Diffusers
Safetensors
English
RFD4ProteinaPipeline
biology
protein-design
biomolecular-design
structure-generation
flow-matching
annotated-cif
Instructions to use nvidia/RFD4-Proteina-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nvidia/RFD4-Proteina-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nvidia/RFD4-Proteina-Diffusers", 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
Model card: examples are generated with these weights
Browse files
README.md
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> You also need the matching [`nvidia/RFD4-Proteina-prep`](https://huggingface.co/nvidia/RFD4-Proteina-prep),
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> which supplies the chirality and element features this checkpoint's encoder requires.
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> **The `examples/*/design.cif` outputs were generated with an earlier release** and are kept
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> for reference. They will not reproduce under these weights: the model, the vocabulary and the
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> sampler recipe all changed. The `input.cif` files remain valid.
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## At a glance
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> You also need the matching [`nvidia/RFD4-Proteina-prep`](https://huggingface.co/nvidia/RFD4-Proteina-prep),
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> which supplies the chirality and element features this checkpoint's encoder requires.
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## At a glance
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