Types of Generative AI Models to Leverage into Your AI Project

• AI/ML
• September 15, 2025

by Kumarpal Nagar

Generative AI is a type of AI that creates new, original content, such as text, images, code, or music, whereas Generative AI models are the type of ML models that power its functions. There are mainly three types of generative AI models that enterprises use in their AI stacks: GAN, Diffusion, and Transformers. This blog covers everything you need to know about the types of Gen AI Models, with a strategy to follow to choose the right one.

Last week, a business manager from a well-known brand approached us and said he spent $200K on what his previous vendor called “advanced generative AI,” only to discover a chatbot with a fancy interface. He’s not alone; there are many as well.

The thing is, Generative AI development services have become a catch-all buzzword across industries. Every company claims that its tool uses “cutting-edge generative models,” but most business leaders fail to differentiate between actual AI innovation vs. repackaged technology from 2019.

What makes this worse? Everyone is talking about it, but nobody mentions the difference between enterprise-ready vs. theoretical generative AI models.

Through this blog, we’ll break down the most important generative AI models relevant to businesses and guide them to make smart investments.

Key Takeaways

• The three core Generative AI models you need to know: GANs, Diffusion Models, and Transformer-Based Models.
• A breakdown of how each model works, their architecture, and generative AI use cases they serve with real-world examples.
• A practical guide on how to select the right Gen AI model based on your business needs.
• Generative AI market size is forecasted to reach $283.37 billion by 2034 at a CAGR of 34.6%.
• Gartner says more than 80% of enterprises are planning to use GenAI APIs or deploy unique GenAI-powered applications by 2026.
• NVIDIA uses GANs for photorealistic virtual environments.
• Today’s most popular tools, like ChatGPT, Gemini, Claude, and more, get the power to generate text and code with transformer-based models.

What is Generative AI Models?

A generative AI model is a mathematical architecture based on neural networks, trained on massive datasets to learn probability distributions and generate new content while understanding context.

Call it the engine or computational brain; it powers popular Generative AI applications like ChatGPT, DALL-E, and many others, not the user interface you interact with.

Learn how to use generative AI in application development.

Types of Generative AI Models (with Real-World Examples)

There are mainly three types of generative AI models, including GAN, diffusion, and transformer models. Apart from these, you’ll also find models like variational autoencoders, autoregressive models, and flow-based models.

Let’s learn about core types of Gen AI models:

#1. Generative Adversarial Network (GAN)

Generative Adversarial Networks (GANs) are a type of machine learning solution that generates data, such as text, images, audio, video, and code.

As a part of its architecture, it uses two neural networks, Generator and Discriminator, and these both compete with each other with specific motives.

• Generator provides answers to queries that look like their training data and tries to fool the discriminator.
• Discriminator evaluates data and decides if it’s real or fake.

This adversarial “game” forces the generator to produce increasingly realistic outputs, allowing GANs to create new, high-quality synthetic data.

Through each repeated competition, the generator gets better at producing realistic outputs.

Common Applications/Use Cases of GANs

• Image generation
• Image editing
• Text, video, and audio generation
• Data augmentation
• Synthetic medical imaging generation

Learn about how you can use this model of generative AI in healthcare workflows.

Strengths of GANs

• Excellent at generating highly realistic images
• Effective with limited training data
• Fast generation once trained
• Good for creating variations of existing content

Weaknesses of GANs

• Difficult to train (unstable, mode collapse issues)
• Requires careful hyperparameter tuning
• Difficult to control specific attributes in generated content

Real-World Examples of Tools using GANs

• NVIDIA StyleGAN: Powers realistic face generation
• Super-Resolution GANs: Used in photo editing software
• DeepArt: Creates artistic style transfers
• This Person Does Not Exist: Demonstrates photorealistic face generation
• Adobe’s GAN-based features: In Photoshop for content-aware fill

#2. Diffusion Models

Diffusion models are a type of deep learning solution that generates data, such as images, by learning to reverse a noise-adding process.

These models are built on a U-Net neural network architecture that flows with:

• Forward Process: Gradually adds noise to training images until they become pure random noise
• Reverse Process: Learns to remove noise step by step, recreating original images.
• Conditioning: Uses text embeddings or other inputs to guide the generation process.

It starts with pure noise in a series of steps (the forward process) until only random noise remains. Leveraging the reverse process, a neural network learns to remove this noise step-by-step. Then, it transforms random noise back to a realistic data sample. Iteratively, it removes noise guided by learned patterns and test prompts to produce high-quality images that match the input description.

Common Applications/Use Cases of Diffusion Models

• Text-to-image generation
• Image editing
• Medical imaging (denoising scans, generating training data)
• Video and 3D object generation

Strengths of Diffusion Models

• Superior image quality compared to GANs
• Stable training process
• Excellent text-to-image capabilities
• Fine control over generated content
• Can handle complex, detailed prompts

Weaknesses of Diffusion Models

• Slower generation (requires multiple denoising steps)
• Higher computational requirements
• Large model sizes
• Can struggle with precise text rendering in images

Real-World Examples of Tools using Diffusion Models

• DALL-E 2/3: OpenAI’s text-to-image generator
• Stable Diffusion: Open-source image generation platform
• Midjourney: Popular AI art creation tool
• Adobe Firefly: Integrated into Adobe Creative Cloud (Read more about it in the guide named: AI in UI/UX design)
• Canva AI: Consumer-friendly design tool integration
• Imagen (Google): High-quality research-level diffusion model.

