PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
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.
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Shijie Huang
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2025-02-24T13:13:17.369Z
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Shijie Huang
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Humor
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Yiren Song
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2025-02-24T13:13:23.453Z
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Yiren Song
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yiren98
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Yuxuan Zhang
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2025-03-11T08:23:27.337Z
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yuxuan
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zzyx
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Hailong Guo
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Xueyin Wang
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2025-02-24T13:17:06.041Z
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Wang Xueying
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Forever-rover
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Mike Zheng Shou
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2025-02-24T13:17:12.124Z
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Mike Zheng Shou
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AnalMom
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Jiaming Liu
claimed_verified
2025-06-02T13:40:39.635Z
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LIU JIAMING
user
jamesliu1217
PhotoDoodle: Learning Artistic Image Editing from Few-Shot Pairwise Data
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.
PhotoDoodle is an image editing framework that uses a two-stage训练 strategy and EditLoRA for realistic blending and perspective alignment, enabling the overlay of decorative elements on photographs with preserved background and artist-specific styles.
PhotoDoodle OmniEditor EditLoRA positional encoding reuse mechanism