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4900749 | 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 | # Copyright 2025 Dhruv Nair. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
RFDiffusion3 Scheduler.
A thin diffusers-compatible wrapper around the foundry EDM noise schedule
and stochastic sampling logic from `rfd3.model.inference_sampler`.
"""
from typing import Optional
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
# Reuse the original noise schedule and sampling config directly
from rfd3.model.inference_sampler import SampleDiffusionWithMotif
class RFDiffusionScheduler(ConfigMixin):
"""
Diffusers-compatible scheduler wrapping the foundry EDM sampler.
Delegates noise schedule construction and sampling parameters to
`rfd3.model.inference_sampler.SampleDiffusionWithMotif`.
"""
config_name = "config.json"
@register_to_config
def __init__(
self,
num_timesteps: int = 200,
sigma_data: float = 16.0,
s_min: float = 4e-4,
s_max: float = 160.0,
p: float = 7.0,
gamma_0: float = 0.6,
gamma_min: float = 1.0,
noise_scale: float = 1.003,
step_scale: float = 1.5,
):
# Instantiate the foundry sampler with matching parameters
self._sampler = SampleDiffusionWithMotif(
num_timesteps=num_timesteps,
sigma_data=sigma_data,
s_min=s_min,
s_max=s_max,
p=p,
gamma_0=gamma_0,
gamma_min=gamma_min,
noise_scale=noise_scale,
step_scale=step_scale,
)
@property
def sampler(self) -> SampleDiffusionWithMotif:
return self._sampler
def get_noise_schedule(self, device: torch.device = None) -> torch.Tensor:
"""
Construct the EDM noise schedule using the foundry implementation.
Returns:
torch.Tensor: Noise schedule [num_timesteps] from high to low noise.
"""
return self._sampler._construct_inference_noise_schedule(
device=device or torch.device("cpu")
)
def get_initial_noise_level(self, device: torch.device = None) -> torch.Tensor:
"""Get the first (largest) noise level from the schedule."""
return self.get_noise_schedule(device=device)[0]
def step(
self,
xyz_pred: torch.Tensor,
xyz_noisy: torch.Tensor,
c_t_minus_1: torch.Tensor,
c_t: torch.Tensor,
motif_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""
Perform one Euler denoising step matching the foundry sampler.
The foundry ``sample_diffusion_like_af3`` does NOT clamp motif
coordinates after the Euler update — it relies on noise injection
having zeroed epsilon for motif atoms so the model's delta is ~0
there. We replicate that behaviour here.
Args:
xyz_pred: Model's denoised prediction X_denoised_L [B, L, 3]
xyz_noisy: Noise-injected coordinates X_noisy_L [B, L, 3]
c_t_minus_1: Previous noise level
c_t: Next (lower) noise level
motif_mask: Boolean mask for fixed positions (True = fixed) [L]
(unused — kept for API compatibility)
Returns:
Updated coordinates X_L [B, L, 3]
"""
gamma = self._sampler.gamma_0 if c_t > self._sampler.gamma_min else 0.0
t_hat = c_t_minus_1 * (gamma + 1.0)
delta_L = (xyz_noisy - xyz_pred) / t_hat
d_t = c_t - t_hat
xyz_next = xyz_noisy + self._sampler.step_scale * d_t * delta_L
return xyz_next
def add_noise(
self,
xyz: torch.Tensor,
c_t_minus_1: torch.Tensor,
c_t: torch.Tensor,
motif_mask: Optional[torch.Tensor] = None,
) -> tuple[torch.Tensor, torch.Tensor]:
"""
Inject stochastic noise before the model call, matching the foundry sampler.
Args:
xyz: Current coordinates X_L [B, L, 3]
c_t_minus_1: Previous noise level
c_t: Current (next lower) noise level
motif_mask: Boolean mask for fixed positions (True = fixed) [L]
Returns:
Tuple of (noisy coordinates X_noisy_L, t_hat scalar)
"""
gamma = self._sampler.gamma_0 if c_t > self._sampler.gamma_min else 0.0
t_hat = c_t_minus_1 * (gamma + 1.0)
noise_std = self._sampler.noise_scale * torch.sqrt(t_hat**2 - c_t_minus_1**2)
epsilon = noise_std * torch.randn_like(xyz)
if motif_mask is not None:
epsilon[:, motif_mask] = 0.0
xyz_noisy = xyz + epsilon
return xyz_noisy, t_hat
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