Enhancing one-step diffusion models using GANs with application to mental health mindfulness

Recent advances in generative models have enabled the synthesis of high-fidelity images across various domains. However, applications in mental health remain underexplored, particularly in the context of interactive emotional visualization. This paper introduces GOOD (GAN Optimized with One-step Diffusion), a hybrid generative framework that integrates a GAN-based refinement mechanism into a one-step diffusion model. The proposed approach addresses key limitations of current one-step methods by enabling localized correction of visual artifacts while preserving low-latency generation. Experimental results demonstrate improvements of up to +32 dB in PSNR (indicating sharper and less noisy outputs) and +0.9 in SSIM (reflecting better structural fidelity and perceptual quality) over the one-step baseline. Beyond quantitative gains, the enhanced visual clarity and stability are particularly valuable in mindfulness and therapeutic settings, where more coherent and emotionally resonant imagery can foster user engagement, reduce distraction caused by artifacts, and support mental well-being through more immersive visualization experiences.