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5ab5b72 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | # Copyright 2026 The HuggingFace Team. All rights reserved.
# Licensed under the Apache License, Version 2.0.
"""Optional modular entry points for the separately installed RFD4 CIF preparation package."""
from pathlib import Path
from typing import ClassVar
import torch
from diffusers.modular_pipelines import (
AutoPipelineBlocks,
InputParam,
ModularPipelineBlocks,
OutputParam,
)
class RFD4PrepareCIFStep(ModularPipelineBlocks):
"""Prepare a single annotated input without loading generator weights."""
_requirements: ClassVar = {"rfd4_proteina_prep": "==0.1.0", "atomworks": "==3.0.3"}
@property
def description(self):
return "Prepare tensors and retain the atom mapping for annotated CIF export."
@property
def inputs(self):
return [
InputParam(
"cif_path",
required=True,
type_hint=str,
description="Local annotated CIF filename.",
),
InputParam(
"seed",
default=42,
type_hint=int,
description="Seed for expandable segments.",
),
InputParam(
"device",
default="cpu",
type_hint=str,
description="Device for returned tensors.",
),
InputParam(
"path_length_cutoff",
default=64,
type_hint=int,
description="Topology path-length cutoff.",
),
]
@property
def intermediate_outputs(self):
return [
OutputParam(
"features",
type_hint=dict,
description="Batched tensors, with coordinates in nanometers.",
),
OutputParam(
"atom_array", description="Original atom mapping; retain it for export."
),
]
def __call__(self, components, state):
block_state = self.get_block_state(state)
path = Path(block_state.cif_path)
if not path.is_file():
raise FileNotFoundError(path)
if type(block_state.seed) is not int:
raise TypeError("seed must be an integer.")
if (
type(block_state.path_length_cutoff) is not int
or block_state.path_length_cutoff < 1
):
raise ValueError("path_length_cutoff must be a positive integer.")
from rfd4_proteina_prep import prepare_cif
features, atoms = prepare_cif(
path,
seed=block_state.seed,
device=torch.device(block_state.device),
path_length_cutoff=block_state.path_length_cutoff,
)
block_state.features = features
block_state.atom_array = atoms
self.set_block_state(state, block_state)
return components, state
class RFD4ExportCIFStep(ModularPipelineBlocks):
"""Export one decoded design through the same package used for preparation."""
_requirements: ClassVar = {"rfd4_proteina_prep": "==0.1.0", "atomworks": "==3.0.3"}
@property
def description(self):
return "Write a new annotated CIF, preserving conditions and insertion mapping; never overwrite a file."
@property
def inputs(self):
return [
InputParam(
"pipeline_output",
required=True,
description="RFD4ProteinaPipelineOutput with torch tensors.",
),
InputParam(
"features",
required=True,
type_hint=dict,
description="The features used for this design batch.",
),
InputParam(
"atom_array",
required=True,
description="Atom mapping returned during preparation.",
),
InputParam(
"output_path",
required=True,
type_hint=str,
description="New .cif filename; parent must exist.",
),
InputParam(
"row",
default=0,
type_hint=int,
description="Batch row to export using this atom mapping.",
),
]
@property
def intermediate_outputs(self):
return [
OutputParam(
"cif_path",
type_hint=str,
description="Written CIF path; coordinates are in angstroms.",
)
]
def __call__(self, components, state):
block_state = self.get_block_state(state)
path = Path(block_state.output_path)
if path.suffix.lower() != ".cif":
raise ValueError("output_path must end in .cif.")
if path.exists() or path.is_symlink():
raise FileExistsError(path)
if not path.parent.is_dir():
raise FileNotFoundError(path.parent)
batch_size, tokens = block_state.features["padding_mask"].shape
if type(block_state.row) is not int or not 0 <= block_state.row < batch_size:
raise ValueError("row must select a batch row in features.")
output = block_state.pipeline_output
for name, shape in {
"coords_nm": (batch_size, tokens, 37, 3),
"residue_type": (batch_size, tokens),
"atom_mask": (batch_size, tokens, 37),
"insertion_logits": (batch_size, tokens, tokens),
}.items():
value = getattr(output, name)
if not isinstance(value, torch.Tensor) or tuple(value.shape) != shape:
raise ValueError(
f"pipeline_output.{name} must be a torch tensor with shape {shape}."
)
from rfd4_proteina_prep import write_cif
block_state.cif_path = write_cif(
output,
block_state.features,
block_state.atom_array,
path,
row=block_state.row,
)
self.set_block_state(state, block_state)
return components, state
class RFD4CIFBlocks(AutoPipelineBlocks):
"""Prepare a CIF, or export when pipeline_output is provided; never perform model inference."""
block_classes: ClassVar = [RFD4ExportCIFStep, RFD4PrepareCIFStep]
block_names: ClassVar = ["export", "prepare"]
block_trigger_inputs: ClassVar = ["pipeline_output", None]
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