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Introduction to Diffusion Models

What you will learn from this chapter:

- What are diffusion models and how do they differ from GANs
- Major sub categories of diffusion models
- Use cases of diffusion models
- Drawback in diffusion models

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Diffusion Models and their Difference from GANs

Diffusion models are a new and exciting area in computer vision that has shown impressive results in creating images. These generative models work on two stages, a forward diffusion stage and a reverse diffusion stage: first, they slightly change the input data by adding some noise, and then they try to undo these changes to get back to the original data. This process of making changes and then undoing them helps generate realistic images.

These generative models raised the bar to a new level in the area of generative modeling, particularly referring to models such as Imagen and Latent Diffusion Models(LDMs). For instance consider the below images generated via such models.

GANs were considered by many as state-of-the-art generative models in terms of the quality of the generated samples, before the recent rise of diffusion models. GANs are also known as being difficult to train due to their adversarial objective, and often suffer from mode collapse. Think of different modes as different categories. Consider cat and dog as two seperate mode. If the task of the generator is to produce cat and dog images, if mode collapse occurs it means the generator just produces plausible images of either cat or dog alone. One reason for this to happen is the failure of the discriminator to move from local minima ending up repeatatively classifying only one of the mode(either cat or dog) as fake. In contrast, diffusion models have a stable training process and provide more diversity because they are likelihood-based.

However, diffusion models tend to be computationally intensive and require longer inference times compared to GANs due to the step-by-step reverse process.

In Science, Diffusion is a process where solute particles move from higher concentrated region of solute to lower concentrated region of solute in a solvent. Consider the below diffusion analogy for high-level intuition:

Above is the traditional diffusion process where the drop of ink completely merges after some time when dropped in a clean water glass. Practically reversing this is impossible, i.e., getting the drop from the mixture. But this is what is done in diffusion models, i.e., removing noise and hence producing a clean image.

In diffusion models, Gaussian noise is added step-by-step to the training images to turn them completely into junk noisy images. Through this process, the model learns to remove the noise step-by-step, hence it is capable of turning any Gaussian noisy image into a new diverse image (can also be conditioned based on text prompts).

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Major Variants of Diffusion models

There are 3 major diffusion modelling frameworks:

- Denoising diffusion probabilistic models (DDPMs):
  - DDPMs are models that employ latent variables to estimate the probability distribution. From this point of view, DDPMs can be viewed as a special kind of variational auto-encoders (VAEs), where the forward diffusion stage corresponds to the encoding process inside VAE, while the reverse diffusion stage corresponds to the decoding process.
- Noise conditioned score networks (NCSNs):
  - It is based on training a shared neural network via score matching to estimate the score function (defined as the gradient of the log density) of the perturbed data distribution at different noise levels.
- Stochastic differential equations (SDEs):
  - It represents an alternative way to model diffusion, forming the third subcategory of diffusion models. Modeling diffusion via forward and reverse SDEs leads to efficient generation strategies as well as strong theoretical results. This can be viewed as a generalization over DDPMs and NCSNs.

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Use Cases of Diffusion Models

Diffusion is used in a variety of tasks including, but not limited to:

- Image generation - Generating images based on prompts.
- Image super-resolution - Increasing resolution of images.
- Image inpainting - Filling up a degraded portion of an image based on prompts.
- Image editing - Editing specific/entire part of the image without losing its visual identity.
- Image-to-image translation - This includes changing background, attributes of the location etc.
- Learned Latent representation from diffusion models can also be used for.
  - Image segmentation
  - Classification
  - Anomaly detection

Want to play with diffusion models? No worries, Hugging Face’s Diffusers library comes to rescue. You can use almost all recent diffusion SOTA models for almost any task.

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Drawbacks in Diffusion Models

The most significant disadvantage of diffusion models remains the need to perform multiple steps at inference time to generate only one sample. Latent consistency models(LCMs) is one direction of research proposed to overcome the slow iterative sampling process of Latent Diffusion models (LDMs), enabling fast inference with minimal steps on any pre-trained LDMs (e.g Stable Diffusion). Despite the important amount of research conducted in this direction, GANs are still faster at producing images.

Other issues of diffusion models can be linked to the commonly used strategy to employ CLIP embeddings for text-to-image generation. Few literature studies highlight that their model struggles to render readable text in an image and motivates the behavior by stating that CLIP embeddings do not contain information about spelling.

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