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Published in CVPR (Computer Vision and Pattern Recognition) 2025 Workshops (Nashville, USA ๐บ๐ธ), 2025
Z-SASLM introduces a zero-shot, fine-tuning-free approach to style alignment in diffusion models by blending multiple reference styles directly in latent space using spherical linear interpolation (SLI) with learned, context-aware weights. The method avoids model retraining, preserves content semantics, and yields consistent style transfer across prompts and seeds.
Recommended citation: Borgi, A.; Maiano, L.; Amerini, I. (2025). "Z-SASLM: Zero-Shot Style-Aligned SLI Blending for Latent Manipulation." CVPR 2025 Workshops.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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