DeTikZify<br>_{^{Synthesizing Graphics Programs for Scientific Figures and Sketches with TikZ}}

Creating high-quality scientific figures can be time-consuming and challenging, even though sketching ideas on paper is relatively easy. Furthermore, recreating existing figures that are not stored in formats preserving semantic information is equally complex. To tackle this problem, we introduce DeTikZify, a novel multimodal language model that automatically synthesizes scientific figures as semantics-preserving TikZ graphics programs based on sketches and existing figures. We also introduce an MCTS-based inference algorithm that enables DeTikZify to iteratively refine its outputs without the need for additional training.

https://github.com/potamides/DeTikZify/assets/53401822/203d2853-0b5c-4a2b-9d09-3ccb65880cd3

News

• 2025-06-30: We release DeTikZify_v2.5 (8b), our latest model, which builds on DeTikZify_v2 using reinforment learning from self-feedback (RLSF). We make it the new default model in our Hugging Face Space. Check out the model card for more information.
• 2025-06-26: TikZero was accepted as a highlight paper at ICCV 2025!
• 2025-03-17: We release TikZero adapters which plug directly into DeTikZify_v2 (8b) and enable zero-shot text-conditioning, and TikZero+ with additional end-to-end fine-tuning. For more information see our paper and usage examples below.
• 2024-12-05: We release DeTikZify_v2 (8b), our new DeTikZify model that surpasses all previous versions in our evaluation. Check out the model card for more information.
• 2024-09-24: DeTikZify was accepted at NeurIPS 2024 as a spotlight paper!

Installation

[!TIP] If you encounter difficulties with installation or inference on your own hardware, consider visiting our Hugging Face Space (please note that restarting the space can take up to 30 minutes). Should you experience long queues, you have the option to duplicate it with a paid private GPU runtime or [run it locally](https://huggingface.co/spaces/nllg/DeTikZify