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cs.CV2024
Style Ambiguity Loss Using CLIP
James Baker
In this work, we explore using the style ambiguity training objective, originally used to approximate creativity, on a diffusion model. However, this objective requires the use of…
cs.CV2024
BRAT: Bonus oRthogonAl Token for Architecture Agnostic Textual Inversion
James Baker
Textual Inversion remains a popular method for personalizing diffusion models, in order to teach models new subjects and styles. We note that textual inversion has been underexplor…
cs.CV2024
Using Multimodal Foundation Models and Clustering for Improved Style Ambiguity Loss
James Baker
Teaching text-to-image models to be creative involves using style ambiguity loss, which requires a pretrained classifier. In this work, we explore a new form of the style ambiguity…