The fundamental concept behind diffusion models is that they begin with a noisy image and progressively eliminate the noise until a clear image is obtained. The model is trained on a dataset of images, learning to identify the patterns that constitute an image. Once trained, it can then utilize these patterns to generate new images.

One of the advantages of diffusion models is their ability to create a wide variety of images. For instance, they can generate images of people, animals, landscapes, and even abstract art. Furthermore, they can produce images based on text descriptions.

However, there are also some limitations to diffusion models. For example, they can sometimes produce images that are unrealistic or nonsensical. Additionally, they can be computationally expensive to train and execute.

Overall, diffusion models are powerful tools capable of creating a wide variety of images. However, it's essential to understand their limitations before using them.

Here are some of the key points from the video:

• Diffusion models begin with a noisy image and progressively eliminate the noise until they achieve a clear image.

• The model is trained on a dataset of images and learns to recognize the patterns that compose an image.

• Diffusion models can be employed to generate a diverse array of images, encompassing depictions of people, animals, landscapes, and abstract art.

• Diffusion models can also be utilized to generate images derived from text descriptions.

• Some limitations of diffusion models include their occasional production of unrealistic or nonsensical images, as well as their computationally expensive nature for training and execution.