Image-to-Video
Diffusers
Safetensors
MLX
mflux
mlx-gen
bernini_renderer
apple-silicon
bf16
bernini
wan
video-generation
video-editing
reference-to-video
Instructions to use AbstractFramework/bernini-r-1.3B-diffusers-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AbstractFramework/bernini-r-1.3B-diffusers-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir bernini-r-1.3B-diffusers-bf16 AbstractFramework/bernini-r-1.3B-diffusers-bf16
- mflux
How to use AbstractFramework/bernini-r-1.3B-diffusers-bf16 with mflux:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
- Xet hash:
- f7b92491858c0923bd01aeb98a554de12094096a12d0195f578ff70b421702b7
- Size of remote file:
- 16.8 MB
- SHA256:
- 20a46ac256746594ed7e1e3ef733b83fbc5a6f0922aa7480eda961743de080ef
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.