Instructions to use zer0int/CLIP-GmP-ViT-L-14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zer0int/CLIP-GmP-ViT-L-14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="zer0int/CLIP-GmP-ViT-L-14") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("zer0int/CLIP-GmP-ViT-L-14") model = AutoModelForZeroShotImageClassification.from_pretrained("zer0int/CLIP-GmP-ViT-L-14") - Notebooks
- Google Colab
- Kaggle
which file to use with flux
#6
by Ai11Ali - opened
what is the current best file to use with for portrait and text on flux
See the README where it says "Looking for a Text Encoder for Flux.1 (or SD3, SDXL, SD, ...) to replace CLIP-L?": https://huggingface.co/zer0int/CLIP-GmP-ViT-L-14
Those 5 sentences are all I know - because it really depends, there isn't a general "is better than" model. Best to get both models and experiment. Enjoy! :)