| 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
|
|
|
| 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:
|
|
|
| if self.weight.dtype in [torch.float16, torch.bfloat16]:
|
| hidden_states = hidden_states.to(self.weight.dtype)
|
| hidden_states = hidden_states * self.weight
|
| else:
|
| hidden_states = hidden_states.to(input_dtype)
|
|
|
| return hidden_states
|
|
|
| class IPAFluxAttnProcessor2_0(nn.Module):
|
| """Attention processor used typically in processing the SD3-like self-attention projections."""
|
|
|
| def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, timestep_range=None):
|
| super().__init__()
|
|
|
| self.hidden_size = hidden_size
|
| self.cross_attention_dim = cross_attention_dim
|
| self.scale = scale
|
| self.num_tokens = num_tokens
|
|
|
| self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
| self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
|
|
|
| self.norm_added_k = RMSNorm(128, eps=1e-5, elementwise_affine=False)
|
| self.norm_added_v = RMSNorm(128, eps=1e-5, elementwise_affine=False)
|
| self.timestep_range = timestep_range
|
|
|
| def __call__(
|
| self,
|
| num_heads,
|
| query,
|
| image_emb: torch.FloatTensor,
|
| t: torch.FloatTensor
|
| ) -> torch.FloatTensor:
|
|
|
| if self.timestep_range is not None:
|
| if t[0] > self.timestep_range[0] or t[0] < self.timestep_range[1]:
|
| return None
|
|
|
| ip_hidden_states = image_emb
|
| ip_hidden_states_key_proj = self.to_k_ip(ip_hidden_states)
|
| ip_hidden_states_value_proj = self.to_v_ip(ip_hidden_states)
|
|
|
| ip_hidden_states_key_proj = rearrange(ip_hidden_states_key_proj, 'B L (H D) -> B H L D', H=num_heads)
|
| ip_hidden_states_value_proj = rearrange(ip_hidden_states_value_proj, 'B L (H D) -> B H L D', H=num_heads)
|
|
|
| ip_hidden_states_key_proj = self.norm_added_k(ip_hidden_states_key_proj)
|
| ip_hidden_states_value_proj = self.norm_added_v(ip_hidden_states_value_proj)
|
|
|
| ip_hidden_states = F.scaled_dot_product_attention(query.to(image_emb.device).to(image_emb.dtype),
|
| ip_hidden_states_key_proj,
|
| ip_hidden_states_value_proj,
|
| dropout_p=0.0, is_causal=False)
|
|
|
| ip_hidden_states = rearrange(ip_hidden_states, "B H L D -> B L (H D)", H=num_heads)
|
| ip_hidden_states = ip_hidden_states.to(query.dtype).to(query.device)
|
|
|
| return self.scale * ip_hidden_states |
