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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- Using an inverse pairing strategy, real surgical videos are reconstructed in a simulator to create paired data for training a video diffusion model for sim-to-real translation, with intermediate scene graphs serving as a constraint regularizer.
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- We demonstrate improved phase recognition, unsupervised style transfer, and strong generalisation to unseen interaction geometries.
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## 🛠 Setup
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```bash
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- Using an inverse pairing strategy, real surgical videos are reconstructed in a simulator to create paired data for training a video diffusion model for sim-to-real translation, with intermediate scene graphs serving as a constraint regularizer.
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- We demonstrate improved phase recognition, unsupervised style transfer, and strong generalisation to unseen interaction geometries.
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## 🛠 Setup
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```bash
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