What Are Diffusion Models in Generative AI?

Generative AI has been the talk of the town for many years, and the research and advancements in this domain have made it even more exciting. With new papers coming up every now and then, generative AI is a technology that no one wants to miss out on. Though there are different types of models that enable generative AI capabilities, many researchers and companies favor diffusion models. 

In this article, we will explore everything about diffusion models in generative AI and try to understand the key components that make a diffusion model useful for everyone. But first, let’s start by understanding generative AI. 

What is Generative AI? 

Generative AI is an AI technology that can be used to produce high-quality image, video, audio, and text content based on the type of data they were trained on. The spark difference about this technology is that it comes up with something new and unique every time, even though it is trained on some data. It uses its training data to understand how a process can be done, and then it uses its intellect to generate new content, images, or other graphics based on the input received.  

Having known about generative AI now is the perfect time to understand its importance. 

Importance of Generative AI 

Data Generation   

Generative AI can be highly useful in generating data for testing other machine learning models or even testing processes. You can train it on sample data, and it will provide you with unique and unbiased data that you can use to train newer machine learning models or run your application tests without worrying about compromising data security.  

Content Creation  

Generative AI models like chatGPT, Bard, etc are highly proficient in creating unique content, and they are important too. With such models on the rise, content creation tasks like writing emails, notices, or articles will become quite easy, and anyone with access to this can create content for their tasks without depending on experts. Using AI increases productivity and saves a lot of time for everyone involved. According to the statistics, 85.1% of AI users are using AI tools for content creation.

Improving human-machine Interactions  

Today businesses are utilizing chatbots for improving customer experience, and generative AI can do a lot here. It can help chatbots and virtual assistants with its intelligence and create better and more human responses. Moreover, its ability to stick to a context and carry conversations along the context also allows in improving human-machine interactions significantly.  

Personalization 

We all know that AI helps in personalization, but generative AI takes it a step further. It can create personalized experiences for users by crafting unique messages based on user data or providing recommendations that get picked at first sight.  

After understanding generative AI and its importance, now is the time to move to the main parts of this article. Let’s start by understanding what diffusion models are. 

What are Diffusion Models? 

Diffusion models are a type of generative AI model that is capable of generating data similar to the ones on which they were trained. They usually destroy their training data once they have learned and try to reconstruct the training data in order to validate their learnings.  

The destruction of training data in diffusion models is done automatically by increasing the Gaussian noise in the data till the data becomes completely unrecognizable. 

When talking about diffusion models, it is imperative to understand how they differ from other generative AI models, and that’s what the next section is about.  

How are Diffusion Models different from other generative AI models? 

Diffusion models are quite different from Generative Adversarial Networks and Variable Auto Encoders machine learning models and below are some points that prove the difference between these models.  

1. Probabilistic Modeling 

Diffusion models directly model the conditional probability distribution of the next data point based on the previous data points in the sequence. This is different from the GANs, which use a discriminator to distinguish between real and generated samples.  

2. Training Process 

The training process between diffusion models and GANs is also different. Diffusion models are trained using the maximum likelihood estimation where the model is trained to maximize the likelihood of generating training data. On the other hand, GAN models are trained using a min-max game between the generator and discriminators. 

3. Sample Quality 

Diffusion models are known for generating high-quality samples, especially in image generation tasks, where they can produce realistic and diverse images. GANs also produce high-quality samples, but they are prone to mode collapse and can produce less diverse outputs. 

The above-discussed differences make diffusion models unique, and they also point toward how diffusion models work. So, let’s understand that in the upcoming section.  

How Does a Diffusion Model Work?

Generative AI Diffusion models work in a different way than other generative AI models. They first add noise to the dataset, and then remove the noise from the dataset till they achieve the proper output. Below is the detailed process of diffusion models. 

Data Preprocessing 

Data preprocessing is the first step in every machine learning project and diffusion models aren’t exceptions. In this stage, the input data needs to be formatted and validated so that model training is also accurate. During the preprocessing stage, data cleaning is done to remove outliers, data normalization is done to bring data at the same scale, and data augmentation is done to increase diversity among training data.  

If the data preprocessing step is done correctly, you will have a better dataset to work with, and with a better dataset the model will provide awesome results, and there will be minimal problems of underfitting or overfitting the model. 

Adding Noise 

The next step in the working of a diffusion model is to add noise to data. Here the initial sample is passed through a series of reversible and incremental modifications where each step introduces some complexity to the data sample. This process gradually adds Gaussian noise to the sample which aids in generating diverse and realistic samples by the time the process ends.  

Reverse Diffusion Process 

After the model has added noise to the sample data, the reverse diffusion process comes into the picture. This is the primary process that differentiates diffusion models from other models. In this process, the noise patterns introduced at each stage are recognized, and the denoising of data takes place. Here the model uses its knowledge acquired during gradual addition of the noise in previous steps to reconstruct the correct output by removing noise.  

