Reconstruction of Semantic Consistency in Brand Visual Identity System via GLIGEN-Guided Multimodal Diffusion Modeling

Addressing the fragmentation of the Brand Visual Identity System (VIS) in digital communication, especially for diverse textures, layouts and brand elements in digital communication, this paper proposes a GLIGEN-guided multimodal diffusion modeling reconstruction framework to achieve precise matching between brand semantics and visual presentation. By constructing a dual-layer label system of "core semantics - visual semantics", designing a five-layer architecture including input layer, feature fusion layer, GLIGEN guidance layer, diffusion generation layer and semantic verification layer, a quantitative evaluation system combining CLIP semantic similarity and expert research is established. Taking 10 brands (5 fast-moving consumer goods and 5 high-end service brands) as experimental objects, comparing with traditional design methods and diffusion models without GLIGEN guidance, the results show that the CLIP semantic similarity of the proposed model reaches 0.89±0.03, the SSIM visual consistency reaches 0.86±0.02, and the single-image generation time is only 8.2 seconds, which significantly improves the efficiency compared with traditional manual design. Case verification shows that this framework can increase the cognitive degree of fast-moving consumer goods brands by 23% and the cross-scenario visual unity of high-end service brands reaches 92%. This research fills the gap of multimodal generation technology in the field of brand semantic control and provides a technical paradigm for brand VIS reconstruction, enhancing the ability of brands to maintain aesthetic coherence across diverse digital presentations.