arXiv subjects

Beomseok Ko

Publications and source records attributed to Beomseok Ko.

3 recordsLinked to original sources

arXiv2026

MAST: Mask-Guided Attention Control for Training-Free Regional-Multi Style Transfer

Style transfer applies the appearance of a reference image to a content image while preserving its spatial structure. Recent diffusion-based methods achieve strong stylization but typically assume a single global style. We instead consider regional-multi style transfer, which assigns multiple references to user-specified regions of a content image. Extending them to this setting reveals two coupled shared-attention issues: ambiguous mass allocation among content and style partitions and degraded selectivity as more styles are jointly normalized, while style aggregation at the attention output further suppresses fine details. We propose MAST (Mask-Guided Attention Control for Training-Free Regional-Multi Style Transfer), a unified attention-control framework for frozen diffusion models. Logit-level Attention Mass Allocation enforces mask-derived partition masses, Sharpness-aware Temperature Scaling adaptively restores selectivity, and Discrepancy-aware Detail Injection recovers high-frequency content. MAST jointly processes all style--mask pairs in a single denoising pass without training, optimization, or post-hoc composition. Across two to five styles, MAST achieves the best average ArtFID, FID, and R-FID among all baselines, demonstrating regional style fidelity, content preservation, and scalability.

cs.CV↗

arXiv2025

PQCAD-DM: Progressive Quantization and Calibration-Assisted Distillation for Extremely Efficient Diffusion Model

Diffusion models excel in image generation but are computational and resource-intensive due to their reliance on iterative Markov chain processes, leading to error accumulation and limiting the effectiveness of naive compression techniques. In this paper, we propose PQCAD-DM, a novel hybrid compression framework combining Progressive Quantization (PQ) and Calibration-Assisted Distillation (CAD) to address these challenges. PQ employs a two-stage quantization with adaptive bit-width transitions guided by a momentum-based mechanism, reducing excessive weight perturbations in low-precision. CAD leverages full-precision calibration datasets during distillation, enabling the student to match full-precision performance even with a quantized teacher. As a result, PQCAD-DM achieves a balance between computational efficiency and generative quality, halving inference time while maintaining competitive performance. Extensive experiments validate PQCAD-DM's superior generative capabilities and efficiency across diverse datasets, outperforming fixed-bit quantization methods.

cs.CV↗

arXiv2024

StyleForge: Enhancing Text-to-Image Synthesis for Any Artistic Styles with Dual Binding

Recent advancements in text-to-image models, such as Stable Diffusion, have showcased their ability to create visual images from natural language prompts. However, existing methods like DreamBooth struggle with capturing arbitrary art styles due to the abstract and multifaceted nature of stylistic attributes. We introduce Single-StyleForge, a novel approach for personalized text-to-image synthesis across diverse artistic styles. Using approximately 15 to 20 images of the target style, Single-StyleForge establishes a foundational binding of a unique token identifier with a broad range of attributes of the target style. Additionally, auxiliary images are incorporated for dual binding that guides the consistent representation of crucial elements such as people within the target style. Furthermore, we present Multi-StyleForge, which enhances image quality and text alignment by binding multiple tokens to partial style attributes. Experimental evaluations across six distinct artistic styles demonstrate significant improvements in image quality and perceptual fidelity, as measured by FID, KID, and CLIP scores.

cs.CV↗