Decoding The Different Types of Generative AI Models

Updated on Jan 10th, 2024

Decoding The Different Types of Generative AI Models

You must have started using ChatGPT for personal or official purposes. There’s no way you haven’t introduced yourself to this generative AI marvel. But do you know that it’s just one subset of this giant subset of AI? Yes, ChatGPT could just be a drop in the ocean. It could be the beginning of the new AI era.  

We’re saying this because there are many types of generative AI. While one is used for text generation, the other can help generate images or even music. This means from generating captivating images that can breathe life into your marketing materials to composing harmonious melodies that elevate your multimedia projects; generative AI has something unique to offer every facet of your business. It can even delve into video production, design, and data augmentation, making it a versatile and invaluable tool in your technological arsenal. 

But before you get all excited to jump right in, understanding the different types of generative AI models is important. Doing this will give you an idea of what you have to innovate with.  Plus, learning about the options will also let you know which category of generative AI can take your business to the next level.  

  • Generative AI models are those parts of the AI family that are trained to mimic patterns and structures in data, crafting original content. They use deep learning models like GANs and VAEs. 
  • Frameworks like TensorFlow, PyTorch, Keras, and several other tools and components support the implementation of different types of generative AI models. 
  • These models have paved the way for various platforms like ChatGPT, Midjourney, Google Bard, etc.  
  • Your business can integrate any of the different types of generative AI models with an expert development like Matellio. We can make your journey of adding AI-based services a breeze.  

Benefits of Using Generative AI Models 

Enhanced CreativityEnhanced Creativity

Exploring different types of generative AI fuels creativity by offering novel solutions for content generation and design. 

Diverse ApplicationsDiverse Applications

Understanding generative AI types expands your toolkit for various tasks, from text and images to music and videos. 

Competitive AdvantageCompetitive Advantage

 Leveraging generative AI can provide a competitive edge through improved content quality and efficiency. 

Time and Cost SavingsTime and Cost Savings

Automation and AI-driven content generation save time and resources in the long run. 

Innovation CatalystInnovation Catalyst

Generative AI sparks innovation, helping businesses stay at the forefront of technological advancements. 

Customized SolutionsCustomized Solutions

Tailor generative AI to your specific needs, ensuring it aligns perfectly with your business goals. 

Data AugmentationData Augmentation

Generative AI can generate synthetic data for machine learning, enhancing model training and accuracy. 

Efficient WorkflowsEfficient Workflows

Streamline creative processes and workflows, reducing manual effort and boosting productivity. 


Generative AI solutions can scale with your business, accommodating growing demands effortlessly. 

Improved User ExperienceImproved User Experience

Deliver captivating content, enhancing the user experience and engagement levels. 

Businesses Are Already Using Generative AI for the Following
Source: Salesforce Insights

Types of Generative AI: An Overview

Now that you know the numerous benefits and competition of using generative AI for your enterprise, it’s time to understand the working of these. So, let’s quickly begin from the beginning.  

Generative AI models are like creative AI wizards. The secret behind their magic? Well, they’re trained in the art of mimicry. 

These clever models take a deep dive into the data your chosen AI development company trains them on. Then, they spot all the patterns and structures hidden in the data. Finally, armed with their newfound knowledge, they start crafting original content from scratch.  

You now be wondering about the tools that work behind this. And here you have it! The deep learning models like Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). These are among the popular ones that we will discuss in the next section. 

In a GAN, you’ve got two key players: the discriminator and the generator. These two play the characters of Tom and Jerry. While the discriminator tries to distinguish the real data from fake, the generator’s mission is to generate content that’s so realistic even the discriminator is fooled. 

Imagine this: You select generative AI development services. Your chosen experts train a generative AI model with a bunch of landscape photos, and voila! It starts either distinguishing or generating stunning new landscapes that look like they came straight from a travel pamphlet. Or, if it’s text-based, it can generate perfectly structured paragraphs, all because of the text it’s read and learned from. 

In a nutshell, the different types of generative AI models are your creative sidekicks, capable of imitating the style and essence of the data they’ve been trained on. Thus, they can be your pretty magical allies for all sorts of creative projects. 

Also Read- Top 10 ChatGPT Use Cases for Businesses in 2023 and Beyond

What Are the Different Types of Generative AI Models? 

Now, it’s time to answer the big question! In this section, you will learn about the most prominent types of generative AI models out there. Remember, this knowledge will help you take the right steps in advancing your business with AI and other digital transformation services. That said, some of the most prominent generative AI models are as follows: 


Meet Generative Adversarial Networks (GANs), the spectacles shaking up the AI world. You’ve already read about the tussle between the “generator” and the “discriminator.” That pretty much sums up the working of GANs.  

