import numbers from typing import Dict, Optional, Tuple import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from diffusers.utils import is_torch_version if is_torch_version(">=", "2.1.0"): LayerNorm = nn.LayerNorm else: # Has optional bias parameter compared to torch layer norm # TODO: replace with torch layernorm once min required torch version >= 2.1 class LayerNorm(nn.Module): def __init__(self, dim, eps: float = 1e-5, elementwise_affine: bool = True, bias: bool = True): super().__init__() self.eps = eps if isinstance(dim, numbers.Integral): dim = (dim,) self.dim = torch.Size(dim) if elementwise_affine: self.weight = nn.Parameter(torch.ones(dim)) self.bias = nn.Parameter(torch.zeros(dim)) if bias else None else: self.weight = None self.bias = None def forward(self, input): return F.layer_norm(input, self.dim, self.weight, self.bias, self.eps) class RMSNorm(nn.Module): def __init__(self, dim, eps: float, elementwise_affine: bool = True): super().__init__() self.eps = eps if isinstance(dim, numbers.Integral): dim = (dim,) self.dim = torch.Size(dim) if elementwise_affine: self.weight = nn.Parameter(torch.ones(dim)) else: self.weight = None def forward(self, hidden_states): input_dtype = hidden_states.dtype variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True) hidden_states = hidden_states * torch.rsqrt(variance + self.eps) if self.weight is not None: # convert into half-precision if necessary if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype) hidden_states = hidden_states * self.weight hidden_states = hidden_states.to(input_dtype) return hidden_states class AdaLayerNormContinuous(nn.Module): def __init__( self, embedding_dim: int, conditioning_embedding_dim: int, # NOTE: It is a bit weird that the norm layer can be configured to have scale and shift parameters # because the output is immediately scaled and shifted by the projected conditioning embeddings. # Note that AdaLayerNorm does not let the norm layer have scale and shift parameters. # However, this is how it was implemented in the original code, and it's rather likely you should # set `elementwise_affine` to False. elementwise_affine=True, eps=1e-5, bias=True, norm_type="layer_norm", ): super().__init__() self.silu = nn.SiLU() self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias) if norm_type == "layer_norm": self.norm = LayerNorm(embedding_dim, eps, elementwise_affine, bias) elif norm_type == "rms_norm": self.norm = RMSNorm(embedding_dim, eps, elementwise_affine) else: raise ValueError(f"unknown norm_type {norm_type}") def forward_with_pad(self, x: torch.Tensor, conditioning_embedding: torch.Tensor, hidden_length=None) -> torch.Tensor: assert hidden_length is not None emb = self.linear(self.silu(conditioning_embedding).to(x.dtype)) batch_emb = torch.zeros_like(x).repeat(1, 1, 2) i_sum = 0 num_stages = len(hidden_length) for i_p, length in enumerate(hidden_length): batch_emb[:, i_sum:i_sum+length] = emb[i_p::num_stages][:,None] i_sum += length batch_scale, batch_shift = torch.chunk(batch_emb, 2, dim=2) x = self.norm(x) * (1 + batch_scale) + batch_shift return x def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor, hidden_length=None) -> torch.Tensor: # convert back to the original dtype in case `conditioning_embedding`` is upcasted to float32 (needed for hunyuanDiT) if hidden_length is not None: return self.forward_with_pad(x, conditioning_embedding, hidden_length) emb = self.linear(self.silu(conditioning_embedding).to(x.dtype)) scale, shift = torch.chunk(emb, 2, dim=1) x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :] return x class AdaLayerNormZero(nn.Module): r""" Norm layer adaptive layer norm zero (adaLN-Zero). Parameters: embedding_dim (`int`): The size of each embedding vector. num_embeddings (`int`): The size of the embeddings dictionary. """ def __init__(self, embedding_dim: int, num_embeddings: Optional[int] = None): super().__init__() self.emb = None self.silu = nn.SiLU() self.linear = nn.Linear(embedding_dim, 6 * embedding_dim, bias=True) self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6) def forward_with_pad( self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, class_labels: Optional[torch.LongTensor] = None, hidden_dtype: Optional[torch.dtype] = None, emb: Optional[torch.Tensor] = None, hidden_length: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: # x: [bs, seq_len, dim] if self.emb is not None: emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype) emb = self.linear(self.silu(emb)) batch_emb = torch.zeros_like(x).repeat(1, 1, 6) i_sum = 0 num_stages = len(hidden_length) for i_p, length in enumerate(hidden_length): batch_emb[:, i_sum:i_sum+length] = emb[i_p::num_stages][:,None] i_sum += length batch_shift_msa, batch_scale_msa, batch_gate_msa, batch_shift_mlp, batch_scale_mlp, batch_gate_mlp = batch_emb.chunk(6, dim=2) x = self.norm(x) * (1 + batch_scale_msa) + batch_shift_msa return x, batch_gate_msa, batch_shift_mlp, batch_scale_mlp, batch_gate_mlp def forward( self, x: torch.Tensor, timestep: Optional[torch.Tensor] = None, class_labels: Optional[torch.LongTensor] = None, hidden_dtype: Optional[torch.dtype] = None, emb: Optional[torch.Tensor] = None, hidden_length: Optional[torch.Tensor] = None, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: if hidden_length is not None: return self.forward_with_pad(x, timestep, class_labels, hidden_dtype, emb, hidden_length) if self.emb is not None: emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype) emb = self.linear(self.silu(emb)) shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(6, dim=1) x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None] return x, gate_msa, shift_mlp, scale_mlp, gate_mlp