A Training-Free Style-aligned Image Generation with Scale-wise Autoregressive Model

We present a training-free style-aligned image generation method that leverages a scale-wise autoregressive model. While large-scale text-to-image (T2I) models, particularly diffusion-based methods, have demonstrated impressive generation quality, they often suffer from style misalignment across generated image sets and slow inference speeds, limiting their practical usability. To address these issues, we propose three key components: initial feature replacement to ensure consistent background appearance, pivotal feature interpolation to align object placement, and dynamic style injection, which reinforces style consistency using a schedule function. Unlike previous methods requiring fine-tuning or additional training, our approach maintains fast inference while preserving individual content details. Extensive experiments show that our method achieves generation quality comparable to competing approaches, significantly improves style alignment, and delivers inference speeds over six times faster than the fastest model.

TEXTOC: Text-driven Object-Centric Style Transfer

We present Text-driven Object-Centric Style Transfer (TEXTOC), a novel method that guides style transfer at an object-centric level using textual inputs. The core of TEXTOC is our Patch-wise Co-Directional (PCD) loss, meticulously designed for precise object-centric transformations that are closely aligned with the input text. This loss combines a patch directional loss for text-guided style direction and a patch distribution consistency loss for even CLIP embedding distribution across object regions. It ensures a seamless and harmonious style transfer across object regions. Key to our method are the Text-Matched Patch Selection (TMPS) and Pre-fixed Region Selection (PRS) modules for identifying object locations via text, eliminating the need for segmentation masks. Lastly, we introduce an Adaptive Background Preservation (ABP) loss to maintain the original style and structural essence of the image's background. This loss is applied to dynamically identified background areas. Extensive experiments underline the effectiveness of our approach in creating visually coherent and textually aligned style transfers.

Prior-Aware Robust Beam Alignment for Low-SNR Millimeter-Wave Communications

This paper presents a robust beam alignment technique for millimeter-wave communications in low signal-to-noise ratio (SNR) environments. The core strategy of our technique is to repeatedly transmit the most probable beam candidates to reduce beam misalignment probability induced by noise. Specifically, for a given beam training overhead, both the selection of candidates and the number of repetitions for each beam candidate are optimized based on channel prior information. To achieve this, a deep neural network is employed to learn the prior probability of the optimal beam at each location. The beam misalignment probability is then analyzed based on the channel prior, forming the basis for an optimization problem aimed at minimizing the analyzed beam misalignment probability. A closed-form solution is derived for a special case with two beam candidates, and an efficient algorithm is developed for general cases with multiple beam candidates. Simulation results using the DeepMIMO dataset demonstrate the superior performance of our technique in dynamic low-SNR communication environments when compared to existing beam alignment techniques.