The generator’s aim is to get so good at its craft that the discriminator can’t tell its creations apart from reality. Meanwhile, the discriminator sharpens its skills, becoming a better detective with each round. The result? GANs churn out astonishingly lifelike content. That’s why this is among the types of generative AI model waves in fields like image creation, art, and video production. 

You can implement GAN architecture easily using the following tools and libraries:  

FrameworkKey OfferingsKey Feature
TensorFlowAn open-source machine learning framework developed by Google. It offers tools and libraries for GAN implementation, including tf.keras.layers for quick GAN model creation.GAN layer for easy model building
PyTorchAn open-source machine learning framework developed by Facebook. It provides tools and libraries for GANs, utilizing the torch.nn.Module class for custom GAN model development.Flexibility for custom model creation
KerasAn open-source deep learning library with a high-level API. It includes a GAN class for straightforward GAN model construction and training.High-level API for rapid model creation
ChainerAn open-source deep-learning framework developed by Preferred Networks. It offers tools like chainer.links.model.Generator and chainer.links.model.Discriminator for custom GAN models.Specific classes for GAN model creation
GANLabA web-based tool for interactive GAN experimentation. It provides a user-friendly, code-free environment for building and training GANs.Visual, drag-and-drop interface

Real- World Applications of GANs 

  • Image Generation 
  • Style Transfer 
  • Deepfake Generation 
  • Super-Resolution 
  • Image-to-Image Translation 
  • Data Augmentation 
  • Anomaly Detection 
  • Text-to-Image Synthesis 


Variational Autoencoders (VAEs) are among the most sought-after types of generative AI today. They have transformed how multimedia and branding is functioning in today’s time. 

They basically come into the picture when you want to encode data into a secret “latent space.” The model can then decode whenever a related query hits. And finally, it executes what was requested. 

The best part? VAEs don’t perform the decoding at just one time. They can be easily trained according to the probability. Hence, they can continuously summon new samples based on what they’ve learned. 

Thus, one can say VAEs are digital artists. So, if you want to launch a platform that generates images from scratch, VAEs have got your back! What’s more? They’re not limited to images. They can also dab text and audio, making them versatile performers. 

So, if you are looking for AI that can craft new content and bring a touch of creativity to your projects, VAEs must be among the top types of generative AI models you shortlist. 

Real-World Application of VAEs 

  • Image generation and manipulation 
  • Anomaly detection 
  • Recommendation systems 
  • Medical image analysis (such as MRI and X-ray image reconstruction), disease diagnosis, and drug discovery. 
  • NLP tasks like text generation, dialogue generation, and language translation. 
  • Speech Synthesis 
  • Sensor data processing in autonomous vehicles 

Transformer Based-Models 

We all have heard about the GPT series. And we all know it’s going to keep advancing. As a decision-maker for your business, you must  

These are among those types of generative AI models that can take NLP and innovative tasks by storm. How? They’ve got this trick called “attention.” It allows them to comprehend how different parts are connected to a particular text.  

But wait for the real kicker. Transformers can “remember” huge datasets from multiple sources online. They are super-efficient and can handle really long chunks of information, which means they become pros at creating coherent and contextually spot-on text.  

So, if you want your business to speak the language of innovation and clarity, these Transformer-based models might just be the magic wand you’ve been searching for. 

Key technologies and components involved in making Transformers-based generative AI models work: 

Attention MechanismsTransformers utilize attention mechanisms to weigh the importance of different parts of the input sequence when processing tokens.
Multi-Head Self-AttentionMulti-head self-attention allows the model to focus on different aspects of the input simultaneously, capturing diverse dependencies.
Feedforward Neural NetworksFeedforward neural networks process the output of the attention mechanisms, applying non-linear transformations to the input data.
Deep Learning FrameworkDeep learning frameworks like TensorFlow, PyTorch, or Hugging Face Transformers library provide the infrastructure for building and training Transformer-based models.
Pretrained ModelsPretrained language models (e.g., BERT, GPT, T5) serve as starting points for fine-tuning specific tasks, saving training time and resources.
GPUs/TPUsGraphics Processing Units (GPUs) and Tensor Processing Units (TPUs) accelerate model training, making it feasible to train large Transformers models on vast datasets.
Model DeploymentTechnologies for deploying models in production, such as serving APIs (e.g., TensorFlow Serving, Flask), cloud services (AWS, GCP, Azure), or edge computing solutions.

