Feature Extraction
Transformers
Safetensors
PyTorch
Diffusers
chemistry
foundation models
AI4Science
materials
molecules
transformer
Instructions to use ibm-research/materials.smi_ssed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-research/materials.smi_ssed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ibm-research/materials.smi_ssed")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ibm-research/materials.smi_ssed", dtype="auto") - Diffusers
How to use ibm-research/materials.smi_ssed 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.smi_ssed", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2f97c61ef084ea1055f3459beb1b79d4a808510425a246dd141fd9193c02d29
- Size of remote file:
- 1.35 GB
- SHA256:
- d5354f4cab219dd780128f26428518edf101fc99ae87eeceedb64eda9172ae4e
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