Instructions to use ibm-research/materials.mhg-ged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ibm-research/materials.mhg-ged 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.mhg-ged", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Transformers
How to use ibm-research/materials.mhg-ged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ibm-research/materials.mhg-ged")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ibm-research/materials.mhg-ged", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ibm-research/materials.mhg-ged: direct link, hf CLI and curl.
- Browser
- Download file 300 MB
-
https://huggingface.co/ibm-research/materials.mhg-ged/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ibm-research/materials.mhg-ged/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ibm-research/materials.mhg-ged/resolve/main/pytorch_model.bin
300 MB
- Xet hash:
- 48081a127d216da0c449d52bfd06fe9344f976f56e452dd8e48b57182e6e0f99
- Size of remote file:
- 300 MB
- SHA256:
- 697d31996fe366a560722017dee4b8e2fa9ae83bcc5489540653d0fed1408259
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