The customization of text-to-image models has seen significant advancements, yet generating multiple personalized concepts remains a challenging task. Current methods struggle with attribute leakage and layout confusion when handling multiple concepts, leading to reduced concept fidelity and semantic consistency. In this work, we introduce a novel training-free framework, Concept Conductor, designed to ensure visual fidelity and correct layout in multi-concept customization. Concept Conductor isolates the sampling processes of multiple customized models to prevent attribute leakage between different concepts and corrects erroneous layouts through self-attention-based spatial guidance. Additionally, we present a concept injection technique that employs shape-aware masks to specify the generation area for each concept. This technique injects the structure and appearance of personalized concepts through feature fusion in the attention layers, ensuring harmony in the final image. Extensive qualitative and quantitative experiments demonstrate that Concept Conductor can consistently generate composite images with accurate layouts while preserving the visual details of each concept. Compared to existing baselines, Concept Conductor shows significant performance improvements. Our method supports the combination of any number of concepts and maintains high fidelity even when dealing with visually similar concepts. The code and trained models will be made publicly available.

Approximate Nearest Neighbor Search (ANNS) is widely used in various fields, including database systems, recommendation engines, and large language models. As data dimension and size continue to expand, many studies explore GPU acceleration for graph-based ANNS. While previous methods use large batch to maximize throughput, they often lead to increased latency. In contrast, small batch is more effective for online lowlatency applications, as it minimizes batch accumulation time. However, employing small batch on GPU presents challenges. First, the query bubble issue in batch processing negatively impacts both latency and GPU utilization. Second, current GPU search methods incur excessive sorting overhead, and introduce additional TopK-merging overhead on GPU. To address these challenges, we propose ALGAS, a low-latency GPU search system designed for small batch. ALGAS employs dynamic batching based on persistent GPU kernel function to optimize query bubble. Additionally, it employs beam extend to reduce sorting overhead, especially effective at high recall rate. It also eliminates TopK-merging overhead via GPU-CPU cooperation. Furthermore, it employs an adaptive GPU tuning scheme to optimize resource utilization. We compare ALGAS with the state-of-the-art graph-based works. ALGAS reduces latency by up to 21.9%-35.4% and increases throughput by up to 27.8%-55.2% under various real-world datasets.

Approximate Nearest Neighbor Search (ANNS) has become a fundamental component in many real-world applications. Among various ANNS algorithms, graph-based methods are state-of-the-art. However, ANNS often suffers from a significant drop in accuracy for certain queries, especially in Out-of-Distribution (OOD) scenarios. To address this issue, a recent approach named RoarGraph constructs a bipartite graph between the base data and historical queries to bridge the gap between two different distributions. However, it suffers from some limitations: (1) Building a bipartite graph between two distributions lacks theoretical support, resulting in the query distribution not being effectively utilized by the graph index. (2) Requires a sufficient number of historical queries before graph construction and suffers from high construction times. (3) When the query workload changes, it requires reconstruction to maintain high search accuracy. In this paper, we first propose Escape Hardness, a metric to evaluate the quality of the graph structure around the query. Then we divide the graph search into two stages and dynamically identify and fix defective graph regions in each stage based on Escape Hardness. (1) From the entry point to the vicinity of the query. We propose R eachability Fix ing (RFix), which enhances the navigability of some key nodes. (2) Searching within the vicinity of the query. We propose N eighboring G raph Defects Fix ing (NGFix) to improve graph connectivity in regions where queries are densely distributed. The results of extensive experiments show that our method outperforms other state-of-the-art methods on real-world datasets, achieving up to 2.25× faster search speed for OOD queries at 99% recall compared with RoarGraph and 6.88× faster speed compared with HNSW. It also accelerates index construction by 2.35-9.02× compared to RoarGraph.