484 lines
18 KiB
Python
484 lines
18 KiB
Python
# 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 List
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import numpy as np
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import torch
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from library import train_util
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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__(self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1):
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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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# if limit_rank:
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# self.lora_dim = min(lora_dim, in_dim, out_dim)
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# if self.lora_dim != lora_dim:
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# print(f"{lora_name} dim (rank) is changed to: {self.lora_dim}")
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# else:
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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.region = None
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self.region_mask = None
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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 merge_to(self, sd, dtype, 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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# extract weight from org_module
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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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# 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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# print(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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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 forward(self, x):
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if self.region is None:
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return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
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# regional LoRA FIXME same as additional-network extension
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if x.size()[1] % 77 == 0:
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# print(f"LoRA for context: {self.lora_name}")
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self.region = None
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return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale
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# calculate region mask first time
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if self.region_mask is None:
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if len(x.size()) == 4:
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h, w = x.size()[2:4]
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else:
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seq_len = x.size()[1]
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ratio = math.sqrt((self.region.size()[0] * self.region.size()[1]) / seq_len)
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h = int(self.region.size()[0] / ratio + 0.5)
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w = seq_len // h
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r = self.region.to(x.device)
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if r.dtype == torch.bfloat16:
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r = r.to(torch.float)
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r = r.unsqueeze(0).unsqueeze(1)
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# print(self.lora_name, self.region.size(), x.size(), r.size(), h, w)
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r = torch.nn.functional.interpolate(r, (h, w), mode="bilinear")
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r = r.to(x.dtype)
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if len(x.size()) == 3:
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r = torch.reshape(r, (1, x.size()[1], -1))
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self.region_mask = r
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return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale * self.region_mask
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def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **kwargs):
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if network_dim is None:
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network_dim = 4 # default
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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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"""
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block_dims = kwargs.get("block_dims")
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block_alphas = None
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if block_dims is not None:
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block_dims = [int(d) for d in block_dims.split(',')]
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assert len(block_dims) == NUM_BLOCKS, f"Number of block dimensions is not same to {NUM_BLOCKS}"
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block_alphas = kwargs.get("block_alphas")
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if block_alphas is None:
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block_alphas = [1] * len(block_dims)
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else:
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block_alphas = [int(a) for a in block_alphas(',')]
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assert len(block_alphas) == NUM_BLOCKS, f"Number of block alphas is not same to {NUM_BLOCKS}"
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conv_block_dims = kwargs.get("conv_block_dims")
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conv_block_alphas = None
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if conv_block_dims is not None:
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conv_block_dims = [int(d) for d in conv_block_dims.split(',')]
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assert len(conv_block_dims) == NUM_BLOCKS, f"Number of block dimensions is not same to {NUM_BLOCKS}"
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conv_block_alphas = kwargs.get("conv_block_alphas")
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if conv_block_alphas is None:
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conv_block_alphas = [1] * len(conv_block_dims)
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else:
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conv_block_alphas = [int(a) for a in conv_block_alphas(',')]
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assert len(conv_block_alphas) == NUM_BLOCKS, f"Number of block alphas is not same to {NUM_BLOCKS}"
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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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conv_lora_dim=conv_dim,
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conv_alpha=conv_alpha,
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)
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return network
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def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weights_sd=None, **kwargs):
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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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# print(lora_name, value.size(), dim)
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# support old LoRA without alpha
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for key in modules_dim.keys():
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if key not in modules_alpha:
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modules_alpha = modules_dim[key]
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network = LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha)
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network.weights_sd = weights_sd
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return network
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class LoRANetwork(torch.nn.Module):
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# is it possible to apply conv_in and conv_out?
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UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
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UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
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TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
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LORA_PREFIX_UNET = "lora_unet"
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LORA_PREFIX_TEXT_ENCODER = "lora_te"
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def __init__(
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self,
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text_encoder,
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unet,
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multiplier=1.0,
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lora_dim=4,
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alpha=1,
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conv_lora_dim=None,
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conv_alpha=None,
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modules_dim=None,
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modules_alpha=None,
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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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if modules_dim is not None:
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print(f"create LoRA network from weights")
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else:
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print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}")
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self.apply_to_conv2d_3x3 = self.conv_lora_dim is not None
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if self.apply_to_conv2d_3x3:
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if self.conv_alpha is None:
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self.conv_alpha = self.alpha
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print(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")
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# create module instances
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def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules) -> List[LoRAModule]:
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loras = []
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for name, module in root_module.named_modules():
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if module.__class__.__name__ in target_replace_modules:
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# TODO get block index here
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for child_name, child_module in module.named_modules():
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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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if modules_dim is not None:
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if lora_name not in modules_dim:
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continue # no LoRA module in this weights file
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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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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.apply_to_conv2d_3x3:
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dim = self.conv_lora_dim
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alpha = self.conv_alpha
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else:
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continue
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lora = LoRAModule(lora_name, child_module, self.multiplier, dim, alpha)
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loras.append(lora)
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return loras
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self.text_encoder_loras = create_modules(
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LoRANetwork.LORA_PREFIX_TEXT_ENCODER, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
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)
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print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.")
