PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
Authors: name: Shijie Huang, name: Yiren Song, name: Yuxuan Zhang, name: Hailong Guo, name: Xueyin Wang, name: Mike Zheng Shou, name: Jiaming Liu

We introduce PhotoDoodle, a novel image editing framework designed to
facilitate photo doodling by enabling artists to overlay decorative elements
onto photographs. Photo doodling is challenging because the inserted elements
must appear seamlessly integrated with the background, requiring realistic
blending, perspective alignment, and contextual coherence. Additionally, the
background must be preserved without distortion, and the artist's unique style
must be captured efficiently from limited training data. These requirements are
not addressed by previous methods that primarily focus on global style transfer
or regional inpainting. The proposed method, PhotoDoodle, employs a two-stage
training strategy. Initially, we train a general-purpose image editing model,
OmniEditor, using large-scale data. Subsequently, we fine-tune this model with
EditLoRA using a small, artist-curated dataset of before-and-after image pairs
to capture distinct editing styles and techniques. To enhance consistency in
the generated results, we introduce a positional encoding reuse mechanism.
Additionally, we release a PhotoDoodle dataset featuring six high-quality
styles. Extensive experiments demonstrate the advanced performance and
robustness of our method in customized image editing, opening new possibilities
for artistic creation.