#3. Transformer-Based Models

Transformers are a powerful deep learning model architecture that understands the sequential data (text) well. They do that through a mechanism called “Attention,” which determines the relationships and relevance between different parts of the input.

This helps them learn the context and meaning within data to transform an input sequence into an output one. They’re the foundation of today’s large language models and multimodal systems.

The key components of its architecture includes:

• Encoder-Decoder Structure: Processes input and generates output sequences
• Multi-Head Attention: Simultaneously focuses on different types of relationships
• Position Encoding: Understands word order and sequence relationships
• Feed-Forward Networks: Processes the attended information

The working of transformers starts with input processing, which converts text into numerical representations (tokens). After that, attention calculation is done, which determines which parts of the input are most relevant to each other.

Next, context-building execution is done, which creates a rich understanding of meaning and relationships. Post all, it predicts the most likely next token based on context. Further, it goes into iterations and continues to generate tokens by token until completion.

Common Applications/Use Cases of Transformer-Based Models

• Text generation (articles, code, chatbots)
• Multimodal AI (text-to-image, text-to-video, text-to-audio)
• Machine translation
• Enterprise use cases: document summarization, knowledge management, AI copilots

Strengths of Transformer-Based Models

• Exceptional language understanding and generation
• Handles long-form content effectively
• Versatile across multiple domains
• Can be fine-tuned for specific use cases
• Processes entire sequences simultaneously (parallel processing)

Weaknesses of Transformer-Based Models

• Massive computational requirements
• Expensive to train and run
• Can generate plausible but incorrect information (hallucinations can occur). (RAG as a Service can help.)
• Limited by training data cutoff dates
• Context window limitations for very long documents

Real-World Examples of Tools using Transformer-Based Models

• GPT-4/ChatGPT: OpenAI’s flagship language model
• Claude: Anthropic’s constitutional AI assistant
• Gemini: Google’s multimodal AI system
• GitHub Copilot: AI-powered code completion
• Grammarly: AI writing assistance
• Jasper: Marketing content generation
• Copy.ai: Business copywriting automation

Apart from these three main, there are two other GenAI model types as well that are worth knowing. Let’s know them:

• Variational Autoencoder (VAE): This generative AI model encodes data into a compact form, such as a latent space, then reconstructs it with slight variations to create new, modified outputs. It can be useful in image reconstruction and drug discovery.
• Autoregressive Models: These are also known as sequence-based AI models, which generate content by predicting one element at a time in sequence (similar to predicting the next word based on previous words). It forms the foundation of modern language and vision models. Transformer-based models are actually a type of it. Therefore, most modern transformer models are actually autoregressive.
• Flow-based Models: These types of gen AI models use reversible mathematical transformations to convert simple data (like noise) into complex outputs. Although these offer precise control over generation, they require significant computational resources. Hence, these can be used in scientific simulations and density estimation.

With these, don’t forget to learn about building AI models that can power your Gen AI solutions.

How to Select the Right Generative AI Model Type?

The process to select the right Generative AI model type includes defining your specific use case, evaluating models, assessing data privacy and security requirements specifically, and testing the model for final confirmation.

Let’s know how these steps are executed:

STEP 1: Specify the primary use case for your generative AI project, such as content creation, coding, design, or customer support.

STEP 2: Determine the output type, from text, images, audio code, or multimodal options.

STEP 3: Set clear performance goals, such as high accuracy, real-time low latency, consistent results, or detailed outputs. Rate its performance based on business metrics like cost per generation vs. manual creation cost, time savings, and volume scalability.

STEP 4: Consider the model characteristics your generative AI solution should have by analyzing model size vs. business needs, performance & accuracy, and latency.

STEP 5: Determine your industry requirements for data privacy and security, as well as compliance with specific industry standards like GDPR, HIPAA, SOX, PCI-DSS, etc.

STEP 6: Decide between open-source vs. proprietary models. Open-source allows full control and customization support with no ongoing license costs, whereas proprietary offers robust support, ease of integration, and faster time-to-market.

You can even think about AI as a Service to speed up processes to launch your Gen AI solution.

STEP 7: Ensure the model allows customization and fine-tuning to match your domain-specific knowledge and output requirements.

STEP 8: Test and evaluate with actual data and prompts to see which one produces the most relevant, detailed, and accurate results.

STEP 9: Refine the selection based on the testing results, implement and integrate it with your application, and provide ongoing support.

Build Future-Ready Generative AI Applications with MindInventory

Well, when building a generative AI application, your task doesn’t end at selecting the right model. You also have to configure, train, and fine-tune it to meet your business demands. This also adds up to costs and associates certain risks. Without the right strategy and AI engineers to hire, many businesses face roadblocks. You can avoid that by selecting MindInventory because we can help to:

• Evaluate the best-fit generative model for your specific business case.
• Integrate and fine-tune it with domain-specific data for higher accuracy and reliability.
• Deploy scalable, compliant, and secure solutions integrated into your ecosystem.

So, whether you’re building a chatbot like nAI, creating synthetic datasets, or designing creative data pipelines, we make sure that our provided AI solution works best for your business.

FAQs About Generative AI Models