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Generative AI with Diffusion Models: Toward Efficient, Fair and Scalable Content Synthesis

  • Unique Paper ID: 178361
  • Volume: 11
  • Issue: 12
  • PageNo: 2988-2990
  • Abstract:
  • Diffusion-based generative models have eclipsed GAN and VAE architectures in fidelity, robustness and mode coverage, but their iterative denoising chains impose steep com- putational and energy budgets. We present a complete stack that (i) fuses Transformer self-attention and convolution into a lightweight denoiser, (ii) compresses a 1 000-step teacher into a four-step student via progressive distillation, (iii) applies loss- aware pruning, mixed-precision kernels and adaptive timestep scheduling, and (iv) embeds a real-time bias-detection guardrail. Trained on a 550 k image–text corpus filtered for legal and ethical compliance, the system delivers an FID of 6.9 on MS- COCO while running 4.6× faster and 4.5× greener than a 50- step baseline, and it surpasses Stable Diffusion 1.5 by 1.2 FID at 38 % lower energy. Experiments on desktop GPUs, laptop GPUs and edge NPUs confirm viability for interactive design, AR filters and mobile creativity apps, moving diffusion models closer to trustworthy, resource-aware deployment.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 2988-2990

Generative AI with Diffusion Models: Toward Efficient, Fair and Scalable Content Synthesis

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