# LoRA network module # reference: # https://github.com/microsoft/LoRA/blob/main/loralib/layers.py # https://github.com/cloneofsimo/lora/blob/master/lora_diffusion/lora.py # temporary implementation of LoRA-FA: https://arxiv.org/abs/2308.03303 # need to be refactored and merged to lora.py import math import os from typing import Dict, List, Optional, Tuple, Type, Union from diffusers import AutoencoderKL from transformers import CLIPTextModel import numpy as np import torch import re from library.utils import setup_logging setup_logging() import logging logger = logging.getLogger(__name__) RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_") class LoRAModule(torch.nn.Module): """ replaces forward method of the original Linear, instead of replacing the original Linear module. """ def __init__( self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1, dropout=None, rank_dropout=None, module_dropout=None, ): """if alpha == 0 or None, alpha is rank (no scaling).""" super().__init__() self.lora_name = lora_name if org_module.__class__.__name__ == "Conv2d": in_dim = org_module.in_channels out_dim = org_module.out_channels else: in_dim = org_module.in_features out_dim = org_module.out_features # if limit_rank: # self.lora_dim = min(lora_dim, in_dim, out_dim) # if self.lora_dim != lora_dim: # logger.info(f"{lora_name} dim (rank) is changed to: {self.lora_dim}") # else: self.lora_dim = lora_dim if org_module.__class__.__name__ == "Conv2d": kernel_size = org_module.kernel_size stride = org_module.stride padding = org_module.padding self.lora_down = torch.nn.Conv2d(in_dim, self.lora_dim, kernel_size, stride, padding, bias=False) self.lora_up = torch.nn.Conv2d(self.lora_dim, out_dim, (1, 1), (1, 1), bias=False) else: self.lora_down = torch.nn.Linear(in_dim, self.lora_dim, bias=False) self.lora_up = torch.nn.Linear(self.lora_dim, out_dim, bias=False) if type(alpha) == torch.Tensor: alpha = alpha.detach().float().numpy() # without casting, bf16 causes error alpha = self.lora_dim if alpha is None or alpha == 0 else alpha self.scale = alpha / self.lora_dim self.register_buffer("alpha", torch.tensor(alpha)) # 定数として扱える # # same as microsoft's # torch.nn.init.kaiming_uniform_(self.lora_down.weight, a=math.sqrt(5)) # according to the paper, initialize LoRA-A (down) as normal distribution torch.nn.init.normal_(self.lora_down.weight, std=math.sqrt(2.0 / (in_dim + self.lora_dim))) torch.nn.init.zeros_(self.lora_up.weight) self.multiplier = multiplier self.org_module = org_module # remove in applying self.dropout = dropout self.rank_dropout = rank_dropout self.module_dropout = module_dropout def get_trainable_params(self): params = self.named_parameters() trainable_params = [] for param in params: if param[0] == "lora_up.weight": # up only trainable_params.append(param[1]) return trainable_params def requires_grad_(self, requires_grad: bool = True): self.lora_up.requires_grad_(requires_grad) self.lora_down.requires_grad_(False) return self def apply_to(self): self.org_forward = self.org_module.forward self.org_module.forward = self.forward del self.org_module def forward(self, x): org_forwarded = self.org_forward(x) # module dropout if self.module_dropout is not None and self.training: if torch.rand(1) < self.module_dropout: return org_forwarded lx = self.lora_down(x) # normal dropout if self.dropout is not None and self.training: lx = torch.nn.functional.dropout(lx, p=self.dropout) # rank dropout if self.rank_dropout is not None and self.training: mask = torch.rand((lx.size(0), self.lora_dim), device=lx.device) > self.rank_dropout if len(lx.size()) == 3: mask = mask.unsqueeze(1) # for Text Encoder elif len(lx.size()) == 4: mask = mask.unsqueeze(-1).unsqueeze(-1) # for Conv2d lx = lx * mask # scaling for rank dropout: treat as if the rank is changed # 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 scale = self.scale * (1.0 / (1.0 - self.rank_dropout)) # redundant for readability else: scale = self.scale lx = self.lora_up(lx) return org_forwarded + lx * self.multiplier * scale class LoRAInfModule(LoRAModule): def __init__( self, lora_name, org_module: torch.nn.Module, multiplier=1.0, lora_dim=4, alpha=1, **kwargs, ): # no dropout for inference super().