Image-to-Text
Transformers
Safetensors
English
qwen2_5_vl
image-text-to-text
vision-language-model
visual-storytelling
chain-of-thought
grounded-text-generation
cross-frame-consistency
storytelling
contrastive-learning
reinforcement-learning
entity-reidentification
Eval Results (legacy)
text-generation-inference
Instructions to use daniel3303/QwenStoryteller2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use daniel3303/QwenStoryteller2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="daniel3303/QwenStoryteller2")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("daniel3303/QwenStoryteller2") model = AutoModelForImageTextToText.from_pretrained("daniel3303/QwenStoryteller2") - Notebooks
- Google Colab
- Kaggle
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README.md
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## Citation
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```bibtex
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title={Entity Re-identification in Visual Storytelling via Contrastive Reinforcement Learning},
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author={Daniel Oliveira and David Martins de Matos},
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year={2025},
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eprint={[PAPER_ID]},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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@misc{oliveira2025storyreasoningdatasetusingchainofthought,
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title={StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation},
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## Citation
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```bibtex
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@misc{oliveira2025storyreasoningdatasetusingchainofthought,
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title={StoryReasoning Dataset: Using Chain-of-Thought for Scene Understanding and Grounded Story Generation},
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