44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
# Copyright 2025 ByteDance and/or its affiliates.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import torch
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from torch import nn
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class SinusPositionEmbedding(nn.Module):
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def __init__(self, dim):
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super().__init__()
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self.dim = dim
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def forward(self, x, scale=1000):
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device = x.device
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half_dim = self.dim // 2
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emb = math.log(10000) / (half_dim - 1)
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emb = torch.exp(torch.arange(half_dim, device=device).float() * -emb)
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emb = scale * x.unsqueeze(1) * emb.unsqueeze(0)
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emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
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return emb
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class TimestepEmbedding(nn.Module):
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def __init__(self, dim, freq_embed_dim=256):
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super().__init__()
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self.time_embed = SinusPositionEmbedding(freq_embed_dim)
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self.time_mlp = nn.Sequential(nn.Linear(freq_embed_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
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def forward(self, timestep): # noqa: F821
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time_hidden = self.time_embed(timestep)
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time_hidden = time_hidden.to(timestep.dtype)
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time = self.time_mlp(time_hidden) # b d
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return time |