LABridge: A Text–Image Latent Alignment Framework via OU Diffusion Bridge
Huiyang Shao, Xin Xia, Yuxi Ren, XING WANG, Xuefeng Xiao
Diffusion models have emerged as state‑of‑the‑art in image synthesis.However, it often suffer from semantic instability and slow iterative denoising. We introduce LABridge (Latent Alignment Bridge), a novel Text–Image Latent Alignment Framework via an Ornstein–Uhlenbeck (OU) bridge, which explicitly preserves and aligns textual and visual semantics in a aligned latent space. LABridge employs a Text-Image Alignment Encoder (TIAE) to encode text prompts into structured priors that are directly aligned with image latents. Instead of a homogeneous Gaussian, OU diffusion bridge smoothly interpolates between these text‑conditioned priors and image latents, improving stability and reducing the number of denoising steps. Extensive experiments on standard text-to-image benchmarks show that LABridge achieves better text–image alignment metric, and competitive FID scores compared to leading diffusion baselines. By unifying text and image representations through principled latent alignment, LABridge paves the way for more efficient, semantically consistent, and high‑fidelity text to image generation.