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import glob
import os
import torch
from modules import devices
class HypernetworkModule(torch.nn.Module):
def __init__(self, dim, state_dict):
super().__init__()
self.linear1 = torch.nn.Linear(dim, dim * 2)
self.linear2 = torch.nn.Linear(dim * 2, dim)
self.load_state_dict(state_dict, strict=True)
self.to(devices.device)
def forward(self, x):
return x + (self.linear2(self.linear1(x)))
class Hypernetwork:
filename = None
name = None
def __init__(self, filename):
self.filename = filename
self.name = os.path.splitext(os.path.basename(filename))[0]
self.layers = {}
state_dict = torch.load(filename, map_location='cpu')
for size, sd in state_dict.items():
self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
def load_hypernetworks(path):
res = {}
for filename in glob.iglob(path + '**/*.pt', recursive=True):
hn = Hypernetwork(filename)
res[hn.name] = hn
return res
def apply(self, x, context=None, mask=None, original=None):
if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork:
if context.shape[1] == 77 and CrossAttention.noise_cond:
context = context + (torch.randn_like(context) * 0.1)
h_k, h_v = CrossAttention.hypernetwork[context.shape[2]]
k = self.to_k(h_k(context))
v = self.to_v(h_v(context))
else:
k = self.to_k(context)
v = self.to_v(context)
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