mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-15 07:20:31 +08:00
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
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import glob
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import os
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import torch
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from modules import devices
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class HypernetworkModule(torch.nn.Module):
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def __init__(self, dim, state_dict):
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super().__init__()
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self.linear1 = torch.nn.Linear(dim, dim * 2)
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self.linear2 = torch.nn.Linear(dim * 2, dim)
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self.load_state_dict(state_dict, strict=True)
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self.to(devices.device)
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def forward(self, x):
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return x + (self.linear2(self.linear1(x)))
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class Hypernetwork:
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filename = None
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name = None
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def __init__(self, filename):
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self.filename = filename
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self.name = os.path.splitext(os.path.basename(filename))[0]
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self.layers = {}
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state_dict = torch.load(filename, map_location='cpu')
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for size, sd in state_dict.items():
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self.layers[size] = (HypernetworkModule(size, sd[0]), HypernetworkModule(size, sd[1]))
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def load_hypernetworks(path):
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res = {}
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for filename in glob.iglob(path + '**/*.pt', recursive=True):
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hn = Hypernetwork(filename)
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res[hn.name] = hn
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return res
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def apply(self, x, context=None, mask=None, original=None):
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if CrossAttention.hypernetwork is not None and context.shape[2] in CrossAttention.hypernetwork:
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if context.shape[1] == 77 and CrossAttention.noise_cond:
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context = context + (torch.randn_like(context) * 0.1)
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h_k, h_v = CrossAttention.hypernetwork[context.shape[2]]
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k = self.to_k(h_k(context))
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v = self.to_v(h_v(context))
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else:
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k = self.to_k(context)
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v = self.to_v(context)
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