Innovative Applications of Diffusion Models in Visual Style Transformation for Brand Logos

Brand logos anchor visual identity, yet adapting them to diverse styles is difficult because geometry, typography, and brand cues must be preserved while appearance changes. The authors present LogoDiffusion-Align (LDA), a diffusion framework with three coordinated modules: Structure-Preserving Control (SPC) constrains shapes and text to prevent geometric drift; Style-Consistent Alignment (SCA) injects learned style tokens to achieve coherent, scene-wide stylization; and a Logo-specific Identity Module (LIM) embeds brand-aware representations to retain distinctive identity features. Across multiple datasets and usage scenarios, LDA outperforms strong diffusion-based baselines including ControlNet, DreamBooth, StyleTokenizer, and InST on both fidelity and identity preservation. In controlled comparisons, LDA attains higher SSIM (0.789 vs. 0.742) and CLIP-Id (0.752 vs. 0.708), while also reducing FID and LPIPS, indicating a more favorable fidelity–perceptual quality trade-off.