Image-to-Image
Transformers
PyTorch
swinv2
physics
pde
neural-operator
cfd
fluid-dynamics
poseidon
scOT
Instructions to use empirischtech/poseidon-b-joint-cyl-kh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use empirischtech/poseidon-b-joint-cyl-kh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-to-image", model="empirischtech/poseidon-b-joint-cyl-kh")# Load model directly from transformers import AutoImageProcessor, ScOT processor = AutoImageProcessor.from_pretrained("empirischtech/poseidon-b-joint-cyl-kh") model = ScOT.from_pretrained("empirischtech/poseidon-b-joint-cyl-kh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 368221ddfdcef68f70512e3b4e14ab75f58df70d30213c73e0c92a7b729cbde4
- Size of remote file:
- 631 MB
- SHA256:
- cae4afa2985ccb19a3e0519af70ec700f387205d70b959f537561df6807133c5
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