mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
172 lines
6.4 KiB
Python
172 lines
6.4 KiB
Python
import torch
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import network
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from modules import devices
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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 kohya's implementation 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 "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.org_module: list[torch.Module] = [self.sd_module]
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self.org_weight = self.org_module[0].weight.to(self.org_module[0].weight.device, copy=True)
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#self.org_weight = self.org_module[0].weight.to(devices.cpu, copy=True)
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init_multiplier = self.multiplier() * self.calc_scale()
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self.last_multiplier = init_multiplier
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self.R = self.get_weight(self.oft_blocks, init_multiplier)
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self.merged_weight = self.merge_weight()
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self.apply_to()
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self.merged = False
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# weights_backup = getattr(self.org_module[0], 'network_weights_backup', None)
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# if weights_backup is None:
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# self.org_module[0].network_weights_backup = self.org_weight
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def merge_weight(self):
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#org_sd = self.org_module[0].state_dict()
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R = self.R.to(self.org_weight.device, dtype=self.org_weight.dtype)
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if self.org_weight.dim() == 4:
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weight = torch.einsum("oihw, op -> pihw", self.org_weight, R)
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else:
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weight = torch.einsum("oi, op -> pi", self.org_weight, R)
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#org_sd['weight'] = weight
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# replace weight
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#self.org_module[0].load_state_dict(org_sd)
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return weight
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pass
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def replace_weight(self, new_weight):
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org_sd = self.org_module[0].state_dict()
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org_sd['weight'] = new_weight
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self.org_module[0].load_state_dict(org_sd)
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self.merged = True
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def restore_weight(self):
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org_sd = self.org_module[0].state_dict()
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org_sd['weight'] = self.org_weight
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self.org_module[0].load_state_dict(org_sd)
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self.merged = False
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# replace forward method of original linear rather than replacing the module
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# how do we revert this to unload the weights?
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def apply_to(self):
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self.org_forward = self.org_module[0].forward
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#self.org_module[0].forward = self.forward
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self.org_module[0].register_forward_pre_hook(self.pre_forward_hook)
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self.org_module[0].register_forward_hook(self.forward_hook)
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def get_weight(self, oft_blocks, multiplier=None):
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multiplier = multiplier.to(oft_blocks.device, dtype=oft_blocks.dtype)
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constraint = self.constraint.to(oft_blocks.device, dtype=oft_blocks.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=constraint)
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block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
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m_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(m_I + block_Q, (m_I - block_Q).inverse())
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block_R_weighted = multiplier * block_R + (1 - multiplier) * m_I
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R = torch.block_diag(*block_R_weighted)
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#R = torch.block_diag(*block_R)
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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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#R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
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##self.R = R
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#R = self.R.to(orig_weight.device, dtype=orig_weight.dtype)
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##self.R = R
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#if orig_weight.dim() == 4:
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# weight = torch.einsum("oihw, op -> pihw", orig_weight, R)
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#else:
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# weight = torch.einsum("oi, op -> pi", orig_weight, R)
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#updown = orig_weight @ R
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#updown = weight
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updown = torch.zeros_like(orig_weight, device=orig_weight.device, dtype=orig_weight.dtype)
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#updown = orig_weight
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output_shape = orig_weight.shape
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orig_weight = self.merged_weight.to(orig_weight.device, dtype=orig_weight.dtype)
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#output_shape = self.oft_blocks.shape
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return self.finalize_updown(updown, orig_weight, output_shape)
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def pre_forward_hook(self, module, input):
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multiplier = self.multiplier() * self.calc_scale()
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if not multiplier==self.last_multiplier or not self.merged:
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#if multiplier != self.last_multiplier or not self.merged:
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self.R = self.get_weight(self.oft_blocks, multiplier)
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self.last_multiplier = multiplier
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self.merged_weight = self.merge_weight()
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self.replace_weight(self.merged_weight)
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#elif not self.merged:
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# self.replace_weight(self.merged_weight)
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def forward_hook(self, module, args, output):
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pass
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#output = output * self.multiplier() * self.calc_scale()
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#if len(args) > 0:
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# y = args[0]
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# output = output + y
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#return output
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#if self.merged:
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# pass
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#self.restore_weight()
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#print(f'Forward hook in {self.network_key} called')
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#x = output
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#R = self.R.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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# def forward(self, x, y=None):
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# x = self.org_forward(x)
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# if self.multiplier() == 0.0:
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# return x
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# # calculating R here is excruciatingly slow
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# #R = self.get_weight().to(x.device, dtype=x.dtype)
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# R = self.R.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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