https://www.techtarget.com/searchenterpriseai/tip/Generative-models-VAEs-GANs-diffusion-transformers-NeRFs
Generative models -- a type of machine learning model that can create new content modeled on training data -- are at the center of AI innovation today. They're the technology that makes generative AI and agentic AI possible.
Generative model design and implementation approaches vary. Depending on considerations such as use case or training data availability, one type of generative model could be better suited than another.
There are five main types of generative models in widespread use today: variational autoencoder (VAEs), generative adversarial networks (GANs), diffusion models, transformers and neural radiance fields (NeRFs). To choose between the generative model options, organizations must explore how each works, along with their strengths, limitations and use cases.
Generative models identify patterns within training data and use those patterns to generate new data through a process known as inference. The new data is similar in form and content to the training data, but it isn't identical.
For example, consider a model trained by parsing thousands of cat images to understand the shapes and colors of cats. If a user asks the model to create a new cat image, the model can build an image based on these visual attributes, but the image wouldn't be identical to any specific cat picture from the model's training data.
Although all generative models use patterns they've identified in training data to generate novel content, how they process and use training data varies.
Here's a look at five common types of generative models:
VAEs work using the following process:
A key advantage of VAEs is that they use a quantitative approach to managing uncertainty. They use probability distributions and comparison scores to determine the likelihood of relationships existing between two or more data points. This is valuable in situations where training data doesn't fully represent the data that a model is asked to generate or evaluate. VAEs fill in the gaps between training data and inference input by using probability to guess what the values should be.
For this reason, VAEs are especially useful in situations where training data is limited or low quality. VAEs tend to perform better with poor training data than other types of generative models. They're also useful in situations where the attributes of each data sample tend to vary significantly, such as when processing medical images or analyzing the chemical structures of drug molecules.
VAEs' probability-centered strategy means they can overlook fine details. They're less adept than some other types of models, such as GANs, at identifying situations where training data is a perfect match for the new data that a user wants the model to generate.
For instance, when asked to complete the sentence "In 1492, Columbus sailed the ocean [blank]," a VAE might be less likely to answer "blue" because it's more susceptible to considering a range of possible responses, rather than recognizing that "blue" virtually always completes this sentence.
VAEs are best in the following scenarios:
GANs rely on a training process that pits two functions against each other as adversaries. The process works as follows:
When training starts, the data the generator creates is essentially random. However, over time, the discriminator recognizes instances where the generator's data resembles the training data. By repeating this process, GAN models gradually learn how to produce new data that closely matches training data.
Like VAEs, GANs also compare generated data to training data. However, whereas a VAE uses probabilistic mapping to predict the similarity between training data and generated data, a GAN iteratively produces generated data that becomes indistinguishable from training data over time. This helps ensure that the new data accurately reflects the training data.
One key advantage of GANs is their accuracy. If the adversarial process continues long enough, GANs learn nuanced details about training data, which they use to create highly realistic content. For example, GAN-produced images tend to be sharper and more detailed than those generated by VAEs.
GANs also can generate new content faster. This is because they don't have to perform a probabilistic assessment when generating new data, so inference requires less computation.
GANs typically require more training time because the adversarial process requires additional iterations to produce a reliable and accurate model. It also uses more computational resources during training, which can make GAN training time-consuming and expensive.
GANs also aren't adept at handling training data sets with a large number of outliers. They typically ignore outliers because the generator never produces output that's similar enough to the outlying data points to receive a high score from the discriminator.
Consider a GAN model for the following scenarios:
Diffusion models work as follows:
Historically, diffusion models were primarily used for generating images, videos and audio files. Over the past few years, researchers have begun exploring diffusion as an option for text analysis and generation as well.
Diffusion models produce highly accurate visual and audio content -- even more accurate than the content generated by GANs in some cases. They're also simpler to train than GANs because they're less prone to risks, such as outlying data points that can contribute to model collapse.
A drawback of diffusion models is that the noising and denoising processes require significant computing power. As a result, training these models can be costly and time-consuming.
In addition, despite their high level of accuracy, diffusion models are more prone to issues such as overlooking details when generating new content. For example, they might add extra fingers when creating an image of a person. This is mainly because the noising-denoising process is less adept at reliably capturing nuances.
The primary remedy for this is to ensure that training data is sufficiently large and diverse to represent all essential details required for reliable content generation. Diffusion models will typically overlook details that aren't frequently represented in the training data.
Diffusion models work best in the following scenarios:
Transformer models are the primary type of generative model behind most of prominent generative AI products, like OpenAI's ChatGPT and Google's Gemini. Transformers use the following attention mechanism process that analyzes training data:
To generate output, transformer models use the results of the attention process to determine which tokens should follow other tokens. This is how they construct novel text.
The standout advantage of transformers compared to other generative models is that they excel at interpreting context and identifying long-range relationships, making connections between data points that might not be otherwise obvious.
Transformers do this by comparing tokens within different samples from the training data and analyzing how token relationships vary. This is why, for example, a transformer model could understand that the word Washington within a sentence that also includes revolutionary war is likely to have an entirely different meaning from the same word in a sentence that also includes Seattle.
While transformer models are powerful, they require large data sets to train effectively. They also have high computational requirements during both training and content generation. Finally, they have a low degree of model explainability, meaning it can be challenging to determine which training data resulted in which output.
Transformer models are ideal in the following cases:
NeRFs are primarily designed for generating 3D videos from still images. They work as follows:
By iterating through this process, NeRFs produce 3D content that accurately reflects the content of still images.
Although other types of models, like diffusion models, can generate 3D content based on still images, NeRFs are able to do this using comparatively little training data. They also don't require training data that includes 3D content; instead, they're able to construct 3D content by guessing which rays to include based on the content of 2D images.
NeRFs require substantial computational resources, and the process of generating new content is often slow. This makes NeRFs poorly suited for use cases that require real-time content generation, such as creating dynamic scenery for a 3D game or virtual world. That said, NeRFs work well in situations where 3D content can be produced ahead of time, such as when generating graphics that can be loaded into a video game, rather than being generated dynamically at runtime.
Consider a NeRF for the following scenarios:
To choose the right generative model for a project, consider the following:
Table 1: Comparison chart of different generative models
Chris Tozzi is a freelance writer, research adviser and professor of IT and society. He has previously worked as a journalist and Linux systems administrator.
19 Nov 2025