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 # Warning: Pipeline type "image-to-image" 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-image", model="Video-Reason/VBVR-Pro-BAGEL")# pip install -U transformers accelerate # Load model directly from transformers import Bagel model = Bagel.from_pretrained("Video-Reason/VBVR-Pro-BAGEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download vit_config.json from Video-Reason/VBVR-Pro-BAGEL: direct link, hf CLI and curl.
- Browser
- Download file 205 Bytes
-
https://huggingface.co/Video-Reason/VBVR-Pro-BAGEL/resolve/main/vit_config.json
- Command line
-
hf download hf://Video-Reason/VBVR-Pro-BAGEL/vit_config.json
-
curl -L -o vit_config.json https://huggingface.co/Video-Reason/VBVR-Pro-BAGEL/resolve/main/vit_config.json
205 Bytes
| { | |
| "hidden_size": 1152, | |
| "image_size": 980, | |
| "intermediate_size": 4304, | |
| "model_type": "siglip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 27, | |
| "patch_size": 14 | |
| } | |