  A novel accelerated mobile edge generation (MEG) framework is proposed for
generating high-resolution images on mobile devices. Exploiting a large-scale
latent diffusion model (LDM) distributed across edge server (ES) and user
equipment (UE), cost-efficient artificial intelligence generated content (AIGC)
is achieved by transmitting low-dimensional features between ES and UE. To
reduce overheads of both distributed computations and transmissions, a dynamic
diffusion and feature merging scheme is conceived. By jointly optimizing the
denoising steps and feature merging ratio, the image generation quality is
maximized subject to latency and energy consumption constraints. To address
this problem and tailor LDM sub-models, a low-complexity MEG acceleration
protocol is developed. Particularly, a backbone meta-architecture is trained
via offline distillation. Then, dynamic diffusion and feature merging are
determined in online channel environment, which can be viewed as a constrained
Markov Decision Process (MDP). A constrained variational policy optimization
(CVPO) based MEG algorithm is further proposed for constraint-guaranteed
learning, namely MEG-CVPO. Numerical results verify that: 1) The proposed
framework can generate 1024$\times$1024 high-quality images over noisy channels
while reducing over $40\%$ latency compared to conventional generation schemes.
2) The developed MEG-CVPO effectively mitigates constraint violations, thus
flexibly controlling the trade-off between image distortion and generation
costs.
