Research on multiclass game image generation based on classifier-free diffusion guidance

Abstract

Diffusion models have achieved remarkable progress in visual generation, yet their practical deployment for multi-class procedural scene synthesis still lacks standardized training schemes and comprehensive performance verification. This study adopts DDPM as the base architecture and introduces classifier-free guidance to facilitate controllable multi-category image generation toward Procgen game scenes. We formulate unified data preprocessing rules and consistent training settings, and quantitatively compare the generation capability of unconditional diffusion, vanilla class-conditional diffusion, and classifier-free guided diffusion models via FID metrics. We further explore the influence of core hyperparameters, including label drop probability and guidance scale on generated sample quality, fidelity, and diversity. Experimental results verify that classifier-free guidance effectively outperforms traditional conditional diffusion methods in overall generation performance, and proper hyperparameter combination can further narrow the distribution gap between synthetic images and real scene data. In addition, we observe obvious performance differences across categories with distinct visual complexity. This work provides solid empirical evidence and practical tuning experience for applying conditional diffusion models to procedural multi-class image generation, and opens feasible directions for subsequent adaptive conditional generation research.