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Zero
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import torch
from torch import nn, Tensor
from einops import rearrange
from concept_attention.flux.src.flux.modules.layers import Modulation, QKNorm
from concept_attention.flux.src.flux.math import attention
NUM_IMAGE_PATCHES = 4096
class ModifiedSingleStreamBlock(nn.Module):
"""
A DiT block with parallel linear layers as described in
https://arxiv.org/abs/2302.05442 and adapted modulation interface.
"""
def __init__(
self,
hidden_size: int,
num_heads: int,
mlp_ratio: float = 4.0,
qk_scale: float | None = None
):
super().__init__()
self.hidden_dim = hidden_size
self.num_heads = num_heads
head_dim = hidden_size // num_heads
self.scale = qk_scale or head_dim**-0.5
self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
# qkv and mlp_in
self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
# proj and mlp_out
self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
self.norm = QKNorm(head_dim)
self.hidden_size = hidden_size
self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
self.mlp_act = nn.GELU(approximate="tanh")
self.modulation = Modulation(hidden_size, double=False)
def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
mod, _ = self.modulation(vec)
# Perform img-text self attention
x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads)
q, k = self.norm(q, k, v)
# compute attention
attn = attention(q, k, v, pe=pe)
# compute activation in mlp stream, cat again and run second linear layer
output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
return x + mod.gate * output
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