Despite their merits, a comprehensive comparison and synergistic application of them to a diverse model zoo is yet to be adequately addressed.

LDM training recipes are oftentimes not available to the research community, preventing apple-to-apple comparisons and hindering the validation of progress in the field. In this work, we perform an in-depth study of LDM training recipes focusing on the performance of models and their training efficiency. To ensure apple-to-apple comparisons, we re-implement five previously published models with their corresponding recipes.

Middle left: The eight images are examples of BLURD SD: Unleashed generated by the original SD 1.5 model conditioned only on text generated from the same factors as the corresponding

Recent advancements in pre-trained vision models have made them pivotal in computer vision, emphasizing the need for their thorough evaluation and benchmarking. This evaluation needs to consider various factors of variation, their potential biases, shortcuts, and inaccuracies that ultimately lead to disparate performance in models.

Minimization (SAM), a recently proposed gradient-based optimization method [Foret et al., 2021] that significantly improves the generalization of deep neural

We implement DapperFL on a real-world FL platform with heterogeneous clients.

As the architecture of the transformers is essential to the model's performance [

While these algorithms have been extensively studied, both theoretically and practically, see, e.g., [