Image-to-Image
Transformers
Safetensors
bagel
image-editing
image-generation
interleaved-generation
vbvr-pro
Instructions to use Video-Reason/VBVR-Pro-BAGEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Video-Reason/VBVR-Pro-BAGEL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="Video-Reason/VBVR-Pro-BAGEL")# Load model directly from transformers import Bagel model = Bagel.from_pretrained("Video-Reason/VBVR-Pro-BAGEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f744f169fc8c7d1c8fcfdfdbd86251d518c801084880373c77e1cf31bcff827e
- Size of remote file:
- 335 MB
- SHA256:
- afc8e28272cd15db3919bacdb6918ce9c1ed22e96cb12c4d5ed0fba823529e38
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