Tutorial on Diffusion Models for Imaging and Vision (2403.18103v3)

Abstract: The astonishing growth of generative tools in recent years has empowered many exciting applications in text-to-image generation and text-to-video generation. The underlying principle behind these generative tools is the concept of diffusion, a particular sampling mechanism that has overcome some shortcomings that were deemed difficult in the previous approaches. The goal of this tutorial is to discuss the essential ideas underlying the diffusion models. The target audience of this tutorial includes undergraduate and graduate students who are interested in doing research on diffusion models or applying these models to solve other problems.

• The paper introduces diffusion models that gradually add and remove noise to generate high-quality images.
• It compares the iterative denoising process with conventional VAE methods, highlighting improvements in image fidelity and control.
• Empirical results demonstrate practical benefits for synthetic data generation and advanced image restoration.

Tutorial on Diffusion Models for Imaging and Vision

Introduction to Diffusion Models

Diffusion models have emerged as a significant advancement in the arena of generative models, particularly in their application to imaging and vision tasks such as text-to-image synthesis. Centered around the concept of gradually adding and then removing noise, these models have provided a novel framework for understanding and enhancing generative tasks. This tutorial outlines the essential principles of diffusion models, with an emphasis on their application in generating high-quality images from latent codes, and compares their functionality with that of traditional Variational Auto-Encoders (VAEs).

The Central Theme of Diffusion Models

Diffusion models operate on the principle of initially introducing noise to an image systematically and then learning to reverse this process. This contrasts with traditional autoencoders where the objective is to map an image to a latent space and then reconstruct it. The process of adding noise in a controlled manner, followed by learning to denoise, allows for a more nuanced approach to data generation, enabling the production of images that are both diverse and detailed.

Variational Auto-Encoders (VAE) as a Prelude

Before exploring diffusion models, it's beneficial to understand the operational framework of VAEs as they establish a foundational understanding of moving between an image and a latent space representation. VAEs, characterized by their encoder-decoder architecture, seek to encode an image into a latent representation and subsequently decode it back to image space, albeit with a probabilistic twist. This probabilistic approach is what differentiates VAEs from traditional autoencoders, laying groundwork for advanced generative models including diffusion models.

Transition to Diffusion Models

Transitioning from VAEs to diffusion models necessitates an understanding of how diffusion models encapsulate the process of noise addition and subtraction more effectively. Unlike VAEs, which directly encode and decode between image and latent space, diffusion models introduce noise in a step-wise fashion, with each step carefully controlled, followed by a learned process of noise removal that attempts to reconstruct the original image. This step-by-step process, although computationally intensive, allows for a more controlled generation process, ultimately leading to higher-quality image synthesis.

Numerical Results and Model Comparisons

Empirical validations demonstrate that diffusion models, owing to their iterative refinement process, can produce images that outperform those generated by VAEs in terms of quality and fidelity. The incremental noise addition and removal process employed by diffusion models afford a finer control over the generation process, leading to images that are not only visually appealing but also demonstrate a higher degree of detail and realism.

Practical Implications and Future Directions

The advent of diffusion models presents a shift in how generative tasks are approached, with potential implications across a range of applications from synthetic data generation for training models to advanced image editing and restoration techniques. Future developments may focus on optimizing the computational efficiency of these models, making them more accessible for real-time applications, and exploring their utility beyond image generation to tasks such as video synthesis and three-dimensional modeling.

Conclusion

Diffusion models represent a significant leap forward in the domain of generative models. By leveraging a controlled process of noise addition and removal, they have opened new avenues for generating high-quality images and have set a new benchmark for generative tasks in imaging and vision. As research in this field progresses, it is anticipated that diffusion models will find broader applications, driving innovation in both theoretical and practical aspects of generative modeling.