825 lines
32 KiB
Python
825 lines
32 KiB
Python
# temporary minimum implementation of LoRA
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# FLUX doesn't have Conv2d, so we ignore it
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# TODO commonize with the original implementation
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# LoRA network module
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# reference:
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# https://github.com/microsoft/LoRA/blob/main/loralib/layers.py
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# https://github.com/cloneofsimo/lora/blob/master/lora_diffusion/lora.py
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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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#from ..library.utils import setup_logging
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#setup_logging()
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import logging
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logger = logging.getLogger(__name__)
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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() # without casting, bf16 causes error
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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)) # 定数として扱える
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# same as microsoft's
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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 # remove in applying
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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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# module dropout
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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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# normal dropout
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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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# rank 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) # for Text Encoder
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elif len(lx.size()) == 4:
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mask = mask.unsqueeze(-1).unsqueeze(-1) # for Conv2d
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lx = lx * mask
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# scaling for rank dropout: treat as if the rank is changed
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# maskから計算することも考えられるが、augmentation的な効果を期待してrank_dropoutを用いる
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scale = self.scale * (1.0 / (1.0 - self.rank_dropout)) # redundant for readability
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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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# no dropout for inference
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super().__init__(lora_name, org_module, multiplier, lora_dim, alpha)
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self.org_module_ref = [org_module] # 後から参照できるように
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self.enabled = True
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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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# freezeしてマージする
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def merge_to(self, sd, dtype, device):
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# extract weight from org_module
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org_sd = self.org_module.state_dict()
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weight = org_sd["weight"]
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org_dtype = weight.dtype
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org_device = weight.device
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weight = weight.to(torch.float) # calc in float
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if dtype is None:
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dtype = org_dtype
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if device is None:
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device = org_device
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# get up/down weight
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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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# merge weight
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if len(weight.size()) == 2:
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# linear
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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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# conv2d 1x1
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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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# conv2d 3x3
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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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# logger.info(conved.size(), weight.size(), module.stride, module.padding)
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weight = weight + self.multiplier * conved * self.scale
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# set weight to org_module
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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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# 復元できるマージのため、このモジュールのweightを返す
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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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# get up/down weight from module
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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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# pre-calculated weight
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if len(down_weight.size()) == 2:
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# linear
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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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# conv2d 1x1
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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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# conv2d 3x3
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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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# logger.info(f"default_forward {self.lora_name} {x.size()}")
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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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return self.default_forward(x)
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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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ae: AutoencoderKL,
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text_encoders: List[CLIPTextModel],
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flux,
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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 # default
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if network_alpha is None:
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network_alpha = 1.0
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# extract dim/alpha for conv2d, and block dim
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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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# rank/module dropout
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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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# single or double blocks
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train_blocks = kwargs.get("train_blocks", None) # None (default), "all" (same as None), "single", "double"
