用文本反转技术让模型学会并迁移个人摄影风格
Personalized Image Filter: Mastering Your Photographic Style
- 基于预训练扩散模型，通过文本反转学习摄影概念
- 能精准提取并保留参考图的风格特征，不破坏内容
- 适合想自动复现自己摄影风格的创作者使用
摄影风格是知名摄影师作品魅力的来源，由特定的摄影概念构成。但学习和迁移这种风格需深入理解图像从原始状态到后期处理的演变过程。以往方法或无法从参考图像中学习有意义的摄影概念，或无法保留内容图像的结构。为此，我们提出个性化图像滤镜（PIF）。基于预训练的文生图扩散模型，生成先验使PIF能够学习摄影概念的平均外观，并根据文本提示调整其表现。PIF利用文本反转技术，通过优化摄影概念对应的提示词来学习参考图像的摄影风格。实验表明，PIF在提取和迁移多种摄影风格方面表现优异。项目页面：https://pif.pages.dev/
原文摘要 · Abstract (English)
Photographic style, as a composition of certain photographic concepts, is the charm behind renowned photographers. But learning and transferring photographic style need a profound understanding of how the photo is edited from the unknown original appearance. Previous works either fail to learn meaningful photographic concepts from reference images, or cannot preserve the content of the content image. To tackle these issues, we proposed a Personalized Image Filter (PIF). Based on a pretrained text-to-image diffusion model, the generative prior enables PIF to learn the average appearance of photographic concepts, as well as how to adjust them according to text prompts. PIF then learns the photographic style of reference images with the textual inversion technique, by optimizing the prompts for the photographic concepts. PIF shows outstanding performance in extracting and transferring various kinds of photographic style. Project page: https://pif.pages.dev/
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