63 lines
2.0 KiB
Python
63 lines
2.0 KiB
Python
# // Copyright (c) 2025 Bytedance Ltd. 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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from typing import Optional, Union
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import torch
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from diffusers.models.embeddings import get_timestep_embedding
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from torch import nn
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def emb_add(emb1: torch.Tensor, emb2: Optional[torch.Tensor]):
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return emb1 if emb2 is None else emb1 + emb2
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class TimeEmbedding(nn.Module):
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def __init__(
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self,
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sinusoidal_dim: int,
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hidden_dim: int,
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output_dim: int,
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):
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super().__init__()
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self.sinusoidal_dim = sinusoidal_dim
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self.proj_in = nn.Linear(sinusoidal_dim, hidden_dim)
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self.proj_hid = nn.Linear(hidden_dim, hidden_dim)
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self.proj_out = nn.Linear(hidden_dim, output_dim)
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self.act = nn.SiLU()
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def forward(
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self,
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timestep: Union[int, float, torch.IntTensor, torch.FloatTensor],
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device: torch.device,
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dtype: torch.dtype,
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) -> torch.FloatTensor:
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if not torch.is_tensor(timestep):
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timestep = torch.tensor([timestep], device=device, dtype=dtype)
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if timestep.ndim == 0:
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timestep = timestep[None]
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emb = get_timestep_embedding(
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timesteps=timestep,
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embedding_dim=self.sinusoidal_dim,
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flip_sin_to_cos=False,
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downscale_freq_shift=0,
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)
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emb = emb.to(dtype)
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emb = self.proj_in(emb)
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emb = self.act(emb)
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emb = self.proj_hid(emb)
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emb = self.act(emb)
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emb = self.proj_out(emb)
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return emb
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