The field of diffusion models is moving towards increased focus on unlearning and evaluation of these models. Researchers are developing new methods to remove unwanted information from diffusion models, such as Concept Unlearning by Modeling Key Steps of Diffusion Process, which strategically focuses on pivotal steps in the diffusion process to reduce the number of parameter updates needed for effective unlearning. Another area of focus is on understanding how diffusion models learn concepts, with methods like Concept-TRAK providing concept-level attribution to identify contributions to specific elements in generated images. Notable papers in this area include Towards a Principled Evaluation of Knowledge Editors, which highlights the need for more robust evaluation methodologies for knowledge editors, and Image Can Bring Your Memory Back, which proposes a novel adversarial framework to compromise the robustness of unlearned image generation models.