What Exactly Are Diffusion Models?
Diffusion models are a type of foundation model that can generate new data based on the data they were trained on. Imagine adding a layer of static or distortion to a TV screen, and then the model learns how to remove that static to restore the original image. This is the magic of diffusion models.
They add Gaussian noise, or random pixels, to an image and then learn to remove this added noise, gradually reducing the noise level until it produces a clear and high-quality image.
This process results in a wide variety of possible outputs, making diffusion models a powerful tool for creating diverse and stable images.
The Technicalities Behind Diffusion Models
To fully grasp how diffusion models work, we need to understand a few technical terms.
Generative models are a type of AI that can learn how to create new content that looks or sounds like something it has seen before from training data.
Diffusion models are a relatively new addition to the field of generative models, which also include generative adversarial networks (GANs), variational auto encoders (VAEs), and transformer-based large language models (LLMs).
Computer vision is a field of artificial intelligence that focuses on enabling computers to “see” and understand images and videos in the same way that humans do. However, diffusion models don’t use computer vision in the same way as GANs and VAEs do.
Instead, they rely on a different technique called score-based generative modeling.
Score-based Generative Modeling
In score-based generative modeling, the diffusion model is trained to measure how likely a new image is to be generated from existing data. This is called a score function.
By training and sampling algorithms from this function, the model can generate new images that look similar to the existing data. This method is considered more stable than other techniques.
Latent space is a mathematical space that represents abstract features of data. In the case of generative models, latent space is where the model learns to map existing data to new, similar data. It’s a virtual space where similar images or text are grouped together by the diffusion model, based on their shared features.
Gaussian noise is a type of random noise that is often added to the input data of a diffusion model. This is done to help the diffusion model learn to generate new data that is similar to the training data, even when the input is not perfect.
Reverse Diffusion Process
In the context of diffusion models, the reverse process refers to the ability of the diffusion model to take a noisy or degraded image and “clean it up” to create a high-quality image. This is done by running the image through the diffusion process in reverse, which removes the added noise.
The Unique Characteristics of Diffusion Models
What sets diffusion models apart from their predecessors is their ability to generate highly realistic images that match the same data point distribution of real images better than GANs.
This means that the images they produce are more varied and stable, and less likely to look the same. Diffusion models can be conditioned on a wide variety of inputs, such astext, bounding boxes, masked images, and lower-resolution images.
They can also be used for super-resolution and class-conditional image generation.
Notable Examples of Diffusion Models
DALL-E 2, a creation of OpenAI, is a large language model that generates images from textual inputs. It has been making headlines for its ability to generate unique images from just a few words of input. DALL-E 2 uses a process called text-to-image synthesis, which allows it to generate images with intricate details. It has massive potential, especially in industries like advertising and entertainment, where the model can be used to generate personalized art and images.
Stable diffusion is another AI model that can generate high-quality images from textual descriptions. It works by taking a starting image and gradually refining it until it matches the given description.
Stable Diffusion allows you to control the level of detail and complexity in the generated images by adjusting a number of parameters. This diffusion model has many potential applications, including creating personalized art, generating realistic and cheaper-to-produce images for video games and movies, and even helping scientists in visualizing complex data.
Practical Applications of Diffusion Models
Inpainting is an advanced image editing technique that allows users to modify specific areas of an image by replacing them with new content generated by a diffusion model. For example, you can use inpainting to modify a family portrait from an old wedding by replacing certain parts of the image with new content generated by the diffusion model.
Outpainting allows users to expand their creative possibilities by adding visual elements beyond the original borders of an image. It works by starting with a real-world or generated image and extending it until it becomes a larger, coherent scene.
The image diffusion model has been used to generate high-quality images, and now this technology is being extended to video generation. AI systems that use diffusion models to generate short video clips based on text prompts have recently been announced by Meta and Google.
If you’re looking for a collection of generated images and their associated prompts, you might find a diffusion model image curation site like Lexica.art helpful. These sites have millions of images indexed and provide highly curated collections.
Diffusion models have revolutionized the field of generative modeling by providing powerful tools for image synthesis and noise reduction. They are capable of generating very diverse and stable images that are similar but slightly different from the original data distribution. As more businesses discover the power of diffusion models to help solve their problems, it is likely that new types of careers will emerge, one of them being “Prompt Engineers.”
So, there you have it! A comprehensive guide to understanding diffusion models in machine learning. Remember, the world of AI is constantly evolving, and diffusion models are just the tip of the iceberg. Stay tuned for more exciting developments in this field!