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c3d0544 | 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 | # SPDX-FileCopyrightText: Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES.
# SPDX-FileCopyrightText: All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# 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.
from typing import Type
import torch
from torch import Tensor, nn
class PositionalEmbedding(nn.Module):
"""
A module for generating positional embeddings based on timesteps.
Parameters:
-----------
num_channels : int
Number of channels for the embedding.
"""
def __init__(self, num_channels: int):
super().__init__()
self.num_channels = num_channels
freqs = torch.pi * torch.arange(
start=1, end=self.num_channels // 2 + 1, dtype=torch.float32
)
self.register_buffer("freqs", freqs)
def forward(self, x: Tensor) -> Tensor:
x = x.view(-1).outer(self.freqs.to(x.dtype))
x = torch.cat([x.cos(), x.sin()], dim=1)
return x
class OneHotEmbedding(nn.Module):
"""
A module for generating one-hot embeddings based on timesteps.
Parameters:
-----------
num_channels : int
Number of channels for the embedding.
"""
def __init__(self, num_channels: int):
super().__init__()
self.num_channels = num_channels
ind = torch.arange(num_channels)
ind = ind.view(1, len(ind))
self.register_buffer("indices", ind)
def forward(self, t: Tensor) -> Tensor:
ind = t * (self.num_channels - 1)
return torch.clamp(1 - torch.abs(ind - self.indices), min=0)
class ModEmbedNet(nn.Module):
"""
A network that generates a timestep embedding and processes it with an MLP.
Parameters:
-----------
max_time : float, optional
Maximum input time. The inputs to `forward` is should be in the range [0, max_time].
dim : int, optional
The dimensionality of the time embedding.
depth : int, optional
The number of layers in the MLP.
activation_fn:
The activation function, default GELU.
method : str, optional
The embedding method. Either "sinusoidal" (default) or "onehot".
"""
def __init__(
self,
max_time: float = 1.0,
dim: int = 64,
depth: int = 1,
activation_fn: Type[nn.Module] = nn.GELU,
method: str = "sinusoidal",
):
super().__init__()
self.max_time = max_time
self.method = method
if method == "onehot":
self.onehot_embed = OneHotEmbedding(dim)
elif method == "sinusoidal":
self.sinusoid_embed = PositionalEmbedding(dim)
else:
raise ValueError(f"Embedding '{method}' not supported")
self.dim = dim
blocks = []
for _ in range(depth):
blocks.extend([nn.Linear(dim, dim), activation_fn()])
self.mlp = nn.Sequential(*blocks)
def forward(self, t: Tensor) -> Tensor:
t = t / self.max_time
if self.method == "onehot":
emb = self.onehot_embed(t)
elif self.method == "sinusoidal":
emb = self.sinusoid_embed(t)
return self.mlp(emb)
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