LEONG Kuan Yew
Emerging Styles by Ensembling Procedurally-Anchored Models
This gallery presents flower images that look painted, printed, stitched, or some form of artistic stylized - yet the system that generated them was never trained on paintings, illustrations, or any art dataset. No pre-trained checkpoints of any art style / dataset as well. I call this approach Procedural Style Anchoring.
The highlight of this work — and the engine of its creative surprise — is ensembling. Instead of relying on one style model at a time, I combine multiple procedurally anchored models during generation using weighted soft blending, sometimes block-wise across the network. Different “style instincts” can influence different parts of the diffusion process, allowing hybrid aesthetics to emerge: halftone structure with fauvist color, mosaic geometry with painterly softness, felt-like texture fused with poster-flat tonal fields.
The result is not a simple average, but an interference pattern where new visual dialects appear between established styles.
→ Visit the project site for more art pieces.
The art pieces were generated by ensembling of different styled models, check the project site for a more details.
Kuan Yew, LEONG is a lead researcher and adjunct professor of Computer Vision and Machine Learning based in Kyoto, Japan. Currently, he works on the improvements of computer vision by researching different learning algorithms. Kuan Yew demonstrates a keen interest in the intersection of art, natural science, and technology.
See more works on his Github: https://github.com/kuanyewleong
Email: [email protected]