GPT-4o图像生成的「核燃料」找到了！万字长文拆解潜在变量，网友：原来AI在另一个维度作画

Core Viewpoint - The article discusses the significance of latent space in generative models, emphasizing its role in enhancing the efficiency and quality of image, audio, and video generation through various training methodologies [2][3][4]. Group 1: Latent Space and Generative Models - Latent space is described as the "essence of data," enabling the compression of complex information for generating images and audio [3]. - The article highlights the comparison between Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, illustrating how latent variables support the generation of realistic content [3][4]. - The two-stage training method for generative models involves training an encoder to map input signals to latent representations and then training the generative model on these representations [7][9]. Group 2: Training Methodologies - The first training phase focuses on ensuring high fidelity in converting input representations to latent vectors and back, utilizing various loss functions [10][12]. - The second phase involves training the generative model using its own loss functions, which are distinct from those used in the first phase [10][22]. - The article emphasizes the importance of using compact representations to enhance the efficiency of generative models, allowing them to focus on perceptually relevant signal content [22][23]. Group 3: Evolution of Generative Models - The emergence of self-regressive and diffusion models has transformed the landscape of generative modeling, with early works focusing on pixel-based generation [15][19]. - The introduction of VQ-VAE marked a significant advancement in image self-regressive models, allowing for discrete latent representations and improving generation efficiency [16][18]. - The combination of latent self-regressive models and diffusion models has led to the development of latent diffusion models, which have gained popularity in recent years [20][21]. Group 4: Loss Functions and Model Performance - The article discusses the role of various loss functions, including regression loss, perceptual loss, and adversarial loss, in enhancing the quality of generated outputs [49][50]. - It highlights the trade-off between reconstruction quality and the ability to model latent representations effectively, suggesting that a balance must be struck to optimize performance [41][61]. - The use of auxiliary decoders is proposed as a strategy to separate the tasks of representation learning and reconstruction, potentially improving model performance [58][60].