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import math
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import os
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from typing import Dict, List, Optional, Tuple, Type, Union
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from diffusers import AutoencoderKL
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from transformers import CLIPTextModel
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import numpy as np
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import torch
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import re
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RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_")
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class LoRAModule(torch.nn.Module):
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"""
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replaces forward method of the original Linear, instead of replacing the original Linear module.
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"""
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def __init__(
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self,
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lora_name,
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org_module: torch.nn.Module,
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multiplier=1.0,
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lora_dim=4,
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alpha=1,
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dropout=None,
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rank_dropout=None,
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module_dropout=None,
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):
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"""if alpha == 0 or None, alpha is rank (no scaling)."""
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super().__init__()
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self.lora_name = lora_name
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if org_module.__class__.__name__ == "Conv2d":
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in_dim = org_module.in_channels
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out_dim = org_module.out_channels
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else:
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in_dim = org_module.in_features
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out_dim = org_module.out_features
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self.lora_dim = lora_dim
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if org_module.__class__.__name__ == "Conv2d":
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kernel_size = org_module.kernel_size
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stride = org_module.stride
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padding = org_module.padding
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self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False)
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self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False)
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else:
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self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False)
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self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False)
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if type(alpha) == torch.Tensor:
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alpha = alpha.detach().float().numpy()
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alpha = self.lora_dim if alpha is None or alpha == 0 else alpha
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self.scale = alpha / self.lora_dim
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self.register_buffer("alpha", torch.tensor(alpha))
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torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5))
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torch.nn.init.zeros_(self.lora_up.weight)
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self.multiplier = multiplier
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self.org_module = org_module
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self.dropout = dropout
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self.rank_dropout = rank_dropout
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self.module_dropout = module_dropout
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def apply_to(self):
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self.org_forward = self.org_module.forward
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self.org_module.forward = self.forward
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del self.org_module
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def forward(self, x):
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org_forwarded = self.org_forward(x)
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if self.module_dropout is not None and self.training:
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if torch.rand(1) < self.module_dropout:
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return org_forwarded
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lx = self.lora_down(x)
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if self.dropout is not None and self.training:
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lx = torch.nn.functional.dropout(lx, p=self.dropout)
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if self.rank_dropout is not None and self.training:
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mask = torch.rand((lx.size(0), self.lora_dim), device=lx.device) > self.rank_dropout
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if len(lx.size()) == 3:
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mask = mask.unsqueeze(1)
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elif len(lx.size()) == 4:
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mask = mask.unsqueeze(-1).unsqueeze(-1)
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lx = lx * mask
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scale = self.scale * (1.0 / (1.0 - self.rank_dropout))
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else:
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scale = self.scale
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lx = self.lora_up(lx)