__init__(lora_name, org_module, multiplier, lora_dim, alpha) self.org_module_ref = [org_module] # 後から参照できるように self.enabled = True # check regional or not by lora_name self.text_encoder = False if lora_name.startswith("lora_te_"): self.regional = False self.use_sub_prompt = True self.text_encoder = True elif "attn2_to_k" in lora_name or "attn2_to_v" in lora_name: self.regional = False self.use_sub_prompt = True elif "time_emb" in lora_name: self.regional = False self.use_sub_prompt = False else: self.regional = True self.use_sub_prompt = False self.network: LoRANetwork = None def set_network(self, network): self.network = network # freezeしてマージする def merge_to(self, sd, dtype, device): # get up/down weight up_weight = sd["lora_up.weight"].to(torch.float).to(device) down_weight = sd["lora_down.weight"].to(torch.float).to(device) # extract weight from org_module org_sd = self.org_module.state_dict() weight = org_sd["weight"].to(torch.float) # merge weight if len(weight.size()) == 2: # linear weight = weight + self.multiplier * (up_weight @ down_weight) * self.scale elif down_weight.size()[2:4] == (1, 1): # conv2d 1x1 weight = ( weight + self.multiplier * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) * self.scale ) else: # conv2d 3x3 conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3) # logger.info(conved.size(), weight.size(), module.stride, module.padding) weight = weight + self.multiplier * conved * self.scale # set weight to org_module org_sd["weight"] = weight.to(dtype) self.org_module.load_state_dict(org_sd) # 復元できるマージのため、このモジュールのweightを返す def get_weight(self, multiplier=None): if multiplier is None: multiplier = self.multiplier # get up/down weight from module up_weight = self.lora_up.weight.to(torch.float) down_weight = self.lora_down.weight.to(torch.float) # pre-calculated weight if len(down_weight.size()) == 2: # linear weight = self.multiplier * (up_weight @ down_weight) * self.scale elif down_weight.size()[2:4] == (1, 1): # conv2d 1x1 weight = ( self.multiplier * (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)).unsqueeze(2).unsqueeze(3) * self.scale ) else: # conv2d 3x3 conved = torch.nn.functional.conv2d(down_weight.permute(1, 0, 2, 3), up_weight).permute(1, 0, 2, 3) weight = self.multiplier * conved * self.scale return weight def set_region(self, region): self.region = region self.region_mask = None def default_forward(self, x): # logger.info("default_forward", self.lora_name, x.size()) return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale def forward(self, x): if not self.enabled: return self.org_forward(x) if self.network is None or self.network.sub_prompt_index is None: return self.default_forward(x) if not self.regional and not self.use_sub_prompt: return self.default_forward(x) if self.regional: return self.regional_forward(x) else: return self.sub_prompt_forward(x) def get_mask_for_x(self, x): # calculate size from shape of x if len(x.size()) == 4: h, w = x.size()[2:4] area = h * w else: area = x.size()[1] mask = self.network.mask_dic[area] if mask is None: raise ValueError(f"mask is None for resolution {area}") if len(x.size()) != 4: mask = torch.reshape(mask, (1, -1, 1)) return mask def regional_forward(self, x): if "attn2_to_out" in self.lora_name: return self.to_out_forward(x) if self.network.mask_dic is None: # sub_prompt_index >= 3 return self.default_forward(x) # apply mask for LoRA result lx = self.lora_up(self.lora_down(x)) * self.multiplier * self.scale mask = self.get_mask_for_x(lx) # logger.info("regional", self.lora_name, self.network.sub_prompt_index, lx.size(), mask.size()) lx = lx * mask x = self.org_forward(x) x = x + lx if "attn2_to_q" in self.lora_name and self.network.is_last_network: x = self.postp_to_q(x) return x def postp_to_q(self, x): # repeat x to num_sub_prompts has_real_uncond = x.size()[0] // self.network.batch_size == 3 qc = self.network.batch_size # uncond qc += self.network.batch_size * self.network.num_sub_prompts # cond if has_real_uncond: qc += self.network.batch_size # real_uncond query = torch.zeros((qc, x.size()[1], x.size()[2]), device=x.device, dtype=x.dtype) query[: self.network.batch_size] = x[: self.network.batch_size] for i in range(self.network.batch_size): qi = self.network.batch_size + i * self.network.num_sub_prompts query[qi : qi + self.network.num_sub_prompts] = x[self.network.batch_size + i] if has_real_uncond: query[-self.network.batch_size :] = x[-self.network.batch_size :] # logger.info("postp_to_q", self.lora_name, x.size(), query.size(), self.network.num_sub_prompts) return query def sub_prompt_forward(self, x): if x.size()[0] == self.network.batch_size: # if uncond in text_encoder, do not apply LoRA return self.org_forward(x) emb_idx = self.network.sub_prompt_index if not self.text_encoder: emb_idx += self.network.batch_size # apply sub prompt of X lx = x[emb_idx :: self.network.num_sub_prompts] lx = self.lora_up(self.lora_down(lx)) * self.multiplier * self.scale # logger.info("sub_prompt_forward", self.lora_name, x.size(), lx.size(), emb_idx) x = self.org_forward(x) x[emb_idx :: self.network.num_sub_prompts] += lx return x def to_out_forward(self, x): # logger.info("to_out_forward", self.lora_name, x.size(), self.network.is_last_network) if self.network.is_last_network: masks = [None] * self.network.num_sub_prompts self.network.shared[self.lora_name] = (None, masks) else: lx, masks = self.network.shared[self.lora_name] # call own LoRA x1 = x[self.network.batch_size + self.network.sub_prompt_index :: self.network.num_sub_prompts] lx1 = self.lora_up(self.lora_down(x1)) * self.multiplier * self.scale if self.network.is_last_network: lx = torch.zeros( (self.network.num_sub_prompts * self.network.batch_size, *lx1.size()[1:]), device=lx1.device, dtype=lx1.dtype ) self.network.shared[self.lora_name] = (lx, masks) # logger.info("to_out_forward", lx.size(), lx1.size(), self.network.sub_prompt_index, self.network.num_sub_prompts) lx[self.network.sub_prompt_index :: self.network.num_sub_prompts] += lx1 masks[self.network.sub_prompt_index] = self.get_mask_for_x(lx1) # if not last network, return x and masks x = self.org_forward(x) if not self.network.is_last_network: return x lx, masks = self.network.shared.pop(self.lora_name) # if last network, combine separated x with mask weighted sum has_real_uncond = x.size()[0] // self.network.batch_size == self.network.num_sub_prompts + 2 out = torch.zeros((self.network.batch_size * (3 if has_real_uncond else 2), *x.size()[1:]), device=x.device, dtype=x.dtype) out[: self.network.batch_size] = x[: self.network.batch_size] # uncond if has_real_uncond: out[-self.network.batch_size :] = x[-self.network.batch_size :] # real_uncond # logger.info("to_out_forward", self.lora_name, self.network.sub_prompt_index, self.network.num_sub_prompts) # for i in range(len(masks)): # if masks[i] is None: # masks[i] = torch.zeros_like(masks[-1]) mask = torch.cat(masks) mask_sum = torch.sum(mask, dim=0) + 1e-4 for i in range(self.network.batch_size): # 1枚の画像ごとに処理する lx1 = lx[i * self.network.num_sub_prompts : (i + 1) * self.network.num_sub_prompts] lx1 = lx1 * mask lx1 = torch.sum(lx1, dim=0) xi = self.network.batch_size + i * self.network.num_sub_prompts x1 = x[xi : xi + self.network.num_sub_prompts] x1 = x1 * mask x1 = torch.sum(x1, dim=0) x1 = x1 / mask_sum x1 = x1 + lx1 out[self.network.batch_size + i] = x1 # logger.info("to_out_forward", x.size(), out.size(), has_real_uncond) return out def parse_block_lr_kwargs(nw_kwargs): down_lr_weight = nw_kwargs.get("down_lr_weight", None) mid_lr_weight = nw_kwargs.get("mid_lr_weight", None) up_lr_weight = nw_kwargs.get("up_lr_weight", None) # 以上のいずれにも設定がない場合は無効としてNoneを返す if down_lr_weight is None and mid_lr_weight is None and up_lr_weight is None: return None, None, None # extract learning rate weight for each block if down_lr_weight is not None: # if some parameters are not set, use