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
metadata
pipeline_tag: image-to-text
tags:
- image-captioning
- anime
license: other
license_name: shadowlilac-extension-bsd-3
license_link: LICENSE
datasets:
- shadowlilac/anime
Visor - Natural language Anime Tagging
Visor is a natural-language-based image tagging model based on the BLIP model architecture.
Potential Use cases can be to caption anime images for training diffusion models