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return org_forwarded + lx * self.multiplier * scale
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class LoRAInfModule(LoRAModule):
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def __init__(
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self,
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lora_name,
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org_module: torch.nn.Module,
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multiplier=1.0,
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lora_dim=4,
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alpha=1,
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**kwargs,
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):
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super().__init__(lora_name, org_module, multiplier, lora_dim, alpha)
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self.org_module_ref = [org_module]
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self.enabled = True
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self.text_encoder = False
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if lora_name.startswith("lora_te_"):
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self.regional = False
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self.use_sub_prompt = True
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self.text_encoder = True
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elif "attn2_to_k" in lora_name or "attn2_to_v" in lora_name:
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self.regional = False
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self.use_sub_prompt = True
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elif "time_emb" in lora_name:
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self.regional = False
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self.use_sub_prompt = False
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else:
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self.regional = True
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self.use_sub_prompt = False
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self.network: LoRANetwork = None
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def set_network(self, network):
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self.network = network
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def merge_to(self, sd, dtype, device):
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up_weight = sd["lora_up.weight"].to(torch.float).to(device)
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down_weight = sd["lora_down.weight"].to(torch.float).to(device)
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org_sd = self.org_module.state_dict()
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weight = org_sd["weight"].to(torch.float)
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if len(weight.size()) == 2:
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weight = weight + self.multiplier * (up_weight @ down_weight) * self.scale
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elif down_weight.size()[2:4] == (1, 1):
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weight = (
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weight
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+ self.multiplier
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* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
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* self.scale
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)
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else:
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conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
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weight = weight + self.multiplier * conved * self.scale
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org_sd["weight"] = weight.to(dtype)
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self.org_module.load_state_dict(org_sd)
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def get_weight(self, multiplier=None):
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if multiplier is None:
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multiplier = self.multiplier
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up_weight = self.lora_up.weight.to(torch.float)
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down_weight = self.lora_down.weight.to(torch.float)
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if len(down_weight.size()) == 2:
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weight = self.multiplier * (up_weight @ down_weight) * self.scale
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elif down_weight.size()[2:4] == (1, 1):
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weight = (
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self.multiplier
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* (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3)
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* self.scale
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)
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else:
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conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3)
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weight = self.multiplier * conved * self.scale
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return weight
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def set_region(self, region):
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self.region = region
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self.region_mask = None
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def default_forward(self, x):
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return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
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def forward(self, x):
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if not self.enabled:
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return self.org_forward(x)
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if self.network is None or self.network.sub_prompt_index is None:
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return self.default_forward(x)
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if not self.regional and not self.use_sub_prompt:
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return self.default_forward(x)
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if self.regional:
