Diffusion models, widely used in image generation, rely on iterative refinement to generate images from noise. Understanding this data evolution is important for model development and interpretability, yet challenging due to its high-dimensional, iterative nature. Prior works often focus on static or instance-level analyses, missing the iterative and holistic aspects of the generative path. While dimensionality reduction can visualize image evolution for few instances, it does preserve the iterative structure. To address these gaps, we introduce EvolvED, a method that presents a holistic view of the iterative generative process in diffusion models. EvolvED goes beyond instance exploration by leveraging predefined research questions to streamline generative space exploration. Tailored prompts aligned with these questions are used to extract intermediate images, preserving iterative context. Targeted feature extractors trace the evolution of key image attribute evolution, addressing the complexity of high-dimensional outputs. Central to EvolvED is a novel evolutionary embedding algorithm that encodes iterative steps while maintaining semantic relations. It enhances the visualization of data evolution by clustering semantically similar elements within each iteration with t-SNE, grouping elements by iteration, and aligning an instance's elements across iterations. We present rectilinear and radial layouts to represent iterations and support exploration. We apply EvolvED to diffusion models like GLIDE and Stable Diffusion, demonstrating its ability to provide valuable insights into the generative process.

翻译：扩散模型在图像生成领域应用广泛，其通过迭代优化从噪声中生成图像。理解这一数据演化过程对于模型开发和可解释性至关重要，但由于其高维、迭代的特性而颇具挑战。先前的研究多集中于静态或实例层面的分析，未能捕捉生成路径的迭代性与整体性。尽管降维技术可为少数实例可视化图像演化过程，但难以保持迭代结构。为弥补这些不足，我们提出了EvolvED方法，该方法为扩散模型的迭代生成过程提供了整体性视角。EvolvED通过利用预设的研究问题来简化生成空间的探索，超越了实例层面的分析。我们使用与这些问题相适配的提示词来提取中间图像，从而保留迭代上下文。针对性的特征提取器追踪关键图像属性的演化轨迹，以应对高维输出的复杂性。EvolvED的核心是一种新颖的进化嵌入算法，该算法在编码迭代步骤的同时保持语义关系。它通过t-SNE在每个迭代内聚类语义相似元素、按迭代分组元素，并对齐同一实例跨迭代的元素，从而增强了数据演化的可视化效果。我们提出了直线型与放射型两种布局来表征迭代过程并支持探索分析。我们将EvolvED应用于GLIDE和Stable Diffusion等扩散模型，证明了其能为生成过程提供有价值的洞见。