两种time_embedding

class GaussianFourierProjection(nn.Module):
  """Gaussian Fourier embeddings for noise levels."""

  def __init__(self, embedding_size=256, scale=1.0):
    super().__init__()
    self.W = nn.Parameter(torch.randn(embedding_size) * scale, requires_grad=False)

  def forward(self, x):
    x_proj = x[:, None] * self.W[None, :] * 2 * np.pi
    return torch.cat([torch.sin(x_proj), torch.cos(x_proj)], dim=-1)

2，SinusoidalPosEmb

class SinusoidalPosEmb(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
        emb = x[:, None] * emb[None, :]
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb