Scene text generation in images remains a major challenge for current text-to-image (T2I) systems, especially in morphologically rich and underrepresented languages such as Spanish. Existing generative models often produce text that is visually distorted, grammatically incorrect, or semantically inconsistent. In this work, we present a targeted approach to improve scene text generation in Spanish by fine-tuning a state-of-the-art diffusion model, FLUX.1-dev, using a novel dataset of Spanish-language memes (CCMD). Our methodology integrates prompt engineering, Low-Rank Adaptation (LoRA), and a custom evaluation protocol that includes human judgment. We demonstrate that increasing the number of denoising steps \(k\) leads to consistent improvements in legibility, alignment, and linguistic fidelity. Our results show that the proposed model outperforms baseline systems such as GPT-4 (DALL \(\cdot \) E). We further propose a roadmap for building automatic evaluation frameworks that assess scene text not only lexically, but also semantically and visually, paving the way toward more inclusive and robust generative systems.