Having known about how a diffusion model works, and how it produces its output, it is also important to know the key components of the diffusion model that enable the model to produce such outputs.  

Key Components of a Diffusion Model 

The key components of AI diffusion models are below:

1. Sequential Generation 

Diffusion models generate data sequentially, one element at a time, conditioning each new element on the previously generated elements. This sequential generation process allows for the generation of complex, high-dimensional data such as images and audio. 

2. Noise Levels 

Diffusion models use a concept called "noise levels" to control the amount of noise added to the data at each step of generation. The noise levels start high and decrease over time, allowing the model to gradually refine the generated data and improve its quality. 

 3. Diffusion Process 

The diffusion process refers to the process of gradually adding noise to the data at each step of generation. This process is guided by the noise levels and helps the model learn the underlying distribution of the data. 

4. Reverse Process 

Once the model has generated some data points, it performs a reverse process wherein it denoises the data and removes previously added noise to it. This is usually done during the training to compare the generated data with the supplied data so that the model can be tweaked or penalized if the deviation is higher. 

5. Loss Function  

Diffusion models are usually trained with a loss function. The loss function penalizes the model for generating data that deviates a lot from the training data and pushes it to learn more and better from the underlying data. It also keeps the model’s output in check and improves the output incrementally.  

6. Architecture 

Diffusion models can be implemented using multiple different neural network diffusion architectures like recurrent neural networks and convolutional neural networks. Based on the type of data you want to generate you should choose convolutional neural networks for image data and RNNs for text data.  

By now, we know a lot of the functions and components of a diffusion model, so it is quite helpful to understand the applications of diffusion models, and how they help us.  

Applications of Diffusion Model 

Text To Video 

Diffusion models are also used when you need to convert text to videos. First, you need to represent the text and video data in a way that the diffusion model can understand. The model will then start with the text and replace video frames one by one by matching the movements in videos to text. At the end of this, you get a video that matches your text description. 

This approach of diffusion models is often used in adding captions to videos, creating animated stories or generating visuals based on text stories and certain video frames.  

Image Search 

Diffusion models offer amazing results when they are utilized to search images from an array of images. The first step in using diffusion models for searching images is to encode the images before using them.  

During the reverse diffusion process, these models map each image to a point in the distribution and also discard noise particles that aren’t related to the image. This helps them to find images faster and more effectively. The similarity between images can be measured using Euclidean distance and the images with the highest similarity numbers can be returned as query responses.  

Image to Image 

Diffusion models offer an effective way for image-to-image transformations. Suppose you want to change a black-and-white image to a color image; it can be done easily using any diffusion model. 

To achieve this task, you start with a black-and-white image, and the model proceeds by gradually adding colors and details to match the new image. After the model completes its forward diffusion process, you’ll get a new image which will be later converted to a simpler form while completing the transformation process.  

While there are many other applications of diffusion models, they also offer significant advantages. In the upcoming section, let’s have a look at some of its advantages and disadvantages.  

Advantages of Diffusion Models 

Handle Missing Data 

Diffusion models are great if you have some missing data points. They can handle missing data during the generation stage and generate coherent data points even when you are missing some portion of your input data.  

Robust To Overfitting 

GANs are prone to overfitting where the model relies so much on the training data that it cannot work well with unknown data. On the contrary, diffusion models are quite robust to overfitting due to the different training processes they use.  

Image Quality  

Diffusion models are quite powerful when it comes to generating pictures that look real and highly detailed. They are able to do this by understanding how things actually look before generating images. Due to such an approach, images generated from diffusion models don’t have any weird things in them, and they are quite true to life. 

Privacy-Based Data Generation 

Diffusion models are good for situations where you need to keep data private. Because these models use reversible changes, they can create new data that looks real without revealing the original private information. 

Disadvantages of Diffusion Models 

Mode Collapse

Like other generative models, diffusion models are susceptible to mode collapse, where the model generates only a limited set of samples, failing to capture the full diversity of the underlying data distribution. 

Data Dependency 

Diffusion models require large amounts of training data to learn effectively, and the quality of the generated samples is highly dependent on the quality and diversity of the training data. 

At this point, you are a pro at diffusion models, and you should use this new knowledge to take informed decisions when building generative AI models. Compared to other generative AI models, diffusion models provide better image quality, and they are less prone to overfitting too. Moreover, they can be used across domains like image-to-image transformations, text to video, image search etc., so you should definitely use a diffusion model if you are building any feature that requires image generation or searching capabilities.  

Also, read: Unlocking Business Value: A Guide to Large Language Models (LLMs)