Exploring Diffusion Models: A New Era in Text-to-Image Generation
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Chapter 1 Introduction to Diffusion Models
For those closely following the latest advancements in computer vision (CV), the remarkable outcomes achieved by generative networks in image creation are nothing short of astonishing. Historically, much of the research centered around the innovative concept of generative adversarial networks (GANs), but recent studies have shifted focus. If you examine the latest papers like ImageN and Stable Diffusion, you will frequently encounter a new term: diffusion probabilistic model.
This article provides a fundamental understanding of this emerging model, a brief overview of its learning process, and the exciting applications that have arisen as a result.
Section 1.1 The Forward Process Explained
To grasp how diffusion models function, consider the process of adding a slight amount of Gaussian noise to an image. Initially, the image remains recognizable, but as you repeatedly add noise, it gradually transforms into nearly pure Gaussian noise. This phase is referred to as the forward process in a diffusion probabilistic model.
The primary aim is straightforward: by utilizing the fact that the forward process operates as a Markov chain (where the current state is independent of the previous one), we can learn to reverse the process, gradually denoising the image at each step.
With a well-learned reverse process and random Gaussian noise, we can repeatedly apply noise and ultimately generate an image that closely resembles the original data distribution used for training — thus forming a generative model.
One significant advantage of diffusion models is their training method, which allows for optimization by selecting a random timestamp rather than requiring a full end-to-end image reconstruction. This approach ensures greater stability during training compared to GANs, where even minor hyperparameter changes can lead to model failure.
Section 1.2 The Evolution of Text-to-Image Generation
The concept of using denoising diffusion models for image generation was first introduced in 2020, but it gained substantial traction with Google’s recent paper on ImageN, which significantly advanced the field. Similar to GANs, diffusion models can be conditioned on various prompts, including text and images. The Google Research Brain Team has highlighted that large, frozen language models serve as excellent encoders for generating photorealistic images.
This video, titled "Ultimate Guide to Diffusion Models | ML Coding Series," provides an in-depth exploration of diffusion models, discussing their architecture and implementation in various applications.
Chapter 2 Transitioning from 2D to 3D
As with many trends in computer vision, the impressive achievements in the two-dimensional domain have sparked aspirations to extend into three-dimensional modeling. Following this trajectory, Poole et al. introduced DreamFusion, a text-to-3D model built upon the robust foundations established by ImageN and NeRF.
To better understand NeRF, please refer to the relevant literature.
Figure 4 illustrates the DreamFusion pipeline. This process initiates with a randomly initialized NeRF. Leveraging the generated density, albedo, and normals (considering a specific light source), the network produces the shading and subsequently the color of the NeRF from a designated camera angle. The rendered image is then combined with Gaussian noise, with the ultimate goal of using a frozen ImageN model to reconstruct and refine the NeRF model.
The second video, "Paper review: Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding," discusses the theoretical underpinnings and applications of diffusion models in achieving realistic image synthesis.
Section 2.1 Achievements in 3D Image Synthesis
Impressive results from DreamFusion are showcased in Figure 5. The gallery exhibits stunning 3D visuals, characterized by consistent colors and shapes, effectively rendered from simple input images. Recent advancements, such as Magic3D, have further optimized the reconstruction process, making it faster and more detailed.
End Note
This overview highlights the evolution of diffusion models in image generation. When abstract concepts are transformed into vivid visuals, it becomes much easier for anyone to envision and articulate their wildest ideas.
"Writing is the painting of the voice." — Voltaire
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