 

Also read: Mastering Diffusion Models: A Guide to Image Generation with Stable Diffusion

Mathematical Formulation 

Let x0​ represent the initial data (e.g., an image). The forward process generates a series of noisy versions of this data x₁,x₂,…,x_T​ through the following iterative equation:

Here,q is our forward process, and x_t is the output of the forward pass at step t. N is a normal distribution, 1-_tx_t-1 is our mean, and tI defines variance.    

Schedule:

t refers to the schedule, and its values range from 0 to 1. The value of t is usually kept low to avoid variance from exploding. The paper from 2020 uses a linear schedule; hence, the output looks like the below:

The images above show us the forward diffusion process using a linear schedule with 1000 time steps.

In this case, 𝛽𝑡 ranges from 0.0001 to 0.02 for the mean and variance behaves as shown below.

Later, in 2021, researchers from OpenAI decided that using a linear schedule is not that efficient. As we have seen before, most of the information from the original image is lost after around half of the total steps. They designed their own schedule and called it the cosine schedule. The improvement in the schedule allowed them to reduce the number of steps to 50.

Latent samples from linear (top) and cosine (bottom)

schedules respectively at linearly spaced values of t from 0 to T

Also read: Stable Diffusion AI has Taken the World By Storm

Complete Forward Process

It can be described as:

Where q(x1:T∣x0) represents the joint distribution of the noisy data over all time steps. With that equation, we can calculate noise at any arbitrary step t without going through the process.

Properties of the Forward Process

• Markov Property: Each step in the forward process only depends on the previous step, making it a Markov chain.
• Progressive Noise Addition: The variance schedule 𝛽𝑡 typically increases with 𝑡, ensuring that the data gradually becomes more noisy.
• Gaussian Convergence: After a sufficient number of steps, the data distribution converges to a Gaussian distribution, facilitating the reverse diffusion process.

Applications of the Forward Process

Here are the applications:

• Image Generation: Enables the creation of new, high-quality images from noise, used in art and content creation.
• Image Inpainting: Fills in missing or corrupted parts of images, useful in photo restoration and object removal.
• Super-Resolution Imaging: Enhances the resolution of low-quality images for applications in medical imaging and satellite imagery.
• Data Augmentation: Generates new training samples with controlled noise to improve machine learning model robustness and performance.

Practical Considerations for Forward Process

When implementing the forward process in practice, several considerations must be addressed:

• Choice of Noise Schedule: Different noise schedules can be experimented with to find the one that provides the best performance for a given application.
• Computational Efficiency: The forward process involves multiple iterations, so computational efficiency is crucial. Techniques such as parallel processing and optimized algorithms can be employed.
• Numerical Stability: Care must be taken to ensure numerical stability, particularly when dealing with very small or very large values of 𝛽𝑡.​

Conclusion

In Stable Diffusion, the forward process is a painstakingly crafted technique that applies progressive noise addition to convert data into a Gaussian noise distribution. Understanding this procedure is essential to using diffusion models for creative endeavors. The forward stable diffusion process creates the foundation for efficient and reliable data production, opening up a world of machine learning and artificial intelligence possibilities. It does this by meticulously adjusting the noise schedule and guaranteeing computing efficiency.

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Badrinarayan M

Data science Trainee at Analytics Vidhya, specializing in ML, DL and Gen AI. Dedicated to sharing insights through articles on these subjects. Eager to learn and contribute to the field's advancements. Passionate about leveraging data to solve complex problems and drive innovation.