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return self.regional_forward(x)
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else:
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return self.sub_prompt_forward(x)
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def get_mask_for_x(self, x):
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if len(x.size()) == 4:
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h, w = x.size()[2:4]
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area = h * w
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else:
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area = x.size()[1]
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mask = self.network.mask_dic.get(area, None)
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if mask is None:
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mask_size = (1, x.size()[1]) if len(x.size()) == 2 else (1, *x.size()[1:-1], 1)
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return torch.ones(mask_size, dtype=x.dtype, device=x.device) / self.network.num_sub_prompts
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if len(x.size()) != 4:
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mask = torch.reshape(mask, (1, -1, 1))
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return mask
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def regional_forward(self, x):
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if "attn2_to_out" in self.lora_name:
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return self.to_out_forward(x)
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if self.network.mask_dic is None:
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return self.default_forward(x)
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lx = self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
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mask = self.get_mask_for_x(lx)
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lx = lx * mask
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x = self.org_forward(x)
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x = x + lx
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if "attn2_to_q" in self.lora_name and self.network.is_last_network:
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x = self.postp_to_q(x)
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return x
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def postp_to_q(self, x):
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has_real_uncond = x.size()[0] // self.network.batch_size == 3
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qc = self.network.batch_size
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qc += self.network.batch_size * self.network.num_sub_prompts
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if has_real_uncond:
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qc += self.network.batch_size
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query = torch.zeros((qc, x.size()[1], x.size()[2]), device=x.device, dtype=x.dtype)
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query[: self.network.batch_size] = x[: self.network.batch_size]
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for i in range(self.network.batch_size):
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qi = self.network.batch_size + i * self.network.num_sub_prompts
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query[qi : qi + self.network.num_sub_prompts] = x[self.network.batch_size + i]
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if has_real_uncond:
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query[-self.network.batch_size :] = x[-self.network.batch_size :]
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return query
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def sub_prompt_forward(self, x):
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if x.size()[0] == self.network.batch_size:
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return self.org_forward(x)
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emb_idx = self.network.sub_prompt_index
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if not self.text_encoder:
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emb_idx += self.network.batch_size
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lx = x[emb_idx :: self.network.num_sub_prompts]
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lx = self.lora_up(self.lora_down(lx)) * self.multiplier * self.scale
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x = self.org_forward(x)
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x[emb_idx :: self.network.num_sub_prompts] += lx
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return x
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def to_out_forward(self, x):
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if self.network.is_last_network:
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masks = [None] * self.network.num_sub_prompts
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self.network.shared[self.lora_name] = (None, masks)
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else:
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lx, masks = self.network.shared[self.lora_name]
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x1 = x[self.network.batch_size + self.network.sub_prompt_index :: self.network.num_sub_prompts]
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lx1 = self.lora_up(self.lora_down(x1)) * self.multiplier * self.scale
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if self.network.is_last_network:
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lx = torch.zeros(
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(self.network.num_sub_prompts * self.network.batch_size, *lx1.size()[1:]), device=lx1.device, dtype=lx1.dtype
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)
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self.network.shared[self.lora_name] = (lx, masks)
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lx[self.network.sub_prompt_index :: self.network.num_sub_prompts] += lx1
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masks[self.network.sub_prompt_index] = self.get_mask_for_x(lx1)
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x = self.org_forward(x)
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if not self.network.is_last_network:
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return x
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lx, masks = self.network.shared.pop(self.lora_name)
