The novel concept of mobile edge generation (MEG) is investigated, where the generative artificial intelligence (GAI) model is partitioned into sub-models to be distributed in the network edge, thus enabling latent feature exchange between the edge server and user equipments (UEs). A seed coding module is introduced to encode the intermediate latent features generated by the GAI sub-model at the edge server into flexibly-sized seed for transmission to UEs, instead of transmitting large-size raw data. A weighted energy consumption minimization problem is formulated by jointly optimizing the seed coding ratio (SCR), transmit power, and computing frequencies while guaranteeing the quality-of-generation requirements including total latency and peak signal-to-noise ratio (PSNR). To enhance the resilience of the MEG models against the channel noise, a joint fine-tuning scheme based on low-rank adaption is proposed to train the introduced rank-reduced bypass matrices and seed coding module. Based on the fine-tuned results, a PSNR model regarding SCR and communication signal-to-noise ratio is established to overcome the optimization difficulty due to the lack of the explicit PSNR model. A proximal policy optimization-based MEG energy consumption optimization (MEG-ECO) algorithm is proposed to solve the formulated problem, where the order of magnitude balancing on state and penalty shaping are exploited for more efficient learning. Numerical results reveal that 1) the fine-tuned MEG models have superior resilience against the channel noise; 2) the proposed MEG-ECO algorithm can significantly reduce energy consumption by up to 87.4% compared to conventional centralized generation and up to 33.5% against MEG without seed coding module; and 3) the energy consumption decreases when more partial models are assigned to the edge server, whereas this impact diminishes as the latency threshold is relaxed.