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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| 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 |