More than enhancing the entertainment experience, the applications of HDR images extend to several areas of recognized importance. In the medical field, for example, the visual precision offered by HDR images is essential for more accurate diagnoses and more effective treatments. In radiology, such images can reveal subtle details that would go unnoticed in LDR images, allowing doctors to identify diseases in earlier stages [5]. Similarly, in education, HDR images can improve understanding and learning by providing more realistic and detailed representations of concepts. This aspect proves especially useful in fields like science and engineering, where the visualization of complex data is essential [6]. Also, in the field of visual communication, HDR images are ideal for advertising and graphic design, as they allow information to be conveyed more effectively and attractively. Despite these numerous benefits, capturing native HDR images, i.e., without the need for software processing, requires specialized hardware, which makes high-quality HDR poorly accessible, as such sensors tend to be expensive and require more complex handling due to their size and weight [7]. As a result, the scientific community has investigated methods to reconstruct HDR images from a single or multiple LDR images. In this perspective, reconstruction refers to the process of creating an HDR version using the information contained in the reference with one or multiple LDR images [8]. These strategies are called single-frame and multi-frame reconstruction, respectively. The first consists of using only one LDR image to generate its HDR counterpart, while multi-frame reconstruction uses more than one image to extract color and luminosity information over a short time interval [9; 10]. It is worth noting that the inherent challenges in these reconstruction approaches are being addressed with increasing sophistication by new deep learning paradigms. For example, single-frame reconstruction is an ill-posed problem that requires \"hallucinating\" lost information, which is a suitable task for generative models like GANs and DMs [8; 10; 11]. Multi-frame reconstruction, although benefiting from more information, faces critical obstacles such as motion-related artifacts (blur, ghosting, misalignment) due to the movement of objects or the camera between exposures and photometric artifacts (noise, saturation, texture loss) arising from sensor limitations and exposure differences [12; 13; 14]. These specific challenges directly motivate the development and comparison of the different deep learning strategies addressed in this research--from Convolutional Neural Networks (CNNs), optical flow, or deformable convolutions for explicit alignment [15; 16] to Transformers using attention for implicit alignment and modeling long-range dependencies [17; 18] and DMs offering robust generative capabilities for artifact reduction [19]. Additionally, model-induced artifacts, such as checkerboarding patterns or edge halos, also require careful architectural design and loss function selection [20; 21]. Early deep learning approaches relied heavily on CNNs, using autoencoder structures [8], feedback mechanisms [22], or multi-exposure generation pipelines [23] for single-frame tasks. Multi-frame CNN methods often incorporated optical flow [15] or attention blocks [13] to handle alignment. In parallel, GANs demonstrated solid results, offering enhanced photorealism through adversarial training to simulate nuances of contrast and light intensity [24; 25; 26]. More recently, Vision Transformers (ViTs) introduced powerful self-attention mechanisms, allowing models to focus on relevant image regions and capture global context, proving effective for implicit alignment and feature fusion in the HDR, and offering an alternative to explicit alignment methods [2; 17; 27]. Following this same line of evolution, DMs emerged as the state of the art in generative models [3], overcoming the limitations of other methods such as stability issues in GANs [25].