Overcoming the Semantic Bottleneck for Deterministic Structural Control in Text-to-Image Synthesis

Latent diffusion models are limited by text encoders, restrict geometric control, and prevent deterministic structural steering.We propose Procedural Latent Prompt Injection as a Zero-shot steerability framework for generative modelling, modelled as a steerable Stochastic differential equation (SDE). This avoids the need for either auxiliary training or linguistics-based conditioning. A key component of the approach is the use of normalized tensor operators to embed geometric priors directly into the latent space. Empirical analysis of the diffusion trajectory indicates a critical plasticity window (timesteps 10–20) where the noise patterns embedded during diffusion are most amenable to structural steering. Compared with baselines, improvements of 19.6% in structural alignment (CLIP) and 12.3% in diversity control are achieved. These results represent an improvement over prior learned control approaches (prompt-to-prompt & controlnet) and provide a new deterministic paradigm for generative modelling control with applications in procedural synthesis of medical images, structural biology models, and physics simulations.