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
Commit History
3 days ago
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Drop transformer options that are constant for the release
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Split the scheduler per modality, move recycle_depths to the pipeline config, rename feature-embedding keys
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4 days ago
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Update to the 2026-10-19 release (LoRA merged); pipeline call signature with explicit inputs
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5 days ago
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Flatten the transformer state dict: no model. prefix, sequence_head
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Update to the 2026-10-15 unified release (147-class, latent 32)
Verified signatureConverted from nvidia/RFD4-Proteina at 976e0c90 (weights_sha256 5f00cdcd...) with converter revision 0291146a. The sampler recipe is read from the RFD4 repository's own configs/generation/inference_base.yaml rather than a transcribed default, so the scheduler records tsr_matched_noise, t_lim_ode 0.95 and tsr_sigma 12.0. Requires the unreleased rfd4-unified-checkpoint branch and the matching prep package; the existing example outputs are from the previous release and will not reproduce.36e146b
8 days ago
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Publication layout: model card, provenance, examples, assets
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