Recent advancements in personalized image generation using diffusion models have been noteworthy. However, existing methods suffer from inefficiencies due to the requirement for subject-specific fine-tuning. This computationally intensive process hinders efficient deployment, limiting practical usability. Moreover, these methods often grapple with identity distortion and limited expression diversity. In light of these challenges, we propose Portrait-Booth, an innovative approach designed for high efficiency, robust identity preservation, and expression-editable text-to-image generation, without the need for fine-tuning. Por-traitBooth leverages subject embeddingsfrom aface recognition model for personalized image generation without fine-tuning. It eliminates computational overhead and mitigates identity distortion. The introduced dynamic identity preservation strategy further ensures close resemblance to the original image identity. Moreover, PortraitBooth incorporates emotion-aware cross-attention control for diverse facial expressions in generated images, supporting text-driven expression editing. Its scalability enables efficient and high-quality image creation, including multi-subject generation. Extensive results demonstrate superior performance over other state-of-the-art methods in both single and multiple image generation scenarios. Our project page is at https://portraitbooth.github.io.

Existing multi-label knowledge distillation methods simply use regression or single-label classification methods without fully exploiting the essence of multi-label classification, resulting in student models’ inadequate performance and poor functional similarity to teacher models. In this paper, we reinterpret multi-label classification as multiple intra-class ranking tasks, with each class corresponding to a ranking task. Furthermore, we define the knowledge of multi-label classification models as the ranking of intra-class samples. On the one hand, we propose to evaluate the functional similarity between multi-label classification models with Kendall’s tau and rank-biased overlap, which are common metrics for evaluating ranking similarity. On the other hand, we propose a new functionally similar multi-label knowledge distillation method called FSD, which enables student models to learn the ranking of intra-class samples from teacher models. Finally, experimental results validate that FSD outperforms existing methods, especially for functional similarity. Specifically, we achieve a mAP of 73.38% and a mKDT of 0.686 on COCO, which are 2.22% and 0.19 better than existing methods, respectively.

Sorting is a kernel algorithm for a wide range of applications. In this paper, we present a new algorithm, GPU-Warpsort, to perform comparison-based parallel sort on Graphics Processing Units (GPUs). It mainly consists of a bitonic sort followed by a merge sort. Our algorithm achieves high performance by efficiently mapping the sorting tasks to GPU architectures. Firstly, we take advantage of the synchronous execution of threads in a warp to eliminate the barriers in bitonic sorting network. We also provide sufficient homogeneous parallel operations for all the threads within a warp to avoid branch divergence. Furthermore, we implement the merge sort efficiently by assigning each warp independent pairs of sequences to be merged and by exploiting totally coalesced global memory accesses to eliminate the bandwidth bottleneck. Our experimental results indicate that GPU-Warpsort works well on different kinds of input distributions, and it achieves up to 30% higher performance than previous optimized comparison-based GPU sorting algorithm on input sequences with millions of elements.

The efficient support of cache coherence is extremely important to design and implement many-core processors. In this paper, we propose a synchronization-based coherence (SBC) protocol to efficiently support cache coherence for shared memory many-core architectures. The unique feature of our scheme is that it doesn’t use directory at all. Inspired by scope consistency memory model, our protocol maintains coherence at synchronization point. Within critical section, processor cores record write-sets (which lines have been written in critical section) with bloom-filter function. When the core releases the lock, the write-set is transferred to a synchronization manager. When another core acquires the same lock, it gets the write-set from the synchronization manager and invalidates stale data in its local cache. Experimental results show that the SBC outperforms by averages of 5% in execution time across a suite of scientific applications. At the mean time, the SBC is more cost-effective comparing to directory-based protocol that requires large amount of hardware resource and huge design verification effort.