All YIN No YANG: Geometric Abstraction of Oil Paintings with Trained Models, Noise and Self-reference

The rapid development of Diffusion models and the declarative nature of interfaces developed for the public require automation methods, where media production can harness natural language as a mode of representation but not necessarily of interaction with humans. This article describes an image-to-video Diffusion system which removes practitioners from the process of defining prompts when producing images with conditional reference, documenting a set of results with a custom dataset of oil paintings. Our research focuses on the appropriation of trained model ensembles that are coordinated to produce indefinite sets of frames with occasional human intervention utilising timeline-based architectures. The proposed system automates a CLIP-guided DDPM with a supplementary depth estimation model and through a set of compositing techniques we found that results with coincidental and diverging descriptions can be useful for moving-image element composition. Our experiments focus on the representation of human figure and its morphological transformation.