Revolutionize your business with generative AI models

Real-World Applications of Transformers 

  •  Language translation 
  • Text summarization 
  • Chatbots and virtual assistants 
  • Sentiment analysis 
  • Question-answering systems 
  • Recommendation Systems 
  • Financial risk assessment, fraud detection, and predicting financial markets 
  • Enhance video game experiences 
  • Improve e-commerce search algorithms, product recommendations, and chatbots for customer support 
  • Legal research and document review 
  • Robot perception, object manipulation, and path planning 


Imagine you’re reading a mystery novel, and you’re trying to guess what’s going to happen next based on the clues from the past chapters. That’s exactly what RNNs or Recurrent Neural Networks do in the tech world. They predict the next element in a sequence by looking at what came before. It’s like solving a mystery but with numbers and data. 

However, there is a slight twist. Undoubtedly, RNNs are among the types of generative AI that are good at playing detective. But they can sometimes lose track of the plot when the story gets really long. It’s like forgetting crucial details in a complex story. 

Still, there are some savvy cousins in the AI family called Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). They’re like experienced detectives who never miss a beat and can handle those extra-long stories without losing their way. Thus, whether you want RNNs for custom enterprise solutions or a brand-new AI-based venture, experts can deal with this type of generative AI to your advantage. Below given is a glimpse of how they’ll do it.  

Deep Learning FrameworkTensorFlow, PyTorch, or Keras provide the infrastructure for building and training RNN-based generative models.
LSTM/GRU CellsLong Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells are variants of RNNs with improved memory and gradient flow properties, often used for better performance.
Sequence PaddingPadding sequences to a common length is important to handle variable-length input data and enable efficient batch processing.
MXNetAn open-source deep learning framework known for its efficiency and scalability. MXNet supports RNNs and provides a user-friendly interface for model development.
CaffeCaffe supports RNNs and is optimized for computer vision tasks but can be used for other applications as well.

Real-World Applications of RNNs 

  • Voice Assistants and text-to-speech synthesis 
  • Predictive text and smart keyboards 
  • Autonomous vehicles and self-driving cars 
  • Language modeling and text generation 
  • Stock market prediction 
  • Human activity recognition for healthcare and sports 
  • Music composition 
  • Customer behavior analysis 

Style Transfer Models 

Such types of generative AI models work their magic by transforming images or videos, giving them entirely new styles. That’s not it. While doing so, such models preserve the original content. How? Well, these models employ advanced neural networks and techniques to separate content and style. Imagine merging the content of one image with the artistic style of another, creating visually striking and unique outputs. Charming, right?  

Popular in digital art, visual effects, photo editing, and video post-production, they add a creative flair to your visuals. As they continue to evolve, they provide businesses with the flexibility to generate personalized and expressive visual content that sets them apart in the digital landscape. So, if you have a visuals-intensive business, Style Transfer Models should be on your priority list while you plan to accelerate your enterprise. Here are the technologies and components involved in typical style transfer models.  

Deep Learning FrameworkTensorFlow, PyTorch, or Keras to build, train, for essential tools and APIs
Convolutional Neural Networks (CNNs)To extract feature representations from input images and perform convolutions on image data.
Pretrained ModelsVGG-19 or ResNet have already learned rich feature representations from a large dataset. Fine-tune these models for style transfer tasks.
Hyperparameter TuningLearning rates, number of iterations, and layer weights in the loss function to achieve the desired style transfer results.
Loss FunctionsDefine custom loss functions, such as content loss and style loss, to measure the differences between the generated image and the style and content images.
GPU/TPUGraphics Processing Units (GPUs) or Tensor Processing Units (TPUs) to accelerate model training and inference, particularly important for real-time processing.

Real-World Applications of Style Transfer Models 

  • Artistic filters and effects in photography apps 
  • Museum art restoration 
  • Video game graphics 
  • Content creation and digital art 
  • Fashion and textile design 
  • Virtual Reality (VR) and Augmented Reality (AR) 
  • Movie and video production 
  • Educational tools and interactive learning 
  • Automated art generation 
  • Advertising and marketing 

Examples of Different Types of Generative AI Models 

Examples of Different Types of Generative AI Models

Also Read- Generative AI for Businesses: Explore Use Cases, Industries and Strategies

Simplify Integration of Different Types of Generative AI Models with Matellio 

In today’s fast-paced AI landscape, having the right tools is essential to stay ahead of the game. With Matellio by your side, you will experience the most seamless generative AI integration. Our professionals have the expertise and knowledge to dive even into the most complicated intricacies of a project. And that’s not it. We can cater this expertise to multiple industries in several ways.  

Build Smarter Software with Our AI Integration Expertise

Even if you do not know where to start your AI advancement, our team can guide you with the best possible journey for your business. It’s because cracking the integration process is not our only forte. Our experts can give your applications the power to think, create, and adapt in a customized way that fits best to your business needs. Wait, there’s more! We can also continuously monitor and upgrade your tools to keep them relevant and relatable for your users.

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