mlsanityData to 14 Oct 2026

Z-SASLM: Zero-Shot Style-Aligned SLI Blending Latent Manipulation

arXiv 2503.23234 ↗First posted 29 Mar 2025cs.CVcited by 0

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Abstract (arXiv)

We introduce Z-SASLM, a Zero-Shot Style-Aligned SLI (Spherical Linear Interpolation) Blending Latent Manipulation pipeline that overcomes the limitations of current multi-style blending methods. Conventional approaches rely on linear blending, assuming a flat latent space leading to suboptimal results when integrating multiple reference styles. In contrast, our framework leverages the non-linear geometry of the latent space by using SLI Blending to combine weighted style representations. By interpolating along the geodesic on the hypersphere, Z-SASLM preserves the intrinsic structure of the latent space, ensuring high-fidelity and coherent blending of diverse styles - all without the need for fine-tuning. We further propose a new metric, Weighted Multi-Style DINO ViT-B/8, designed to quantitatively evaluate the consistency of the blended styles. While our primary focus is on the theoretical and practical advantages of SLI Blending for style manipulation, we also demonstrate its effectiveness in a multi-modal content fusion setting through comprehensive experimental studies. Experimental results show that Z-SASLM achieves enhanced and robust style alignment. The implementation code can be found at: https://github.com/alessioborgi/Z-SASLM.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 30 references that are in the map. First posted on arXiv 29 Mar 2025.

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References · 19

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  7. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
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  11. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  12. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 577
7 more
  1. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  2. 2023BLIP-Diffusion: Pre-trained Subject Representation for Controllable Text-to-Image Generation and Editingcited by 442
  3. 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
  4. 2023Style Aligned Image Generation via Shared Attentioncited by 216
  5. 2019MirrorGAN: Learning Text-to-image Generation by Redescriptioncited by 176
  6. 2018Photographic Text-to-Image Synthesis with a Hierarchically-nested Adversarial Networkcited by 64
  7. 2019Supporting stylists by recommending fashion stylecited by 6

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