AI-Powered Sprite Generation for Visual Novel Games: A Latent Diffusion Model Approach - Ayo, Eliza B.; Belleza, Pio Honesto Rico; Esmaquel, Aaron Dwayne F.; Lazaro, Edmund N. (Future Science / FSH-PH Publications, 2026)
Creating character sprites for visual novel games has long been a resource-intensive process, erecting significant barriers for independent developers and small studios. Accessible, high-quality asset generation remains an unmet need—one this study addresses by developing a specialized AI-powered sprite generator built on a Latent Diffusion Model (LDM) fine-tuned with Low-Rank Adaptation (LoRA). Unlike general-purpose image generators, the system is optimized specifically for 2D anime-style character sprites with multiple emotional variants, making it suitable for narrative-driven gameplay. Development followed a seven-phase Agile methodology, and evaluation involved 50 student game developers working against ISO/IEC 25010 software quality standards. Results confirm that the system reliably generates up to nine cohesive emotional variants from a single text prompt, substantially streamlining the asset-creation workflow. User scores indicate strong satisfaction with Functional Suitability (M=3.73), Security (M=3.74), and Usability (M=3.66). Performance Efficiency received a lower rating (M=3.20, Agree), attributable to a generation latency of approximately 2–3 minutes per sprite set—acceptable for final production, but less so for rapid prototyping. Taken together, these findings support the viability of specialized LDM applications as practical tools for democratizing visual game development and empowering collaborative storytelling.