DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models

• URL: http://arxiv.org/abs/2410.11208v2
• Date: Wed, 30 Oct 2024 01:16:45 GMT
• Title: DreamSteerer: Enhancing Source Image Conditioned Editability using Personalized Diffusion Models
• Authors: Zhengyang Yu, Zhaoyuan Yang, Jing Zhang,
• Abstract summary: Recent text-to-image personalization methods have shown great promise in teaching a diffusion model user-specified concepts. A promising extension is personalized editing, namely to edit an image using personalized concepts. We propose DreamSteerer, a plug-in method for augmenting existing T2I personalization methods.
• Score: 7.418186319496487
• License:
• Abstract: Recent text-to-image personalization methods have shown great promise in teaching a diffusion model user-specified concepts given a few images for reusing the acquired concepts in a novel context. With massive efforts being dedicated to personalized generation, a promising extension is personalized editing, namely to edit an image using personalized concepts, which can provide a more precise guidance signal than traditional textual guidance. To address this, a straightforward solution is to incorporate a personalized diffusion model with a text-driven editing framework. However, such a solution often shows unsatisfactory editability on the source image. To address this, we propose DreamSteerer, a plug-in method for augmenting existing T2I personalization methods. Specifically, we enhance the source image conditioned editability of a personalized diffusion model via a novel Editability Driven Score Distillation (EDSD) objective. Moreover, we identify a mode trapping issue with EDSD, and propose a mode shifting regularization with spatial feature guided sampling to avoid such an issue. We further employ two key modifications to the Delta Denoising Score framework that enable high-fidelity local editing with personalized concepts. Extensive experiments validate that DreamSteerer can significantly improve the editability of several T2I personalization baselines while being computationally efficient.

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