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arXiv · 2408.09248

FPGA: Flexible Portrait Generation Approach

Zhaoli Deng·Fanyi Wang·Junkang Zhang·Fan Chen·Meng Zhang·Wendong Zhang·Wen Liu·Zhenpeng Mi
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Abstract

Portrait Fidelity Generation is a prominent research area in generative models.Current methods face challenges in generating full-body images with low-resolution faces, especially in multi-ID photo phenomenon.To tackle these issues, we propose a comprehensive system called FPGA and construct a million-level multi-modal dataset IDZoom for training.FPGA consists of Multi-Mode Fusion training strategy (MMF) and DDIM Inversion based ID Restoration inference framework (DIIR). The MMF aims to activate the specified ID in the specified facial region. The DIIR aims to address the issue of face artifacts while keeping the background.Furthermore, DIIR is plug-and-play and can be applied to any diffusion-based portrait generation method to enhance their performance. DIIR is also capable of performing face-swapping tasks and is applicable to stylized faces as well.To validate the effectiveness of FPGA, we conducted extensive comparative and ablation experiments. The experimental results demonstrate that FPGA has significant advantages in both subjective and objective metrics, and achieves controllable generation in multi-ID scenarios. In addition, we accelerate the inference speed to within 2.5 seconds on a single L20 graphics card mainly based on our well designed reparameterization method, RepControlNet.

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Zhaoli Deng, Fanyi Wang, Junkang Zhang, Fan Chen, Meng Zhang, Wendong Zhang, Wen Liu, Zhenpeng Mi. 2024-08-17. FPGA: Flexible Portrait Generation Approach. https://arxiv.org/abs/2408.09248

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