Publication layout: model card, provenance, examples, assets
Browse files
README.md
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@@ -52,10 +52,10 @@ from huggingface_hub import hf_hub_download
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from diffusers import RFD4ProteinaPipeline
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from rfd4_proteina_prep import prepare_cif, write_cif
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pipe = RFD4ProteinaPipeline.from_pretrained("nvidia/RFD4-Proteina-
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# An annotated CIF says what to hold fixed and what to design: here a PD-L1 target and a 50-120 residue binder.
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input_cif = hf_hub_download("nvidia/RFD4-Proteina-
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features, atoms = prepare_cif(input_cif, seed=42, device=pipe.device)
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output = pipe(features, generator=torch.Generator("cuda:0").manual_seed(42))
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from diffusers import RFD4ProteinaPipeline
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from rfd4_proteina_prep import prepare_cif, write_cif
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pipe = RFD4ProteinaPipeline.from_pretrained("nvidia/RFD4-Proteina-Diffusers", dtype=torch.bfloat16).to("cuda:0")
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# An annotated CIF says what to hold fixed and what to design: here a PD-L1 target and a 50-120 residue binder.
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input_cif = hf_hub_download("nvidia/RFD4-Proteina-Diffusers", "examples/protein_binder/input.cif")
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features, atoms = prepare_cif(input_cif, seed=42, device=pipe.device)
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output = pipe(features, generator=torch.Generator("cuda:0").manual_seed(42))
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