Calligrapher: Freestyle Text Image Customization
We introduce Calligrapher, a novel diffusion-based framework that
innovatively integrates advanced text customization with artistic typography
for digital calligraphy and design applications. Addressing the challenges of
precise style control and data dependency in typographic customization, our
framework incorporates three key technical contributions. First, we develop a
self-distillation mechanism that leverages the pre-trained text-to-image
generative model itself alongside the large language model to automatically
construct a style-centric typography benchmark. Second, we introduce a
localized style injection framework via a trainable style encoder, which
comprises both Qformer and linear layers, to extract robust style features from
reference images. An in-context generation mechanism is also employed to
directly embed reference images into the denoising process, further enhancing
the refined alignment of target styles. Extensive quantitative and qualitative
evaluations across diverse fonts and design contexts confirm Calligrapher's
accurate reproduction of intricate stylistic details and precise glyph
positioning. By automating high-quality, visually consistent typography,
Calligrapher surpasses traditional models, empowering creative practitioners in
digital art, branding, and contextual typographic design.
68634673588cea0da970c862
Yue Ma
68634673588cea0da970c863
Qingyan Bai
68634673588cea0da970c864
Hao Ouyang
68634673588cea0da970c865
Ka Leong Cheng
68634673588cea0da970c866
Qiuyu Wang
68634673588cea0da970c867
Hongyu Liu
68634673588cea0da970c868
Zichen Liu
68634673588cea0da970c869
Haofan Wang
claimed_verified
2025-07-02T08:02:46.992Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1669187672174-637745113a63a2983ffbde13.jpeg
Haofan Wang
user
wanghaofan
68634673588cea0da970c86a
Jingye Chen
claimed_verified
2025-07-01T10:14:05.849Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/PUJ-N2cQxgEmDGfyjajyA.jpeg
JingyeChen22
user
JingyeChen22
68634673588cea0da970c86b
Yujun Shen
68634673588cea0da970c86c
Qifeng Chen
Calligrapher: Freestyle Text Image Customization
We introduce Calligrapher, a novel diffusion-based framework that
innovatively integrates advanced text customization with artistic typography
for digital calligraphy and design applications. Addressing the challenges of
precise style control and data dependency in typographic customization, our
framework incorporates three key technical contributions. First, we develop a
self-distillation mechanism that leverages the pre-trained text-to-image
generative model itself alongside the large language model to automatically
construct a style-centric typography benchmark. Second, we introduce a
localized style injection framework via a trainable style encoder, which
comprises both Qformer and linear layers, to extract robust style features from
reference images. An in-context generation mechanism is also employed to
directly embed reference images into the denoising process, further enhancing
the refined alignment of target styles. Extensive quantitative and qualitative
evaluations across diverse fonts and design contexts confirm Calligrapher's
accurate reproduction of intricate stylistic details and precise glyph
positioning. By automating high-quality, visually consistent typography,
Calligrapher surpasses traditional models, empowering creative practitioners in
digital art, branding, and contextual typographic design.
Calligrapher, a diffusion-based framework, integrates advanced text customization with artistic typography using self-distillation, a localized style injection framework, and in-context generation to achieve high-quality, visually consistent typography.
diffusion-based framework self-distillation mechanism text-to-image generative model large language model style-centric typography benchmark localized style injection framework trainable style encoder Qformer linear layers in-context generation mechanism denoising process glyph positioning