Instructions to use neulab/t-dig with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use neulab/t-dig with PEFT:
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- Notebooks
- Google Colab
- Kaggle
Add pipeline tag, link to paper and project page
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by nielsr HF Staff - opened
README.md
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license: apache-2.0
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base_model:
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- Qwen/Qwen3.5-4B
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- Qwen/Qwen3.5-9B
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- Qwen/Qwen3.5-35B-A3B
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library_name: peft
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tags:
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- text-to-image
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- prompt-expansion
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LoRA adapters for the prompt-expansion policies of *T-DIG: Text-conditioned Diversity for Image Generation* (under review).
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Given a prompt and a request for what should differ, a *generation* policy writes K prompts, one per image, that vary the requested axis and keep everything else in the prompt. An *editing* policy instead writes one edit instruction per image of an existing set. The text-to-image (T2I) model is not changed. Each policy is trained with GRPO against one T2I model; it works with others, but best with its own.
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## Contents
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## Citation
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Citation information will be added after review.
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---
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base_model:
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- Qwen/Qwen3.5-4B
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- Qwen/Qwen3.5-9B
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- Qwen/Qwen3.5-35B-A3B
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library_name: peft
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- text-to-image
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- prompt-expansion
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LoRA adapters for the prompt-expansion policies of *T-DIG: Text-conditioned Diversity for Image Generation* (under review).
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Paper: [T-DIG on Hugging Face](https://huggingface.co/papers/2610.29003) · Project page: [image-study.org/t-dig](https://image-study.org/t-dig/)
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Given a prompt and a request for what should differ, a *generation* policy writes K prompts, one per image, that vary the requested axis and keep everything else in the prompt. An *editing* policy instead writes one edit instruction per image of an existing set. The text-to-image (T2I) model is not changed. Each policy is trained with GRPO against one T2I model; it works with others, but best with its own.
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## Contents
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## Citation
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Citation information will be added after review.
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