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#if train_blocks is not None:
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# assert train_blocks in ["all", "single", "double"], f"invalid train_blocks: {train_blocks}"
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# すごく引数が多いな ( ^ω^)･･･
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network = LoRANetwork(
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text_encoders,
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flux,
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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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train_blocks=train_blocks,
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varbose=True,
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)
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loraplus_lr_ratio = kwargs.get("loraplus_lr_ratio", None)
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loraplus_unet_lr_ratio = kwargs.get("loraplus_unet_lr_ratio", None)
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loraplus_text_encoder_lr_ratio = kwargs.get("loraplus_text_encoder_lr_ratio", None)
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loraplus_lr_ratio = float(loraplus_lr_ratio) if loraplus_lr_ratio is not None else None
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loraplus_unet_lr_ratio = float(loraplus_unet_lr_ratio) if loraplus_unet_lr_ratio is not None else None
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loraplus_text_encoder_lr_ratio = float(loraplus_text_encoder_lr_ratio) if loraplus_text_encoder_lr_ratio is not None else None
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if loraplus_lr_ratio is not None or loraplus_unet_lr_ratio is not None or loraplus_text_encoder_lr_ratio is not None:
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network.set_loraplus_lr_ratio(loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio)
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return network
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# Create network from weights for inference, weights are not loaded here (because can be merged)
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def create_network_from_weights(multiplier, file, ae, text_encoders, flux, weights_sd=None, for_inference=False, **kwargs):
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# if unet is an instance of SdxlUNet2DConditionModel or subclass, set is_sdxl to True
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if weights_sd is None:
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if os.path.splitext(file)[1] == ".safetensors":
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from safetensors.torch import load_file, safe_open
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weights_sd = load_file(file)
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else:
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weights_sd = torch.load(file, map_location="cpu")
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# get dim/alpha mapping
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modules_dim = {}
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modules_alpha = {}
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for key, value in weights_sd.items():
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if "." not in key:
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continue
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lora_name = key.split(".")[0]
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if "alpha" in key:
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modules_alpha[lora_name] = value
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elif "lora_down" in key:
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dim = value.size()[0]
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modules_dim[lora_name] = dim
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# logger.info(lora_name, value.size(), dim)
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module_class = LoRAInfModule if for_inference else LoRAModule
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network = LoRANetwork(
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text_encoders, flux, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha, module_class=module_class
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)
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return network, weights_sd
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class LoRANetwork(torch.nn.Module):
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# FLUX_TARGET_REPLACE_MODULE = ["DoubleStreamBlock", "SingleStreamBlock"]
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FLUX_TARGET_REPLACE_MODULE_DOUBLE = ["DoubleStreamBlock"]
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FLUX_TARGET_REPLACE_MODULE_SINGLE = ["SingleStreamBlock"]
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TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPSdpaAttention", "CLIPMLP"]
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LORA_PREFIX_FLUX = "lora_unet" # make ComfyUI compatible
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LORA_PREFIX_TEXT_ENCODER_CLIP = "lora_te1"
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LORA_PREFIX_TEXT_ENCODER_T5 = "lora_te2"
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def __init__(
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self,
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text_encoders: Union[List[CLIPTextModel], CLIPTextModel],
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unet,
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multiplier: float = 1.0,
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lora_dim: int = 4,
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alpha: float = 1,
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dropout: Optional[float] = None,
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rank_dropout: Optional[float] = None,
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module_dropout: Optional[float] = None,
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conv_lora_dim: Optional[int] = None,
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conv_alpha: Optional[float] = None,
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module_class: Type[object] = LoRAModule,
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modules_dim: Optional[Dict[str, int]] = None,
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modules_alpha: Optional[Dict[str, int]] = None,
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train_blocks: Optional[str] = None,
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varbose: Optional[bool] = False,
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) -> None:
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super().__init__()
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self.multiplier = multiplier
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self.lora_dim = lora_dim
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self.alpha = alpha
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self.conv_lora_dim = conv_lora_dim
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self.conv_alpha = conv_alpha
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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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self.train_blocks = train_blocks if train_blocks is not None else "all"
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self.loraplus_lr_ratio = None
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self.loraplus_unet_lr_ratio = None
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self.loraplus_text_encoder_lr_ratio = None
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if modules_dim is not None:
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logger.info(f"create LoRA network from weights")
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else:
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logger.info(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
