| 42 | """ |
| 43 | |
| 44 | def __init__( |
| 45 | self, |
| 46 | embedding_dim: int, |
| 47 | num_embeddings: Optional[int] = None, |
| 48 | output_dim: Optional[int] = None, |
| 49 | norm_elementwise_affine: bool = False, |
| 50 | norm_eps: float = 1e-5, |
| 51 | chunk_dim: int = 0, |
| 52 | ): |
| 53 | super().__init__() |
| 54 | |
| 55 | self.chunk_dim = chunk_dim |
| 56 | output_dim = output_dim or embedding_dim * 2 |
| 57 | |
| 58 | if num_embeddings is not None: |
| 59 | self.emb = nn.Embedding(num_embeddings, embedding_dim) |
| 60 | else: |
| 61 | self.emb = None |
| 62 | |
| 63 | self.silu = nn.SiLU() |
| 64 | self.linear = nn.Linear(embedding_dim, output_dim) |
| 65 | self.norm = nn.LayerNorm(output_dim // 2, norm_eps, norm_elementwise_affine) |
| 66 | |
| 67 | def forward( |
| 68 | self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, temb: Optional[torch.Tensor] = None |