JanusCoder: Towards a Foundational Visual-Programmatic Interface for
  Code Intelligence
Authors: name: Qiushi Sun, name: Jingyang Gong, name: Yang Liu, name: Qiaosheng Chen, name: Lei Li, name: Kai Chen, name: Qipeng Guo, name: Ben Kao, name: Fei Yuan
Organization: Intern Large Models

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.