JanusCoder: Towards a Foundational Visual-Programmatic Interface for
  Code Intelligence
The scope of neural code intelligence is rapidly expanding beyond text-based
source code to encompass the rich visual outputs that programs generate. This
visual dimension is critical for advanced applications like flexible content
generation and precise, program-driven editing of visualizations. However,
progress has been impeded by the scarcity of high-quality multimodal code data,
a bottleneck stemming from challenges in synthesis and quality assessment. To
address these challenges, we make contributions from both a data and modeling
perspective. We first introduce a complete synthesis toolkit that leverages
reciprocal synergies between data modalities to efficiently produce a
large-scale, high-quality corpus spanning from standard charts to complex
interactive web UIs and code-driven animations. Leveraging this toolkit, we
construct JanusCode-800K, the largest multimodal code corpus to date. This
powers the training of our models, JanusCoder and JanusCoderV, which establish
a visual-programmatic interface for generating code from textual instructions,
visual inputs, or a combination of both. Our unified model is a departure from
existing approaches that build specialized models for isolated tasks. Extensive
experiments on both text-centric and vision-centric coding tasks demonstrate
the superior performance of the JanusCoder series, with our 7B to 14B scale
models approaching or even exceeding the performance of commercial models.
Furthermore, extensive analysis provides key insights into harmonizing
programmatic logic with its visual expression. Our code and checkpoints will
are available at https://github.com/InternLM/JanusCoder.
6902211f646208eac0d1f6eb
Qiushi Sun
claimed_verified
2025-10-30T14:40:40.247Z
6064a0eeb1703ddba0d458b9
https://cdn-avatars.huggingface.co/v1/production/uploads/1617207525789-noauth.png
Qiushi
QiushiSun
user
QiushiSun
6902211f646208eac0d1f6ec
Jingyang Gong
6902211f646208eac0d1f6ed
Yang Liu
6902211f646208eac0d1f6ee
Qiaosheng Chen
claimed_verified
2025-10-30T14:40:37.936Z
619ef3f253061ce00477b09e
https://cdn-avatars.huggingface.co/v1/production/uploads/619ef3f253061ce00477b09e/FknZhgQhV2_3aTqIKVsTo.jpeg
Qiaosheng Chen
cqsss
user
cqsss
6902211f646208eac0d1f6ef
Lei Li
6902211f646208eac0d1f6f0
Kai Chen
6902211f646208eac0d1f6f1
Qipeng Guo
6902211f646208eac0d1f6f2
Ben Kao
6902211f646208eac0d1f6f3
Fei Yuan
JanusCoder: Towards a Foundational Visual-Programmatic Interface for
  Code Intelligence
The scope of neural code intelligence is rapidly expanding beyond text-based
source code to encompass the rich visual outputs that programs generate. This
visual dimension is critical for advanced applications like flexible content
generation and precise, program-driven editing of visualizations. However,
progress has been impeded by the scarcity of high-quality multimodal code data,
a bottleneck stemming from challenges in synthesis and quality assessment. To
address these challenges, we make contributions from both a data and modeling
perspective. We first introduce a complete synthesis toolkit that leverages
reciprocal synergies between data modalities to efficiently produce a
large-scale, high-quality corpus spanning from standard charts to complex
interactive web UIs and code-driven animations. Leveraging this toolkit, we
construct JanusCode-800K, the largest multimodal code corpus to date. This
powers the training of our models, JanusCoder and JanusCoderV, which establish
a visual-programmatic interface for generating code from textual instructions,
visual inputs, or a combination of both. Our unified model is a departure from
existing approaches that build specialized models for isolated tasks. Extensive
experiments on both text-centric and vision-centric coding tasks demonstrate
the superior performance of the JanusCoder series, with our 7B to 14B scale
models approaching or even exceeding the performance of commercial models.
Furthermore, extensive analysis provides key insights into harmonizing
programmatic logic with its visual expression. Our code and checkpoints will
are available at https://github.com/InternLM/JanusCoder.
A unified multimodal code corpus and model enable high-quality code generation from both text and visual inputs, outperforming commercial models.
neural code intelligence visual outputs content generation program-driven editing multimodal code data synthesis toolkit JanusCode-800K JanusCoder JanusCoderV visual-programmatic interface text-centric vision-centric coding tasks harmonizing programmatic logic visual expression