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has_real_uncond = x.size()[0] // self.network.batch_size == self.network.num_sub_prompts + 2
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out = torch.zeros((self.network.batch_size * (3 if has_real_uncond else 2), *x.size()[1:]), device=x.device, dtype=x.dtype)
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out[: self.network.batch_size] = x[: self.network.batch_size]
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if has_real_uncond:
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out[-self.network.batch_size :] = x[-self.network.batch_size :]
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for i in range(len(masks)):
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if masks[i] is None:
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masks[i] = torch.zeros_like(masks[0])
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mask = torch.cat(masks)
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mask_sum = torch.sum(mask, dim=0) + 1e-4
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for i in range(self.network.batch_size):
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lx1 = lx[i * self.network.num_sub_prompts : (i + 1) * self.network.num_sub_prompts]
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lx1 = lx1 * mask
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lx1 = torch.sum(lx1, dim=0)
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xi = self.network.batch_size + i * self.network.num_sub_prompts
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x1 = x[xi : xi + self.network.num_sub_prompts]
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x1 = x1 * mask
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x1 = torch.sum(x1, dim=0)
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x1 = x1 / mask_sum
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x1 = x1 + lx1
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out[self.network.batch_size + i] = x1
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return out
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def parse_block_lr_kwargs(nw_kwargs):
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down_lr_weight = nw_kwargs.get("down_lr_weight", None)
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mid_lr_weight = nw_kwargs.get("mid_lr_weight", None)
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up_lr_weight = nw_kwargs.get("up_lr_weight", None)
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if down_lr_weight is None and mid_lr_weight is None and up_lr_weight is None:
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return None, None, None
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if down_lr_weight is not None:
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if "," in down_lr_weight:
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down_lr_weight = [(float(s) if s else 0.0) for s in down_lr_weight.split(",")]
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if mid_lr_weight is not None:
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mid_lr_weight = float(mid_lr_weight)
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if up_lr_weight is not None:
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if "," in up_lr_weight:
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up_lr_weight = [(float(s) if s else 0.0) for s in up_lr_weight.split(",")]
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down_lr_weight, mid_lr_weight, up_lr_weight = get_block_lr_weight(
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down_lr_weight, mid_lr_weight, up_lr_weight, float(nw_kwargs.get("block_lr_zero_threshold", 0.0))
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)
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return down_lr_weight, mid_lr_weight, up_lr_weight
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def create_network(
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multiplier: float,
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network_dim: Optional[int],
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network_alpha: Optional[float],
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vae: AutoencoderKL,
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text_encoder: Union[CLIPTextModel, List[CLIPTextModel]],
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unet,
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neuron_dropout: Optional[float] = None,
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**kwargs,
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):
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if network_dim is None:
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network_dim = 4
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if network_alpha is None:
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network_alpha = 1.0
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conv_dim = kwargs.get("conv_dim", None)
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conv_alpha = kwargs.get("conv_alpha", None)
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if conv_dim is not None:
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conv_dim = int(conv_dim)
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if conv_alpha is None:
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conv_alpha = 1.0
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else:
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conv_alpha = float(conv_alpha)
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block_dims = kwargs.get("block_dims", None)
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down_lr_weight, mid_lr_weight, up_lr_weight = parse_block_lr_kwargs(kwargs)
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if block_dims is not None or down_lr_weight is not None or mid_lr_weight is not None or up_lr_weight is not None:
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block_alphas = kwargs.get("block_alphas", None)
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conv_block_dims = kwargs.get("conv_block_dims", None)
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conv_block_alphas = kwargs.get("conv_block_alphas", None)
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block_dims, block_alphas, conv_block_dims, conv_block_alphas = get_block_dims_and_alphas(
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block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha
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)