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# extend U-Net target modules if conv2d 3x3 is enabled, or load from weights
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target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE
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if modules_dim is not None or self.conv_lora_dim is not None:
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target_modules += LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3
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self.unet_loras = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, target_modules)
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print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
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self.weights_sd = None
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# assertion
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names = set()
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for lora in self.text_encoder_loras + self.unet_loras:
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assert lora.lora_name not in names, f"duplicated lora name: {lora.lora_name}"
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names.add(lora.lora_name)
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def set_multiplier(self, multiplier):
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self.multiplier = multiplier
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for lora in self.text_encoder_loras + self.unet_loras:
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lora.multiplier = self.multiplier
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def load_weights(self, file):
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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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self.weights_sd = load_file(file)
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else:
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self.weights_sd = torch.load(file, map_location="cpu")
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def apply_to(self, text_encoder, unet, apply_text_encoder=None, apply_unet=None):
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if self.weights_sd:
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weights_has_text_encoder = weights_has_unet = False
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for key in self.weights_sd.keys():
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if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
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weights_has_text_encoder = True
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elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
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weights_has_unet = True
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if apply_text_encoder is None:
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apply_text_encoder = weights_has_text_encoder
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else:
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assert (
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apply_text_encoder == weights_has_text_encoder
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), f"text encoder weights: {weights_has_text_encoder} but text encoder flag: {apply_text_encoder} / 重みとText Encoderのフラグが矛盾しています"
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if apply_unet is None:
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apply_unet = weights_has_unet
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else:
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assert (
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apply_unet == weights_has_unet
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), f"u-net weights: {weights_has_unet} but u-net flag: {apply_unet} / 重みとU-Netのフラグが矛盾しています"
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else:
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assert apply_text_encoder is not None and apply_unet is not None, f"internal error: flag not set"
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if apply_text_encoder:
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print("enable LoRA for text encoder")
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else:
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self.text_encoder_loras = []
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if apply_unet:
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print("enable LoRA for U-Net")
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else:
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self.unet_loras = []
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for lora in self.text_encoder_loras + self.unet_loras:
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lora.apply_to()
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self.add_module(lora.lora_name, lora)
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if self.weights_sd:
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# if some weights are not in state dict, it is ok because initial LoRA does nothing (lora_up is initialized by zeros)
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info = self.load_state_dict(self.weights_sd, False)
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print(f"weights are loaded: {info}")
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# TODO refactor to common function with apply_to
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def merge_to(self, text_encoder, unet, dtype, device):
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assert self.weights_sd is not None, "weights are not loaded"
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apply_text_encoder = apply_unet = False
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for key in self.weights_sd.keys():
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if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
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apply_text_encoder = True
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elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
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apply_unet = True
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if apply_text_encoder:
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print("enable LoRA for text encoder")
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else:
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self.text_encoder_loras = []
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if apply_unet:
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print("enable LoRA for U-Net")
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else:
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self.unet_loras = []
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for lora in self.text_encoder_loras + self.unet_loras:
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sd_for_lora = {}
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for key in self.weights_sd.keys():
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if key.startswith(lora.lora_name):
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sd_for_lora[key[len(lora.lora_name) + 1 :]] = self.weights_sd[key]
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lora.merge_to(sd_for_lora, dtype, device)
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print(f"weights are merged")
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def enable_gradient_checkpointing(self):
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# not supported
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pass
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def prepare_optimizer_params(self, text_encoder_lr, unet_lr):
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def enumerate_params(loras):
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params = []
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for lora in loras:
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params.extend(lora.parameters())
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return params
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self.requires_grad_(True)
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all_params = []
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if self.text_encoder_loras:
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param_data = {"params": enumerate_params(self.text_encoder_loras)}
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if text_encoder_lr is not None:
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param_data["lr"] = text_encoder_lr
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all_params.append(param_data)
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if self.unet_loras:
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param_data = {"params": enumerate_params(self.unet_loras)}
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if unet_lr is not None:
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param_data["lr"] = unet_lr
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all_params.append(param_data)
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return all_params
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def prepare_grad_etc(self, text_encoder, unet):
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self.requires_grad_(True)
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def on_epoch_start(self, text_encoder, unet):
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self.train()
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def get_trainable_params(self):
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return self.parameters()
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def save_weights(self, file, dtype, metadata):
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if metadata is not None and len(metadata) == 0:
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metadata = None
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state_dict = self.state_dict()
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if dtype is not None:
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for key in list(state_dict.keys()):
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v = state_dict[key]
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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 = 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)
|
|
|
|
@staticmethod
|
|
def set_regions(networks, image):
|
|
image = image.astype(np.float32) / 255.0
|
|
for i, network in enumerate(networks[:3]):
|
|
# NOTE: consider averaging overwrapping area
|
|
region = image[:, :, i]
|
|
if region.max() == 0:
|
|
continue
|
|
region = torch.tensor(region)
|
|
network.set_region(region)
|
|
|
|
def set_region(self, region):
|
|
for lora in self.unet_loras:
|
|
lora.set_region(region)
|