HYPNOS : Highly Precise Foreground-focused Diffusion Finetuning for Inanimate Objects

• URL: http://arxiv.org/abs/2410.14265v1
• Date: Fri, 18 Oct 2024 08:20:37 GMT
• Title: HYPNOS : Highly Precise Foreground-focused Diffusion Finetuning for Inanimate Objects
• Authors: Oliverio Theophilus Nathanael, Jonathan Samuel Lumentut, Nicholas Hans Muliawan, Edbert Valencio Angky, Felix Indra Kurniadi, Alfi Yusrotis Zakiyyah, Jeklin Harefa,
• Abstract summary: A robust diffusion model is determined by its ability to perform near-perfect reconstruction of certain product outcomes. The current prominent diffusion-based finetuning technique falls short in maintaining the foreground object consistency. We propose Hypnos, a highly precise foreground-focused diffusion finetuning technique.
• Score: 1.706656684496508
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• Abstract: In recent years, personalized diffusion-based text-to-image generative tasks have been a hot topic in computer vision studies. A robust diffusion model is determined by its ability to perform near-perfect reconstruction of certain product outcomes given few related input samples. Unfortunately, the current prominent diffusion-based finetuning technique falls short in maintaining the foreground object consistency while being constrained to produce diverse backgrounds in the image outcome. In the worst scenario, the overfitting issue may occur, meaning that the foreground object is less controllable due to the condition above, for example, the input prompt information is transferred ambiguously to both foreground and background regions, instead of the supposed background region only. To tackle the issues above, we proposed Hypnos, a highly precise foreground-focused diffusion finetuning technique. On the image level, this strategy works best for inanimate object generation tasks, and to do so, Hypnos implements two main approaches, namely: (i) a content-centric prompting strategy and (ii) the utilization of our additional foreground-focused discriminative module. The utilized module is connected with the diffusion model and finetuned with our proposed set of supervision mechanism. Combining the strategies above yielded to the foreground-background disentanglement capability of the diffusion model. Our experimental results showed that the proposed strategy gave a more robust performance and visually pleasing results compared to the former technique. For better elaborations, we also provided extensive studies to assess the fruitful outcomes above, which reveal how personalization behaves in regard to several training conditions.

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