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block_dims, block_alphas, conv_block_dims, conv_block_alphas = remove_block_dims_and_alphas(
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block_dims, block_alphas, conv_block_dims, conv_block_alphas, down_lr_weight, mid_lr_weight, up_lr_weight
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)
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else:
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block_alphas = None
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conv_block_dims = None
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conv_block_alphas = None
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rank_dropout = kwargs.get("rank_dropout", None)
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if rank_dropout is not None:
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rank_dropout = float(rank_dropout)
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module_dropout = kwargs.get("module_dropout", None)
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if module_dropout is not None:
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module_dropout = float(module_dropout)
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|
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network = LoRANetwork(
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text_encoder,
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unet,
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multiplier=multiplier,
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lora_dim=network_dim,
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alpha=network_alpha,
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dropout=neuron_dropout,
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rank_dropout=rank_dropout,
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module_dropout=module_dropout,
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conv_lora_dim=conv_dim,
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conv_alpha=conv_alpha,
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block_dims=block_dims,
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block_alphas=block_alphas,
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conv_block_dims=conv_block_dims,
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conv_block_alphas=conv_block_alphas,
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varbose=True,
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)
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if up_lr_weight is not None or mid_lr_weight is not None or down_lr_weight is not None:
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network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight)
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return network
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|
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def get_block_dims_and_alphas(
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block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha
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):
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num_total_blocks = LoRANetwork.NUM_OF_BLOCKS * 2 + 1
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|
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def parse_ints(s):
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return [int(i) for i in s.split(",")]
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|
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def parse_floats(s):
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return [float(i) for i in s.split(",")]
|
|
|
|
|
|
if block_dims is not None:
|
|
block_dims = parse_ints(block_dims)
|
|
assert (
|
|
len(block_dims) == num_total_blocks
|
|
), f"block_dims must have {num_total_blocks} elements / block_dimsは{num_total_blocks}個指定してください"
|
|
else:
|
|
print(f"block_dims is not specified. all dims are set to {network_dim} / block_dimsが指定されていません。すべてのdimは{network_dim}になります")
|
|
block_dims = [network_dim] * num_total_blocks
|
|
|
|
if block_alphas is not None:
|
|
block_alphas = parse_floats(block_alphas)
|
|
assert (
|
|
len(block_alphas) == num_total_blocks
|
|
), f"block_alphas must have {num_total_blocks} elements / block_alphasは{num_total_blocks}個指定してください"
|
|
else:
|
|
print(
|
|
f"block_alphas is not specified. all alphas are set to {network_alpha} / block_alphasが指定されていません。すべてのalphaは{network_alpha}になります"
|
|
)
|
|
block_alphas = [network_alpha] * num_total_blocks
|
|
|
|
|
|
if conv_block_dims is not None:
|
|
conv_block_dims = parse_ints(conv_block_dims)
|
|
assert (
|
|
len(conv_block_dims) == num_total_blocks
|
|
), f"conv_block_dims must have {num_total_blocks} elements / conv_block_dimsは{num_total_blocks}個指定してください"
|
|
|
|
if conv_block_alphas is not None:
|
|
conv_block_alphas = parse_floats(conv_block_alphas)
|
|
assert (
|
|
len(conv_block_alphas) == num_total_blocks
|
|
), f"conv_block_alphas must have {num_total_blocks} elements / conv_block_alphasは{num_total_blocks}個指定してください"
|
|
else:
|
|
if conv_alpha is None:
|
|
conv_alpha = 1.0
|
|
print(
|
|
f"conv_block_alphas is not specified. all alphas are set to {conv_alpha} / conv_block_alphasが指定されていません。すべてのalphaは{conv_alpha}になります"
|
|
)
|
|
conv_block_alphas = [conv_alpha] * num_total_blocks
|
|
else:
|
|
if conv_dim is not None:
|
|
print(
|
|
f"conv_dim/alpha for all blocks are set to {conv_dim} and {conv_alpha} / すべてのブロックのconv_dimとalphaは{conv_dim}および{conv_alpha}になります"
|
|
)
|
|
conv_block_dims = [conv_dim] * num_total_blocks
|
|
conv_block_alphas = [conv_alpha] * num_total_blocks
|
|
else:
|
|
conv_block_dims = None
|
|
conv_block_alphas = None
|
|
|
|
return block_dims, block_alphas, conv_block_dims, conv_block_alphas
|
|
|
|
|
|
|
|
def get_block_lr_weight(
|
|
down_lr_weight, mid_lr_weight, up_lr_weight, zero_threshold
|
|
) -> Tuple[List[float], List[float], List[float]]:
|
|
|
|
if up_lr_weight is None and mid_lr_weight is None and down_lr_weight is None:
|
|
return None, None, None
|
|
|
|
max_len = LoRANetwork.NUM_OF_BLOCKS
|
|
|
|
def get_list(name_with_suffix) -> List[float]:
|
|
import math
|
|
|
|
tokens = name_with_suffix.split("+")
|
|
name = tokens[0]
|
|
base_lr = float(tokens[1]) if len(tokens) > 1 else 0.0
|
|
|
|
if name == "cosine":
|
|
return [math.sin(math.pi * (i / (max_len - 1)) / 2) + base_lr for i in reversed(range(max_len))]
|
|
elif name == "sine":
|
|
return [math.sin(math.pi * (i / (max_len - 1)) / 2) + base_lr for i in range(max_len)]
|
|
elif name == "linear":
|
|
return [i / (max_len - 1) + base_lr for i in range(max_len)]
|
|
elif name == "reverse_linear":
|
|
