Instructions to use SsharvienKumar/SWoMo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SsharvienKumar/SWoMo with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image, export_to_video # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SsharvienKumar/SWoMo", dtype=torch.bfloat16, device_map="cuda") pipe.to("cuda") prompt = "A man with short gray hair plays a red electric guitar." image = load_image( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/guitar-man.png" ) output = pipe(image=image, prompt=prompt).frames[0] export_to_video(output, "output.mp4") - Notebooks
- Google Colab
- Kaggle
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# SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation (MICCAI 2026)
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## ⭐ Acknowledgement
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Thanks for the following projects and theoretical works that we have either used or inspired from:
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- [SG2VID](https://github.com/MECLabTUDA/SG2VID)
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- [VQGAN](https://github.com/CompVis/taming-transformers)
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license: cc-by-4.0
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---
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# SWoMo: Neuro-Symbolic World Model for Cataract Surgery Simulation (MICCAI 2026)
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## ⭐ Acknowledgement
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Thanks for the following projects and theoretical works that we have either used or inspired from:
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- [SG2VID](https://github.com/MECLabTUDA/SG2VID)
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- [VQGAN](https://github.com/CompVis/taming-transformers)
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