Instructions to use ibm-research/materials.pos-egnn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ibm-research/materials.pos-egnn with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ibm-research/materials.pos-egnn", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - AnemoI
How to use ibm-research/materials.pos-egnn with AnemoI:
from anemoi.inference.runners.default import DefaultRunner from anemoi.inference.config.run import RunConfiguration # Create Configuration config = RunConfiguration(checkpoint = {"huggingface":"ibm-research/materials.pos-egnn"}) # Load Runner runner = DefaultRunner(config) - MedVAE
How to use ibm-research/materials.pos-egnn with MedVAE:
# 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
Download pytorch_model.bin from ibm-research/materials.pos-egnn: direct link, hf CLI and curl.
- Browser
- Download file 26.5 MB
-
https://huggingface.co/ibm-research/materials.pos-egnn/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ibm-research/materials.pos-egnn/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ibm-research/materials.pos-egnn/resolve/main/pytorch_model.bin
26.5 MB
- Xet hash:
- 59e5f8ceaef0fe38ac5a41399a76dda0e45d81e87e7cb565d7ada6e4a2ae5486
- Size of remote file:
- 26.5 MB
- SHA256:
- 5708231e3f13b01cde6d2bef6d813dfb9637e5d10e7b4e267594711883088dfd
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