return [i / (max_len - 1) + base_lr for i in reversed(range(max_len))]
|
|
elif name == "zeros":
|
|
return [0.0 + base_lr] * max_len
|
|
else:
|
|
print(
|
|
"Unknown lr_weight argument %s is used. Valid arguments: / 不明なlr_weightの引数 %s が使われました。有効な引数:\n\tcosine, sine, linear, reverse_linear, zeros"
|
|
% (name)
|
|
)
|
|
return None
|
|
|
|
if type(down_lr_weight) == str:
|
|
down_lr_weight = get_list(down_lr_weight)
|
|
if type(up_lr_weight) == str:
|
|
up_lr_weight = get_list(up_lr_weight)
|
|
|
|
if (up_lr_weight != None and len(up_lr_weight) > max_len) or (down_lr_weight != None and len(down_lr_weight) > max_len):
|
|
print("down_weight or up_weight is too long. Parameters after %d-th are ignored." % max_len)
|
|
print("down_weightもしくはup_weightが長すぎます。%d個目以降のパラメータは無視されます。" % max_len)
|
|
up_lr_weight = up_lr_weight[:max_len]
|
|
down_lr_weight = down_lr_weight[:max_len]
|
|
|
|
if (up_lr_weight != None and len(up_lr_weight) < max_len) or (down_lr_weight != None and len(down_lr_weight) < max_len):
|
|
print("down_weight or up_weight is too short. Parameters after %d-th are filled with 1." % max_len)
|
|
print("down_weightもしくはup_weightが短すぎます。%d個目までの不足したパラメータは1で補われます。" % max_len)
|
|
|
|
if down_lr_weight != None and len(down_lr_weight) < max_len:
|
|
down_lr_weight = down_lr_weight + [1.0] * (max_len - len(down_lr_weight))
|
|
if up_lr_weight != None and len(up_lr_weight) < max_len:
|
|
up_lr_weight = up_lr_weight + [1.0] * (max_len - len(up_lr_weight))
|
|
|
|
if (up_lr_weight != None) or (mid_lr_weight != None) or (down_lr_weight != None):
|
|
print("apply block learning rate / 階層別学習率を適用します。")
|
|
if down_lr_weight != None:
|
|
down_lr_weight = [w if w > zero_threshold else 0 for w in down_lr_weight]
|
|
print("down_lr_weight (shallower -> deeper, 浅い層->深い層):", down_lr_weight)
|
|
else:
|
|
print("down_lr_weight: all 1.0, すべて1.0")
|
|
|
|
if mid_lr_weight != None:
|
|
mid_lr_weight = mid_lr_weight if mid_lr_weight > zero_threshold else 0
|
|
print("mid_lr_weight:", mid_lr_weight)
|
|
else:
|
|
print("mid_lr_weight: 1.0")
|
|
|
|
if up_lr_weight != None:
|
|
up_lr_weight = [w if w > zero_threshold else 0 for w in up_lr_weight]
|
|
print("up_lr_weight (deeper -> shallower, 深い層->浅い層):", up_lr_weight)
|
|
else:
|
|
print("up_lr_weight: all 1.0, すべて1.0")
|
|
|
|
return down_lr_weight, mid_lr_weight, up_lr_weight
|
|
|
|
|
|
|
|
def remove_block_dims_and_alphas(
|
|
block_dims, block_alphas, conv_block_dims, conv_block_alphas, down_lr_weight, mid_lr_weight, up_lr_weight
|
|
):
|
|
|
|
if down_lr_weight != None:
|
|
for i, lr in enumerate(down_lr_weight):
|
|
if lr == 0:
|
|
block_dims[i] = 0
|
|
if conv_block_dims is not None:
|
|
conv_block_dims[i] = 0
|
|
if mid_lr_weight != None:
|
|
if mid_lr_weight == 0:
|
|
block_dims[LoRANetwork.NUM_OF_BLOCKS] = 0
|
|
if conv_block_dims is not None:
|
|
conv_block_dims[LoRANetwork.NUM_OF_BLOCKS] = 0
|
|
if up_lr_weight != None:
|
|
for i, lr in enumerate(up_lr_weight):
|
|
if lr == 0:
|
|
block_dims[LoRANetwork.NUM_OF_BLOCKS + 1 + i] = 0
|
|
if conv_block_dims is not None:
|
|
conv_block_dims[LoRANetwork.NUM_OF_BLOCKS + 1 + i] = 0
|
|
|
|
return block_dims, block_alphas, conv_block_dims, conv_block_alphas
|
|
|
|
|
|
|
|
def get_block_index(lora_name: str) -> int:
|
|
block_idx = -1
|
|
|
|
m = RE_UPDOWN.search(lora_name)
|
|
if m:
|
|
g = m.groups()
|
|
i = int(g[1])
|
|
j = int(g[3])
|
|
if g[2] == "resnets":
|
|
idx = 3 * i + j
|
|
elif g[2] == "attentions":
|
|
idx = 3 * i + j
|
|
elif g[2] == "upsamplers" or g[2] == "downsamplers":
|
|
idx = 3 * i + 2
|
|
|
|
if g[0] == "down":
|
|
block_idx = 1 + idx
|
|
elif g[0] == "up":
|
|
block_idx = LoRANetwork.NUM_OF_BLOCKS + 1 + idx
|
|
|
|
elif "mid_block_" in lora_name:
|
|
block_idx = LoRANetwork.NUM_OF_BLOCKS
|
|
|
|
return block_idx
|
|
|
|
|
|
|
|
def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, for_inference=False, **kwargs):
|
|
if weights_sd is None:
|
|
if os.path.splitext(file)[1] == ".safetensors":
|
|
from safetensors.torch import load_file, safe_open
|
|
|
|
weights_sd = load_file(file)
|
|
else:
|
|
weights_sd = torch.load(file, map_location="cpu")
|
|
|
|
|
|
modules_dim = {}
|
|
modules_alpha = {}
|
|
for key, value in weights_sd.items():
|
|
if "." not in key:
|
|
continue
|
|
|
|
lora_name = key.split(".")[0]
|
|
if "alpha" in key:
|
|
modules_alpha[lora_name] = value
|
|
elif "lora_down" in key:
|
|
dim = value.size()[0]
|
|
modules_dim[lora_name] = dim
|
|
|
|
|
|
|
|
for key in modules_dim.keys():
|
|
if key not in modules_alpha:
|
|
modules_alpha[key] = modules_dim[key]
|
|
|
|
module_class = LoRAInfModule if for_inference else LoRAModule
|
|
|
|
network = LoRANetwork(
|
|
text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha, module_class=module_class
|
|
)
|
|
|
|
|
|
down_lr_weight, mid_lr_weight, up_lr_weight = parse_block_lr_kwargs(kwargs)
|
|
if up_lr_weight is not None or mid_lr_weight is not None or down_lr_weight is not None:
|
|
network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight)
|
|
|
|
return network, weights_sd
|
|
|
|
|
|
class LoRANetwork(torch.nn.Module):
|
|
NUM_OF_BLOCKS = 12
|
|
|
|
UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel"]
|
|
UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
|
|
TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
|
|
LORA_PREFIX_UNET = "lora_unet"
|
|
LORA_PREFIX_TEXT_ENCODER = "lora_te"
|
|
|
|
|
|
LORA_PREFIX_TEXT_ENCODER1 = "lora_te1"
|
|
LORA_PREFIX_TEXT_ENCODER2 = "lora_te2"
|
|
|
|
def __init__(
|
|
self,
|
|
text_encoder: Union[List[CLIPTextModel], CLIPTextModel],
|
|
unet,
|
|
multiplier: float = 1.0,
|
|
lora_dim: int = 4,
|
|
alpha: float = 1,
|
|
dropout: Optional[float] = None,
|
|
rank_dropout: Optional[float] = None,
|
|
module_dropout: Optional[float] = None,
|
|
conv_lora_dim: Optional[int] = None,
|
|
conv_alpha: Optional[float] = None,
|
|
block_dims: Optional[List[int]] = None,
|
|
block_alphas: Optional[List[float]] = None,
|
|
conv_block_dims: Optional[List[int]] = None,
|
|
conv_block_alphas: Optional[List[float]] = None,
|
|
modules_dim: Optional[Dict[str, int]] = None,
|
|
modules_alpha: Optional[Dict[str, int]] = None,
|
|
module_class: Type[object] = LoRAModule,
|
|
varbose: Optional[bool] = False,
|
|
) -> None:
|
|
"""
|
|
LoRA network: すごく引数が多いが、パターンは以下の通り
|
|
1. lora_dimとalphaを指定
|
|
2. lora_dim、alpha、conv_lora_dim、conv_alphaを指定
|
|
3. block_dimsとblock_alphasを指定 : Conv2d3x3には適用しない
|
|
4. block_dims、block_alphas、conv_block_dims、conv_block_alphasを指定 : Conv2d3x3にも適用する
|
|
5. modules_dimとmodules_alphaを指定 (推論用)
|
|
"""
|
|
super().__init__()
|
|
self.multiplier = multiplier
|
|
|
|
self.lora_dim = lora_dim
|
|
self.alpha = alpha
|
|
self.conv_lora_dim = conv_lora_dim
