Existing text-to-image models often rely on parameter fine-tuning techniques such as Low-Rank Adaptation (LoRA) to customize visual attributes. However, when combining multiple LoRA models for content-style fusion tasks, unstructured modifications of weight matrices often lead to undesired feature entanglement between content and style attributes. We propose QR-LoRA, a novel fine-tuning framework leveraging QR decomposition for structured parameter updates that effectively separate visual attributes. Our key insight is that the orthogonal Q matrix naturally minimizes interference between different visual features, while the upper triangular R matrix efficiently encodes attribute-specific transformations. Our approach fixes both Q and R matrices while only training an additional task-specific $ΔR$ matrix. This structured design reduces trainable parameters to half of conventional LoRA methods and supports effective merging of multiple adaptations without cross-contamination due to the strong disentanglement properties between $ΔR$ matrices. Experiments demonstrate that QR-LoRA achieves superior disentanglement in content-style fusion tasks, establishing a new paradigm for parameter-efficient, disentangled fine-tuning in generative models. The project page is available at: https://luna-ai-lab.github.io/QR-LoRA/.

Diffusion Transformers (DiT) excel in video generation but encounter significant computational challenges due to the quadratic complexity of attention. Notably, attention differences between adjacent diffusion steps follow a U-shaped pattern. Current methods leverage this property by caching attention blocks; however, they still struggle with sudden error spikes and large discrepancies. To address these issues, we propose UniCP—a unified caching and pruning framework for efficient video generation. UniCP optimizes both temporal and spatial dimensions through: Error-Aware Dynamic Cache Window (EDCW): Dynamically adjusts cache window sizes for different blocks at various timesteps to adapt to abrupt error changes. PCA-based Slicing (PCAS) and Dynamic Weight Shift (DWS): PCAS prunes redundant attention components, while DWS integrates caching and pruning by enabling dynamic switching between pruned and cached outputs. By adjusting cache windows and pruning redundant components, UniCP enhances computational efficiency and maintains video detail fidelity. Experimental results show that UniCP outperforms existing methods, delivering superior performance and efficiency.

In this paper, we explore personalized morphing food that enhances traditional food with new HCI capabilities, rather than replacing the chef and authentic ingredients (e.g. flour) with an autonomous machine and heterogeneous mixtures (e.g. gel). Thus, we contribute a unique transformation mechanism of kneaded and sheeted flour-based dough, with an integrated design strategy for morphing food during two general cooking methods: dehydration (e.g. baking) or hydration (e.g. water boiling). We also enrich the design space of morphing food by demonstrating several applications. We end by discussing hybrid cooking between human and a design tool that we developed to ensure accuracy while preserving customizability for morphing food.