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logger.info(
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f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}"
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)
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if self.conv_lora_dim is not None:
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logger.info(
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f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}"
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)
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# create module instances
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def create_modules(
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is_flux: bool, text_encoder_idx: Optional[int], root_module: torch.nn.Module, target_replace_modules: List[str]
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) -> List[LoRAModule]:
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prefix = (
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self.LORA_PREFIX_FLUX
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if is_flux
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else (self.LORA_PREFIX_TEXT_ENCODER_CLIP if text_encoder_idx == 0 else self.LORA_PREFIX_TEXT_ENCODER_T5)
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)
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def process_module(name, module, prefix, modules_dim, modules_alpha, dropout, rank_dropout, module_dropout, target_replace_modules):
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loras = []
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skipped = []
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def process_child(child_name, child_module):
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is_linear = child_module.__class__.__name__ == "Linear"
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is_conv2d = child_module.__class__.__name__ == "Conv2d"
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is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
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if is_linear or is_conv2d:
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lora_name = prefix + "." + name + "." + child_name
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lora_name = lora_name.replace(".", "_")
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dim = None
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alpha = None
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if modules_dim is not None:
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# Module specified
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if lora_name in modules_dim:
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dim = modules_dim[lora_name]
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alpha = modules_alpha[lora_name]
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else:
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# Normally, target all
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if is_linear or is_conv2d_1x1:
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dim = self.lora_dim
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alpha = self.alpha
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elif self.conv_lora_dim is not None:
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dim = self.conv_lora_dim
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alpha = self.conv_alpha
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if dim is None or dim == 0:
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# Output skipped information
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if is_linear or is_conv2d_1x1 or (self.conv_lora_dim is not None):
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skipped.append(lora_name)
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return
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lora = module_class(
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lora_name,
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child_module,
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self.multiplier,
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dim,
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alpha,
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dropout=dropout,
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rank_dropout=rank_dropout,
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module_dropout=module_dropout,
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)
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loras.append(lora)
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for child_name, child_module in module.named_modules():
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process_child(child_name, child_module)
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return loras, skipped
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loras = []
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skipped = []
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target_replace_modules = [module.strip() for module in target_replace_modules]
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for name, module in root_module.named_modules():
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if any("blocks" in part for part in target_replace_modules) and name in target_replace_modules:
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print(module)
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module_loras, module_skipped = process_module(name, module, prefix, modules_dim, modules_alpha, dropout, rank_dropout, module_dropout, target_replace_modules)
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loras.extend(module_loras)
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skipped.extend(module_skipped)
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elif module.__class__.__name__ in target_replace_modules:
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print(module)
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module_loras, module_skipped = process_module(name, module, prefix, modules_dim, modules_alpha, dropout, rank_dropout, module_dropout, target_replace_modules)
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loras.extend(module_loras)
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skipped.extend(module_skipped)
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return loras, skipped
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# create LoRA for text encoder
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# 毎回すべてのモジュールを作るのは無駄なので要検討
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self.text_encoder_loras: List[Union[LoRAModule, LoRAInfModule]] = []
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skipped_te = []
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for i, text_encoder in enumerate(text_encoders):
|
|
index = i
|
|
logger.info(f"create LoRA for Text Encoder {index+1}:")
|
|
|
|
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
|
|
logger.info(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
|
|
print("TRAIN BLOCKS:", self.train_blocks)
|
|
# create LoRA for U-Net
|
|
if any("blocks" in part for part in self.train_blocks.split(',')):
|
|
target_replace_modules = self.train_blocks.split(',')
|
|
print("TARGET_REPLACE_MODULES:", target_replace_modules)
|
|
elif self.train_blocks == "all":
|
|
target_replace_modules = LoRANetwork.FLUX_TARGET_REPLACE_MODULE_DOUBLE + LoRANetwork.FLUX_TARGET_REPLACE_MODULE_SINGLE
|
|
elif self.train_blocks == "single":
|
|
target_replace_modules = LoRANetwork.FLUX_TARGET_REPLACE_MODULE_SINGLE
|
|
elif self.train_blocks == "double":
|
|
target_replace_modules = LoRANetwork.FLUX_TARGET_REPLACE_MODULE_DOUBLE
|
|
|
|
self.unet_loras: List[Union[LoRAModule, LoRAInfModule]]
|
|
self.unet_loras, skipped_un = create_modules(True, None, unet, target_replace_modules)
|
|
logger.info(f"create LoRA for FLUX {self.train_blocks} blocks: {len(self.unet_loras)} modules.")