|
|
self.conv_alpha = conv_alpha
|
|
self.dropout = dropout
|
|
self.rank_dropout = rank_dropout
|
|
self.module_dropout = module_dropout
|
|
|
|
if modules_dim is not None:
|
|
print(f"create LoRA network from weights")
|
|
elif block_dims is not None:
|
|
print(f"create LoRA network from block_dims")
|
|
print(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}")
|
|
print(f"block_dims: {block_dims}")
|
|
print(f"block_alphas: {block_alphas}")
|
|
if conv_block_dims is not None:
|
|
print(f"conv_block_dims: {conv_block_dims}")
|
|
print(f"conv_block_alphas: {conv_block_alphas}")
|
|
else:
|
|
print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
|
|
print(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}")
|
|
if self.conv_lora_dim is not None:
|
|
print(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")
|
|
|
|
|
|
def create_modules(
|
|
is_unet: bool,
|
|
text_encoder_idx: Optional[int],
|
|
root_module: torch.nn.Module,
|
|
target_replace_modules: List[torch.nn.Module],
|
|
) -> List[LoRAModule]:
|
|
prefix = (
|
|
self.LORA_PREFIX_UNET
|
|
if is_unet
|
|
else (
|
|
self.LORA_PREFIX_TEXT_ENCODER
|
|
if text_encoder_idx is None
|
|
else (self.LORA_PREFIX_TEXT_ENCODER1 if text_encoder_idx == 1 else self.LORA_PREFIX_TEXT_ENCODER2)
|
|
)
|
|
)
|
|
loras = []
|
|
skipped = []
|
|
for name, module in root_module.named_modules():
|
|
if module.__class__.__name__ in target_replace_modules:
|
|
for child_name, child_module in module.named_modules():
|
|
is_linear = child_module.__class__.__name__ == "Linear"
|
|
is_conv2d = child_module.__class__.__name__ == "Conv2d"
|
|
is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
|
|
|
|
if is_linear or is_conv2d:
|
|
lora_name = prefix + "." + name + "." + child_name
|
|
lora_name = lora_name.replace(".", "_")
|
|
|
|
dim = None
|
|
alpha = None
|
|
|
|
if modules_dim is not None:
|
|
|
|
if lora_name in modules_dim:
|
|
dim = modules_dim[lora_name]
|
|
alpha = modules_alpha[lora_name]
|
|
elif is_unet and block_dims is not None:
|
|
|
|
block_idx = get_block_index(lora_name)
|
|
if is_linear or is_conv2d_1x1:
|
|
dim = block_dims[block_idx]
|
|
alpha = block_alphas[block_idx]
|
|
elif conv_block_dims is not None:
|
|
dim = conv_block_dims[block_idx]
|
|
alpha = conv_block_alphas[block_idx]
|
|
else:
|
|
|
|
if is_linear or is_conv2d_1x1:
|
|
dim = self.lora_dim
|
|
alpha = self.alpha
|
|
elif self.conv_lora_dim is not None:
|
|
dim = self.conv_lora_dim
|
|
alpha = self.conv_alpha
|
|
|
|
if dim is None or dim == 0:
|
|
|
|
if is_linear or is_conv2d_1x1 or (self.conv_lora_dim is not None or conv_block_dims is not None):
|
|
skipped.append(lora_name)
|
|
continue
|
|
|
|
lora = module_class(
|
|
lora_name,
|
|
child_module,
|
|
self.multiplier,
|
|
dim,
|
|
alpha,
|
|
dropout=dropout,
|
|
rank_dropout=rank_dropout,
|
|
module_dropout=module_dropout,
|
|
)
|
|
loras.append(lora)
|
|
return loras, skipped
|
|
|
|
text_encoders = text_encoder if type(text_encoder) == list else [text_encoder]
|
|
|
|
|
|
|
|
self.text_encoder_loras = []
|
|
skipped_te = []
|
|
for i, text_encoder in enumerate(text_encoders):
|
|
if len(text_encoders) > 1:
|
|
index = i + 1
|
|
print(f"create LoRA for Text Encoder {index}:")
|
|
else:
|
|
index = None
|
|
print(f"create LoRA for Text Encoder:")
|
|
|
|
text_encoder_loras, skipped = create_modules(False, index, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
|
|
self.text_encoder_loras.extend(text_encoder_loras)
|
|
skipped_te += skipped
|
|
print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
|
|
|
|
|
|
target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE
|
|
if modules_dim is not None or self.conv_lora_dim is not None or conv_block_dims is not None:
|
|
target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
|
|
|
|
self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules)
|
|
print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
|
|
|
|
skipped = skipped_te + skipped_un
|
|
if varbose and len(skipped) > 0:
|
|
print(
|
|
f"because block_lr_weight is 0 or dim (rank) is 0, {len(skipped)} LoRA modules are skipped / block_lr_weightまたはdim (rank)が0の為、次の{len(skipped)}個のLoRAモジュールはスキップされます:"
|
|
)
|
|
for name in skipped:
|
|
print(f"\t{name}")
|
|
|
|
self.up_lr_weight: List[float] = None
|
|
self.down_lr_weight: List[float] = None
|
|
self.mid_lr_weight: float = None
|
|
self.block_lr = False
|
|
|
|
|
|
names = set()
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
|
|
names.add(lora.lora_name)
|
|
|
|
def set_multiplier(self, multiplier):
|
|
self.multiplier = multiplier
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.multiplier = self.multiplier
|
|
|
|
def load_weights(self, file):
|
|
if os.path.splitext(file)[1] == ".safetensors":
|
|
from safetensors.torch import load_file
|
|
|
|
weights_sd = load_file(file)
|
|
else:
|
|
weights_sd = torch.load(file, map_location="cpu")
|
|
|
|
info = self.load_state_dict(weights_sd, False)
|
|
return info
|
|
|
|
def apply_to(self, text_encoder, unet, apply_text_encoder=True, apply_unet=True):
|
|
if apply_text_encoder:
|
|
print("enable LoRA for text encoder")
|
|
else:
|
|
self.text_encoder_loras = []
|
|
|
|
if apply_unet:
|
|
print("enable LoRA for U-Net")
|
|
else:
|
|
self.unet_loras = []
|
|
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.apply_to()
|
|
self.add_module(lora.lora_name, lora)
|
|
|
|
|
|
def is_mergeable(self):
|
|
return True
|
|
|
|
|
|
def merge_to(self, text_encoder, unet, weights_sd, dtype, device):
|
|
apply_text_encoder = apply_unet = False
|
|
for key in weights_sd.keys():
|
|
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
|
|
apply_text_encoder = True
|
|
elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
|
|
apply_unet = True
|
|
|
|
if apply_text_encoder:
|
|
print("enable LoRA for text encoder")
|
|
else:
|
|
self.text_encoder_loras = []
|
|
|
|
if apply_unet:
|
|
print("enable LoRA for U-Net")
|
|
else:
|
|
self.unet_loras = []
|
|
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
sd_for_lora = {}
|
|
for key in weights_sd.keys():
|
|
if key.startswith(lora.lora_name):
|
|
sd_for_lora[key[len(lora.lora_name) + 1 :]] = weights_sd[key]
|
|
lora.merge_to(sd_for_lora, dtype, device)
|
|
|
|
print(f"weights are merged")
|
|
|
|
|
|
def set_block_lr_weight(
|
|
self,
|
|
up_lr_weight: List[float] = None,
|
|
mid_lr_weight: float = None,
|
|
down_lr_weight: List[float] = None,
|
|
):
|
|
self.block_lr = True
|
|
self.down_lr_weight = down_lr_weight
|
|
self.mid_lr_weight = mid_lr_weight
|
|
self.up_lr_weight = up_lr_weight
|
|
|
|
def get_lr_weight(self, lora: LoRAModule) -> float:
|
|
lr_weight = 1.0
|
|
