Abstract Art Interpretation Using ControlNet
Rishabh Srivastava, Addrish Roy
TL;DR
This paper explores using ControlNet to improve spatial control in text-to-image synthesis of abstract art, enabling precise manipulation through novel geometric conditions inspired by minimalist forms.
Contribution
It introduces a new geometric primitive-based condition for ControlNet, enhancing control over abstract art generation from textual prompts.
Findings
Enhanced spatial control in abstract art synthesis
Successful integration of geometric primitives as conditions
Improved manipulation capabilities in text-to-image models
Abstract
Our study delves into the fusion of abstract art interpretation and text-to-image synthesis, addressing the challenge of achieving precise spatial control over image composition solely through textual prompts. Leveraging the capabilities of ControlNet, we empower users with finer control over the synthesis process, enabling enhanced manipulation of synthesized imagery. Inspired by the minimalist forms found in abstract artworks, we introduce a novel condition crafted from geometric primitives such as triangles.
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Taxonomy
TopicsAesthetic Perception and Analysis