|
|
|
|
skipped = skipped_te + skipped_un
|
|
if varbose and len(skipped) > 0:
|
|
logger.warning(
|
|
f"because dim (rank) is 0, {len(skipped)} LoRA modules are skipped / 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:
|
|
logger.info(f"\t{name}")
|
|
|
|
# assertion
|
|
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 set_enabled(self, is_enabled):
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.enabled = is_enabled
|
|
|
|
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_encoders, flux, apply_text_encoder=True, apply_unet=True):
|
|
if apply_text_encoder:
|
|
logger.info(f"enable LoRA for text encoder: {len(self.text_encoder_loras)} modules")
|
|
else:
|
|
self.text_encoder_loras = []
|
|
|
|
if apply_unet:
|
|
logger.info(f"enable LoRA for U-Net: {len(self.unet_loras)} modules")
|
|
else:
|
|
self.unet_loras = []
|
|
|
|
for lora in self.text_encoder_loras + self.unet_loras:
|
|
lora.apply_to()
|
|
self.add_module(lora.lora_name, lora)
|
|
|
|
# マージできるかどうかを返す
|
|
def is_mergeable(self):
|
|
return True
|
|
|
|
# TODO refactor to common function with apply_to
|
|
def merge_to(self, text_encoders, flux, weights_sd, dtype=None, device=None):
|
|
apply_text_encoder = apply_unet = False
|
|
for key in weights_sd.keys():
|
|
if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER_CLIP) or key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER_T5):
|
|
apply_text_encoder = True
|
|
elif key.startswith(LoRANetwork.LORA_PREFIX_FLUX):
|
|
apply_unet = True
|
|
|
|
if apply_text_encoder:
|
|
logger.info("enable LoRA for text encoder")
|
|
else:
|
|
self.text_encoder_loras = []
|
|
|
|
if apply_unet:
|
|
logger.info("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)
|
|
|
|
logger.info(f"weights are merged")
|
|
|
|
def set_loraplus_lr_ratio(self, loraplus_lr_ratio, loraplus_unet_lr_ratio, loraplus_text_encoder_lr_ratio):
|
|
self.loraplus_lr_ratio = loraplus_lr_ratio
|
|
self.loraplus_unet_lr_ratio = loraplus_unet_lr_ratio
|
|
self.loraplus_text_encoder_lr_ratio = loraplus_text_encoder_lr_ratio
|
|
|
|
logger.info(f"LoRA+ UNet LR Ratio: {self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio}")
|
|
logger.info(f"LoRA+ Text Encoder LR Ratio: {self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio}")
|
|
|
|
# 二つのText Encoderに別々の学習率を設定できるようにするといいかも
|
|
def prepare_optimizer_params(self, text_encoder_lr, unet_lr, default_lr):
|
|
# TODO warn if optimizer is not compatible with LoRA+ (but it will cause error so we don't need to check it here?)
|
|
# if (
|
|
# self.loraplus_lr_ratio is not None
|
|
# or self.loraplus_text_encoder_lr_ratio is not None
|
|
# or self.loraplus_unet_lr_ratio is not None
|
|
# ):
|
|
# assert (
|
|
# optimizer_type.lower() != "prodigy" and "dadapt" not in optimizer_type.lower()
|
|
# ), "LoRA+ and Prodigy/DAdaptation is not supported / LoRA+とProdigy/DAdaptationの組み合わせはサポートされていません"
|
|
|
|
self.requires_grad_(True)
|
|
|
|
all_params = []
|
|
lr_descriptions = []
|
|
|
|
def assemble_params(loras, lr, ratio):
|
|
param_groups = {"lora": {}, "plus": {}}
|
|
for lora in loras:
|
|
for name, param in lora.named_parameters():
|
|
if ratio is not None and "lora_up" in name:
|
|
param_groups["plus"][f"{lora.lora_name}.{name}"] = param
|
|
else:
|
|
param_groups["lora"][f"{lora.lora_name}.{name}"] = param
|
|
|
|
params = []
|
|
descriptions = []
|
|
for key in param_groups.keys():
|
|
param_data = {"params": param_groups[key].values()}
|
|
|
|
if len(param_data["params"]) == 0:
|
|
continue
|
|
|
|
if lr is not None:
|
|
if key == "plus":
|
|
param_data["lr"] = lr * ratio
|
|
else:
|
|
param_data["lr"] = lr
|
|
|
|
if param_data.get("lr", None) == 0 or param_data.get("lr", None) is None:
|
|
logger.info("NO LR skipping!")
