Flow matching audio editing models manipulate only synchronized music samples of the same duration, limiting their understanding of expressive timing. We introduce PianoKontext, a flow matching rendering model for classical piano and chamber music that generates variable-length performances in the latent space of a pretrained Music2Latent model. We synthesize deadpan audio from MIDI scores and employ Dynamic Time Warping (DTW) in the latent space to construct paired data for training. The aligned embeddings are concatenated within DiT blocks, allowing for simple and effective learning of the score-performance dependencies. Demo page: \url{https://realfolkcode.github.io/ismir2026_lbd/}.