Instructions to use shadowlilac/visor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shadowlilac/visor 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="shadowlilac/visor")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("shadowlilac/visor") model = AutoModelForMultimodalLM.from_pretrained("shadowlilac/visor") - Notebooks
- Google Colab
- Kaggle
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README.md
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pipeline_tag: image-to-text
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tags:
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- image-captioning
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license: other
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license_name: shadowlilac-extension-bsd-3
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license_link: LICENSE
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---
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pipeline_tag: image-to-text
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tags:
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- image-captioning
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- anime
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license: other
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license_name: shadowlilac-extension-bsd-3
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license_link: LICENSE
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datasets:
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- shadowlilac/anime
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---
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# Visor - Natural language Anime Tagging
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Visor is a natural-language-based image tagging model based on the BLIP model architecture.
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Potential Use cases can be to caption anime images for training diffusion models
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