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  ```
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  ## Changes from DeTi*k*Zify<sub>v1</sub>
 
 
 
 
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  ### Architecture
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  Similar to DeTi*k*Zify<sub>v1</sub>, DeTi*k*Zify<sub>v2</sub> uses a SigLIP
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  For pretraining, we switch from MetaFig to the much larger
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  [ArXivCap](https://huggingface.co/datasets/MMInstruction/ArxivCap) dataset and
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  extract 1 million (figure, caption, OCR) tuples for pretraining the modality
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- connector. For fine-tuning, we create a new DaTi*k*Z<sub>v3</sub> dataset (to
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- be released soon) with over 450k Ti*k*Z drawings.
 
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  We also train a new model called
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  [UltraSketch](https://huggingface.co/nllg/ultrasketch) to generate synthetic
 
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  ```
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  ## Changes from DeTi*k*Zify<sub>v1</sub>
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+ We document all changes between DeTikZify<sub>v1</sub> and
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+ DeTikZify<sub>v2</sub> in our paper, "[TikZero: Zero-Shot Text-Guided Graphics
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+ Program Synthesis](https://arxiv.org/abs/2503.11509)". For convenience, they
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+ are also listed below.
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  ### Architecture
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  Similar to DeTi*k*Zify<sub>v1</sub>, DeTi*k*Zify<sub>v2</sub> uses a SigLIP
 
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  For pretraining, we switch from MetaFig to the much larger
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  [ArXivCap](https://huggingface.co/datasets/MMInstruction/ArxivCap) dataset and
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  extract 1 million (figure, caption, OCR) tuples for pretraining the modality
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+ connector. For fine-tuning, we create the new
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+ [DaTi*k*Z<sub>v3</sub>](https://huggingface.co/datasets/nllg/datikz-v3) dataset
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+ with over 450k Ti*k*Z drawings.
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  We also train a new model called
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  [UltraSketch](https://huggingface.co/nllg/ultrasketch) to generate synthetic