|
|
continue
|
|
|
|
params.append(param_data)
|
|
descriptions.append("plus" if key == "plus" else "")
|
|
|
|
return params, descriptions
|
|
|
|
if self.text_encoder_loras:
|
|
params, descriptions = assemble_params(
|
|
self.text_encoder_loras,
|
|
text_encoder_lr if text_encoder_lr is not None else default_lr,
|
|
self.loraplus_text_encoder_lr_ratio or self.loraplus_lr_ratio,
|
|
)
|
|
all_params.extend(params)
|
|
lr_descriptions.extend(["textencoder" + (" " + d if d else "") for d in descriptions])
|
|
|
|
if self.unet_loras:
|
|
# if self.block_lr:
|
|
# is_sdxl = False
|
|
# for lora in self.unet_loras:
|
|
# if "input_blocks" in lora.lora_name or "output_blocks" in lora.lora_name:
|
|
# is_sdxl = True
|
|
# break
|
|
|
|
# # 学習率のグラフをblockごとにしたいので、blockごとにloraを分類
|
|
# block_idx_to_lora = {}
|
|
# for lora in self.unet_loras:
|
|
# idx = get_block_index(lora.lora_name, is_sdxl)
|
|
# if idx not in block_idx_to_lora:
|
|
# block_idx_to_lora[idx] = []
|
|
# block_idx_to_lora[idx].append(lora)
|
|
|
|
# # blockごとにパラメータを設定する
|
|
# for idx, block_loras in block_idx_to_lora.items():
|
|
# params, descriptions = assemble_params(
|
|
# block_loras,
|
|
# (unet_lr if unet_lr is not None else default_lr) * self.get_lr_weight(idx),
|
|
# self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio,
|
|
# )
|
|
# all_params.extend(params)
|
|
# lr_descriptions.extend([f"unet_block{idx}" + (" " + d if d else "") for d in descriptions])
|
|
|
|
# else:
|
|
params, descriptions = assemble_params(
|
|
self.unet_loras,
|
|
unet_lr if unet_lr is not None else default_lr,
|
|
self.loraplus_unet_lr_ratio or self.loraplus_lr_ratio,
|
|
)
|
|
all_params.extend(params)
|
|
lr_descriptions.extend(["unet" + (" " + d if d else "") for d in descriptions])
|
|
|
|
return all_params, lr_descriptions
|
|
|
|
def enable_gradient_checkpointing(self):
|
|
# not supported
|
|
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
|
|
|
|
# Precalculate model hashes to save time on indexing
|
|
if metadata is None:
|
|
metadata = {}
|
|
model_hash, legacy_hash = 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 backup_weights(self):
|
|
# 重みのバックアップを行う
|
|
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):
|
|
# 重みのリストアを行う
|
|
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):
|
|
# 事前計算を行う
|
|
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)
|
|
|
|
def precalculate_safetensors_hashes(tensors, metadata):
|
|
"""Precalculate the model hashes needed by sd-webui-additional-networks to
|
|
save time on indexing the model later."""
|
|
|
|
import hashlib
|
|
import safetensors.torch
|
|
from io import BytesIO
|
|
|
|
# Retain only training metadata for hash calculation
|
|
metadata = {k: v for k, v in metadata.items() if k.startswith("ss_")}
|
|
bytes = safetensors.torch.save(tensors, metadata)
|
|
b = BytesIO(bytes)
|
|
|
|
def addnet_hash_legacy(b):
|
|
"""Old model hash used by sd-webui-additional-networks for .safetensors format files"""
|
|
m = hashlib.sha256()
|
|
b.seek(0x100000)
|
|
m.update(b.read(0x10000))
|
|
return m.hexdigest()[0:8]
|
|
|
|
def addnet_hash_safetensors(b):
|
|
"""New model hash used by sd-webui-additional-networks for .safetensors format files"""
|
|
hash_sha256 = hashlib.sha256()
|
|
blksize = 1024 * 1024
|
|
b.seek(0)
|
|
header = b.read(8)
|
|
n = int.from_bytes(header, "little")
|
|
offset = n + 8
|
|
b.seek(offset)
|
|
for chunk in iter(lambda: b.read(blksize), b""):
|
|
hash_sha256.update(chunk)
|
|
return hash_sha256.hexdigest()
|
|
|
|
model_hash = addnet_hash_safetensors(b)
|
|
legacy_hash = addnet_hash_legacy(b)
|
|
return model_hash, legacy_hash |