Source: Zhuanzhi
This article is approximately 1500 words long and is recommended for a 5-minute read.
This review provides a comprehensive classification framework, summarizing various forms of control techniques and strategies in diffusion model image synthesis, and explores the practical applications of controllable generation in different scenarios.
Research Background: In recent years, the field of artificial intelligence has experienced rapid development, with generative models making significant progress in various areas such as computer vision, natural language processing, and reinforcement learning. Traditional methods like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Normalizing Flows have long dominated, but the recent rise of Diffusion Models has triggered a paradigm shift in generative modeling. Diffusion models consist of three key components: the forward process transforms data distributions into random noise; the reverse process uses learnable neural networks to gradually estimate the transformation kernel to reverse the forward process; and the sampling process generates data from random noise using the optimized network. Despite advantages in theoretical foundations, training stability, and simplicity of loss functions, diffusion models typically require more sampling time and are challenging to control and guide during the generation process. To address these challenges, researchers have proposed various solutions, including improved Ordinary Differential Equation (ODE) or Stochastic Differential Equation (SDE) solvers, model distillation techniques to accelerate sampling, and guiding mechanisms to correct the direction of unconditional generation based on conditions. These conditions can encompass various forms, such as images, text, or 2D poses.
Research Objectives: Although several review articles have explored various aspects of diffusion models, there remains a gap in comprehensive reviews of controllable generation. This review aims to fill that gap by providing a comprehensive classification framework, summarizing various control techniques and strategies in diffusion model image synthesis, and exploring the practical applications of controllable generation in different scenarios. We hope to provide valuable insights into the potential of controllable diffusion models and inspire future research directions in this rapidly evolving field of generative models.Research Methods: First, this article outlines the formulas, sampling methods, and key directions driving the development of diffusion models. Diffusion models are a type of probabilistic distribution-based generative model that generates new data samples by simulating the random processes of data distributions. In terms of sampling methods, diffusion models adopt various strategies, such as Markov Chain Monte Carlo sampling and Langevin sampling, to enhance the quality and diversity of generated samples. However, traditional diffusion models often struggle to meet specific needs in practical applications. Thus, controllable diffusion models have emerged. Controllable diffusion models introduce various controllable factors, such as semantic control, spatial location control, ID control, image style control, and degree control, based on traditional diffusion models. These controllable factors enable the model to generate samples with specific attributes or features according to different requirements, significantly enhancing the model’s practicality and flexibility. In advancing controllable technologies, establishing evaluation metrics is crucial. By setting reasonable evaluation standards, we can quantify the model’s performance, guiding its optimization and improvement. Currently, common evaluation metrics include aspects such as the quality, diversity, and semantic matching of generated samples, which together form the foundational framework for assessing the performance of controllable diffusion models. In addition to theoretical research, controllable diffusion models have also achieved significant results in various application fields. In image processing, controllable diffusion models can be used to generate 2D images with specific styles or content, such as artistic works and face synthesis, and can also be applied to image restoration, video, 3D generation, and personalized generation.Research Results and Conclusions: With the continuous advancement of deep learning technologies, model performance will further improve, significantly enhancing the quality and diversity of generated samples. Secondly, with the advent of the big data era, controllable diffusion models will play a greater role in addressing massive data, providing more effective solutions to practical problems across various fields. Finally, with the development of multimodal data, controllable diffusion models will achieve significant breakthroughs in cross-modal generation, enabling free conversion and generation among various data forms such as text, images, and audio. As an emerging technology, controllable diffusion models demonstrate tremendous potential in solving practical problems. By gaining a deeper understanding of their core principles, technological advancements, and application areas, we can provide strong support for future research and development.
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