We describe three PCFG-based models for Chinese sentence realisation from Lexical-Functional Grammar (LFG) f-structures. Both the lexicalised model and the history-based model improve on the accuracy of a simple wide-coverage PCFG model by adding lexical and contextual information to weaken inappropriate independence assumptions implicit in the PCFG models. In addition, we provide techniques for lexical smoothing and rule smoothing to increase the generation coverage. Trained on 15,663 automatically LFG f-structure annotated sentences of the Penn Chinese treebank and tested on 500 sentences randomly selected from the treebank test set, the lexicalised model achieves a BLEU score of 0.7265 at 100% coverage, while the history-based model achieves a BLEU score of 0.7245 also at 100% coverage.