| import torch |
| import torch.nn as nn |
|
|
| |
|
|
|
|
| class TimestepEmbedder(nn.Module): |
| """ |
| Embeds scalar timesteps into vector representations. |
| """ |
|
|
| def __init__(self, dim, frequency_embedding_size, max_period): |
| super().__init__() |
| self.mlp = nn.Sequential( |
| nn.Linear(frequency_embedding_size, dim), |
| nn.SiLU(), |
| nn.Linear(dim, dim), |
| ) |
| self.dim = dim |
| self.max_period = max_period |
| assert dim % 2 == 0, 'dim must be even.' |
|
|
| with torch.autocast('cuda', enabled=False): |
| self.freqs = nn.Buffer( |
| 1.0 / (10000**(torch.arange(0, frequency_embedding_size, 2, dtype=torch.float32) / |
| frequency_embedding_size)), |
| persistent=False) |
| freq_scale = 10000 / max_period |
| self.freqs = freq_scale * self.freqs |
|
|
| def timestep_embedding(self, t): |
| """ |
| Create sinusoidal timestep embeddings. |
| :param t: a 1-D Tensor of N indices, one per batch element. |
| These may be fractional. |
| :param dim: the dimension of the output. |
| :param max_period: controls the minimum frequency of the embeddings. |
| :return: an (N, D) Tensor of positional embeddings. |
| """ |
| |
|
|
| args = t[:, None].float() * self.freqs[None] |
| embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) |
| return embedding |
|
|
| def forward(self, t): |
| t_freq = self.timestep_embedding(t).to(t.dtype) |
| t_emb = self.mlp(t_freq) |
| return t_emb |
|
|
