Deep generative models produce data according to a learned representation,
e.g. diffusion models, through a process of approximation computing possible
samples. Approximation can be understood as reconstruction and the large
datasets used to train models as sets of records in which we represent the
physical world with some data structure (photographs, audio recordings,
manuscripts). During the process of reconstruction, e.g., image frames develop
each timestep towards a textual input description. While moving forward in
time, frame sets are shaped according to learned bias and their production, we
argue here, can be considered as going back in time; not by inspiration on the
backward diffusion process but acknowledging culture is specifically marked in
the records. Futures of generative modelling, namely in film and audiovisual
arts, can benefit by dealing with diffusion systems as a process to compute the
future by inevitably being tied to the past, if acknowledging the records as to
capture fields of view at a specific time, and to correlate with our own finite
memory ideals. Models generating new data distributions can target video
production as signal processors and by developing sequences through timelines
we ourselves also go back to decade-old algorithmic and multi-track
methodologies revealing the actual predictive failure of contemporary
approaches to synthesis in moving image, both as relevant to composition and
not explanatory.

通过近似计算生成新数据分布的深度生成模型，特别是扩散模型，在重构过程中使用大型数据集来训练模型，以图像帧为单位推进时间，同时认识到文化在记录中标记，将扩散系统视为未来计算过程，与过去息息相关，并针对视频制作作为信号处理器生成序列，回溯十年前的算法和多轨制作方法，揭示了当代合成方法在动态图像中的实际预测失败。