block_idx = get_block_index(lora.lora_name)
|
|
if block_idx < 0:
|
|
return lr_weight
|
|
|
|
if block_idx < LoRANetwork.NUM_OF_BLOCKS:
|
|
if self.down_lr_weight != None:
|
|
lr_weight = self.down_lr_weight[block_idx]
|
|
elif block_idx == LoRANetwork.NUM_OF_BLOCKS:
|
|
if self.mid_lr_weight != None:
|
|
lr_weight = self.mid_lr_weight
|
|
elif block_idx > LoRANetwork.NUM_OF_BLOCKS:
|
|
if self.up_lr_weight != None:
|
|
lr_weight = self.up_lr_weight[block_idx - LoRANetwork.NUM_OF_BLOCKS - 1]
|
|
|
|
return lr_weight
|
|
|
|
|
|
def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
|
|
self.requires_grad_(True)
|
|
all_params = []
|
|
|
|
def enumerate_params(loras):
|
|
params = []
|
|
for lora in loras:
|
|
params.extend(lora.parameters())
|
|
return params
|
|
|
|
if self.text_encoder_loras:
|
|
param_data = {"params": enumerate_params(self.text_encoder_loras)}
|
|
if text_encoder_lr is not None:
|
|
param_data["lr"] = text_encoder_lr
|
|
all_params.append(param_data)
|
|
|
|
if self.unet_loras:
|
|
if self.block_lr:
|
|
|
|
block_idx_to_lora = {}
|
|
for lora in self.unet_loras:
|
|
idx = get_block_index(lora.lora_name)
|
|
if idx not in block_idx_to_lora:
|
|
block_idx_to_lora[idx] = []
|
|
block_idx_to_lora[idx].append(lora)
|
|
|
|
|
|
for idx, block_loras in block_idx_to_lora.items():
|
|
param_data = {"params": enumerate_params(block_loras)}
|
|
|
|
if unet_lr is not None:
|
|
param_data["lr"] = unet_lr * self.get_lr_weight(block_loras[0])
|
|
elif default_lr is not None:
|
|
param_data["lr"] = default_lr * self.get_lr_weight(block_loras[0])
|
|
if ("lr" in param_data) and (param_data["lr"] == 0):
|
|
continue
|
|
all_params.append(param_data)
|
|
|
|
else:
|
|
param_data = {"params": enumerate_params(self.unet_loras)}
|
|
if unet_lr is not None:
|
|
param_data["lr"] = unet_lr
|
|
all_params.append(param_data)
|
|
|
|
return all_params
|
|
|
|
def enable_gradient_checkpointing(self):
|
|
|
|
pass
|
|
|
|
def prepare_grad_etc(self, text_encoder, unet):
|
|
self.requires_grad_(True)
|
|
|
|
def on_epoch_start(self, text_encoder, unet):
|
|
self.train()
|
|
|
|
def get_trainable_params(self):
|
|
return self.parameters()
|
|
|
|
def save_weights(self, file, dtype, metadata):
|
|
if metadata is not None and len(metadata) == 0:
|
|
metadata = None
|
|
|
|
state_dict = self.state_dict()
|
|
|
|
if dtype is not None:
|
|
for key in list(state_dict.keys()):
|
|
v = state_dict[key]
|
|
v = v.detach().clone().to("cpu").to(dtype)
|
|
state_dict[key] = v
|
|
|
|
if os.path.splitext(file)[1] == ".safetensors":
|
|
from safetensors.torch import save_file
|
|
from library import train_util
|
|
|
|
|
|
if metadata is None:
|
|
metadata = {}
|
|
model_hash, legacy_hash = train_util.precalculate_safetensors_hashes(state_dict, metadata)
|
|
metadata["sshs_model_hash"] = model_hash
|
|
metadata["sshs_legacy_hash"] = legacy_hash
|
|
|
|
save_file(state_dict, file, metadata)
|
|
else:
|
|
torch.save(state_dict, file)
|
|
|
|
|
|
def set_region(self, sub_prompt_index, is_last_network, mask):
|
|
if mask.max() == 0:
|
|
mask = torch.ones_like(mask)
|
|
|
|
self.mask = mask
|
|
self.sub_prompt_index = sub_prompt_index
|
|
self.is_last_network = is_last_network
|
|
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.set_network(self)
|
|
|
|
def set_current_generation(self, batch_size, num_sub_prompts, width, height, shared):
|
|
self.batch_size = batch_size
|
|
self.num_sub_prompts = num_sub_prompts
|
|
self.current_size = (height, width)
|
|
self.shared = shared
|
|
|
|
|
|
mask = self.mask
|
|
mask_dic = {}
|
|
mask = mask.unsqueeze(0).unsqueeze(1)
|
|
ref_weight = self.text_encoder_loras[0].lora_down.weight if self.text_encoder_loras else self.unet_loras[0].lora_down.weight
|
|
dtype = ref_weight.dtype
|
|
device = ref_weight.device
|
|
|
|
def resize_add(mh, mw):
|
|
|
|
m = torch.nn.functional.interpolate(mask, (mh, mw), mode="bilinear")
|
|
m = m.to(device, dtype=dtype)
|
|
mask_dic[mh * mw] = m
|
|
|
|
h = height // 8
|
|
w = width // 8
|
|
for _ in range(4):
|
|
resize_add(h, w)
|
|
if h % 2 == 1 or w % 2 == 1:
|
|
resize_add(h + h % 2, w + w % 2)
|
|
h = (h + 1) // 2
|
|
w = (w + 1) // 2
|
|
|
|
self.mask_dic = mask_dic
|
|
|
|
def backup_weights(self):
|
|
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
if not hasattr(org_module, "_lora_org_weight"):
|
|
sd = org_module.state_dict()
|
|
org_module._lora_org_weight = sd["weight"].detach().clone()
|
|
org_module._lora_restored = True
|
|
|
|
def restore_weights(self):
|
|
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
if not org_module._lora_restored:
|
|
sd = org_module.state_dict()
|
|
sd["weight"] = org_module._lora_org_weight
|
|
org_module.load_state_dict(sd)
|
|
org_module._lora_restored = True
|
|
|
|
def pre_calculation(self):
|
|
|
|
loras: List[LoRAInfModule] = self.text_encoder_loras + self.unet_loras
|
|
for lora in loras:
|
|
org_module = lora.org_module_ref[0]
|
|
sd = org_module.state_dict()
|
|
|
|
org_weight = sd["weight"]
|
|
lora_weight = lora.get_weight().to(org_weight.device, dtype=org_weight.dtype)
|
|
sd["weight"] = org_weight + lora_weight
|
|
assert sd["weight"].shape == org_weight.shape
|
|
org_module.load_state_dict(sd)
|
|
|
|
org_module._lora_restored = False
|
|
lora.enabled = False
|
|
|
|
def apply_max_norm_regularization(self, max_norm_value, device):
|
|
downkeys = []
|
|
upkeys = []
|
|
alphakeys = []
|
|
norms = []
|
|
keys_scaled = 0
|
|
|
|
state_dict = self.state_dict()
|
|
for key in state_dict.keys():
|
|
if "lora_down" in key and "weight" in key:
|
|
downkeys.append(key)
|
|
upkeys.append(key.replace("lora_down", "lora_up"))
|
|
alphakeys.append(key.replace("lora_down.weight", "alpha"))
|
|
|
|
for i in range(len(downkeys)):
|
|
down = state_dict[downkeys[i]].to(device)
|
|
up = state_dict[upkeys[i]].to(device)
|
|
alpha = state_dict[alphakeys[i]].to(device)
|
|
dim = down.shape[0]
|
|
scale = alpha / dim
|
|
|
|
if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
|
|
updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
|
|
elif up.shape[2:] == (3, 3) or down.shape[2:] == (3, 3):
|
|
updown = torch.nn.functional.conv2d(down.permute(1, 0, 2, 3), up).permute(1, 0, 2, 3)
|
|
else:
|
|
updown = up @ down
|
|
|
|
updown *= scale
|
|
|
|
norm = updown.norm().clamp(min=max_norm_value / 2)
|
|
desired = torch.clamp(norm, max=max_norm_value)
|
|
ratio = desired.cpu() / norm.cpu()
|
|
sqrt_ratio = ratio**0.5
|
|
if ratio != 1:
|
|
keys_scaled += 1
|
|
state_dict[upkeys[i]] *= sqrt_ratio
|
|
state_dict[downkeys[i]] *= sqrt_ratio
|
|
scalednorm = updown.norm() * ratio
|
|
norms.append(scalednorm.item())
|
|
|
|
return keys_scaled, sum(norms) / len(norms), max(norms)
|
|
|