83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
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import torch
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import network
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class ModuleTypeOFT(network.ModuleType):
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def create_module(self, net: network.Network, weights: network.NetworkWeights):
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if all(x in weights.w for x in ["oft_blocks"]):
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return NetworkModuleOFT(net, weights)
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return None
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# adapted from https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
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class NetworkModuleOFT(network.NetworkModule):
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def __init__(self, net: network.Network, weights: network.NetworkWeights):
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super().__init__(net, weights)
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self.oft_blocks = weights.w["oft_blocks"]
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self.alpha = weights.w["alpha"]
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self.dim = self.oft_blocks.shape[0]
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self.num_blocks = self.dim
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#if type(self.alpha) == torch.Tensor:
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# self.alpha = self.alpha.detach().numpy()
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if "Linear" in self.sd_module.__class__.__name__:
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self.out_dim = self.sd_module.out_features
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elif "Conv" in self.sd_module.__class__.__name__:
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self.out_dim = self.sd_module.out_channels
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self.constraint = self.alpha * self.out_dim
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self.block_size = self.out_dim // self.num_blocks
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self.oft_multiplier = self.multiplier()
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# replace forward method of original linear rather than replacing the module
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# self.org_forward = self.sd_module.forward
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# self.sd_module.forward = self.forward
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def get_weight(self):
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block_Q = self.oft_blocks - self.oft_blocks.transpose(1, 2)
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norm_Q = torch.norm(block_Q.flatten())
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new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
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block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
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I = torch.eye(self.block_size, device=self.oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
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block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
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block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
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R = torch.block_diag(*block_R_weighted)
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return R
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def calc_updown(self, orig_weight):
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oft_blocks = self.oft_blocks.to(orig_weight.device, dtype=orig_weight.dtype)
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block_Q = oft_blocks - oft_blocks.transpose(1, 2)
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norm_Q = torch.norm(block_Q.flatten())
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new_norm_Q = torch.clamp(norm_Q, max=self.constraint)
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block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
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I = torch.eye(self.block_size, device=oft_blocks.device).unsqueeze(0).repeat(self.num_blocks, 1, 1)
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block_R = torch.matmul(I + block_Q, (I - block_Q).inverse())
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block_R_weighted = self.oft_multiplier * block_R + (1 - self.oft_multiplier) * I
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R = torch.block_diag(*block_R_weighted)
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#R = self.get_weight().to(orig_weight.device, dtype=orig_weight.dtype)
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# W = R*W_0
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updown = orig_weight + R
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output_shape = [R.size(0), orig_weight.size(1)]
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return self.finalize_updown(updown, orig_weight, output_shape)
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# def forward(self, x, y=None):
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# x = self.org_forward(x)
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# if self.oft_multiplier == 0.0:
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# return x
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# R = self.get_weight().to(x.device, dtype=x.dtype)
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# if x.dim() == 4:
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# x = x.permute(0, 2, 3, 1)
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# x = torch.matmul(x, R)
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# x = x.permute(0, 3, 1, 2)
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# else:
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# x = torch.matmul(x, R)
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# return x
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