ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
- URL: http://arxiv.org/abs/2311.13600v1
- Date: Wed, 22 Nov 2023 18:59:36 GMT
- Title: ZipLoRA: Any Subject in Any Style by Effectively Merging LoRAs
- Authors: Viraj Shah, Nataniel Ruiz, Forrester Cole, Erika Lu, Svetlana
Lazebnik, Yuanzhen Li, Varun Jampani
- Abstract summary: Low-rank adaptations (LoRA) have been proposed as a parameter-efficient way of achieving concept-driven personalization.
We propose ZipLoRA, a method to cheaply and effectively merge independently trained style and subject LoRAs.
Experiments show that ZipLoRA can generate compelling results with meaningful improvements over baselines in subject and style fidelity.
- Score: 56.85106417530364
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Methods for finetuning generative models for concept-driven personalization
generally achieve strong results for subject-driven or style-driven generation.
Recently, low-rank adaptations (LoRA) have been proposed as a
parameter-efficient way of achieving concept-driven personalization. While
recent work explores the combination of separate LoRAs to achieve joint
generation of learned styles and subjects, existing techniques do not reliably
address the problem; they often compromise either subject fidelity or style
fidelity. We propose ZipLoRA, a method to cheaply and effectively merge
independently trained style and subject LoRAs in order to achieve generation of
any user-provided subject in any user-provided style. Experiments on a wide
range of subject and style combinations show that ZipLoRA can generate
compelling results with meaningful improvements over baselines in subject and
style fidelity while preserving the ability to recontextualize. Project page:
https://ziplora.github.io
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