zero if "," in down_lr_weight: down_lr_weight = [(float(s) if s else 0.0) for s in down_lr_weight.split(",")] if mid_lr_weight is not None: mid_lr_weight = float(mid_lr_weight) if up_lr_weight is not None: if "," in up_lr_weight: up_lr_weight = [(float(s) if s else 0.0) for s in up_lr_weight.split(",")] down_lr_weight, mid_lr_weight, up_lr_weight = get_block_lr_weight( down_lr_weight, mid_lr_weight, up_lr_weight, float(nw_kwargs.get("block_lr_zero_threshold", 0.0)) ) return down_lr_weight, mid_lr_weight, up_lr_weight def create_network( multiplier: float, network_dim: Optional[int], network_alpha: Optional[float], vae: AutoencoderKL, text_encoder: Union[CLIPTextModel, List[CLIPTextModel]], unet, neuron_dropout: Optional[float] = None, **kwargs, ): if network_dim is None: network_dim = 4 # default if network_alpha is None: network_alpha = 1.0 # extract dim/alpha for conv2d, and block dim conv_dim = kwargs.get("conv_dim", None) conv_alpha = kwargs.get("conv_alpha", None) if conv_dim is not None: conv_dim = int(conv_dim) if conv_alpha is None: conv_alpha = 1.0 else: conv_alpha = float(conv_alpha) # block dim/alpha/lr block_dims = kwargs.get("block_dims", None) down_lr_weight, mid_lr_weight, up_lr_weight = parse_block_lr_kwargs(kwargs) # 以上のいずれかに指定があればblockごとのdim(rank)を有効にする 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: block_alphas = kwargs.get("block_alphas", None) conv_block_dims = kwargs.get("conv_block_dims", None) conv_block_alphas = kwargs.get("conv_block_alphas", None) block_dims, block_alphas, conv_block_dims, conv_block_alphas = get_block_dims_and_alphas( block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha ) # remove block dim/alpha without learning rate block_dims, block_alphas, conv_block_dims, conv_block_alphas = remove_block_dims_and_alphas( block_dims, block_alphas, conv_block_dims, conv_block_alphas, down_lr_weight, mid_lr_weight, up_lr_weight ) else: block_alphas = None conv_block_dims = None conv_block_alphas = None # rank/module dropout rank_dropout = kwargs.get("rank_dropout", None) if rank_dropout is not None: rank_dropout = float(rank_dropout) module_dropout = kwargs.get("module_dropout", None) if module_dropout is not None: module_dropout = float(module_dropout) # すごく引数が多いな ( ^ω^)･･･ network = LoRANetwork( text_encoder, unet, multiplier=multiplier, lora_dim=network_dim, alpha=network_alpha, dropout=neuron_dropout, rank_dropout=rank_dropout, module_dropout=module_dropout, conv_lora_dim=conv_dim, conv_alpha=conv_alpha, block_dims=block_dims, block_alphas=block_alphas, conv_block_dims=conv_block_dims, conv_block_alphas=conv_block_alphas, varbose=True, ) 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 # このメソッドは外部から呼び出される可能性を考慮しておく # network_dim, network_alpha にはデフォルト値が入っている。 # block_dims, block_alphas は両方ともNoneまたは両方とも値が入っている # conv_dim, conv_alpha は両方ともNoneまたは両方とも値が入っている def get_block_dims_and_alphas( block_dims, block_alphas, network_dim, network_alpha, conv_block_dims, conv_block_alphas, conv_dim, conv_alpha ): num_total_blocks = LoRANetwork.NUM_OF_BLOCKS * 2 + 1 def parse_ints(s): return [int(i) for i in s.split(",")] def parse_floats(s): return [float(i) for i in s.split(",")] # block_dimsとblock_alphasをパースする。必ず値が入る 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: logger.warning(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: logger.warning( 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 # conv_block_dimsとconv_block_alphasを、指定がある場合のみパースする。指定がなければconv_dimとconv_alphaを使う 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 logger.warning( 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: logger.warning( 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 # 層別学習率用に層ごとの学習率に対する倍率を定義する、外部から呼び出される可能性を考慮しておく def get_block_lr_weight( down_lr_weight, mid_lr_weight, up_lr_weight, zero_threshold ) -> Tuple[List[float], List[float], List[float]]: # パラメータ未指定時は何もせず、今までと同じ動作とする 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 # フルモデル相当でのup,downの層の数 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: logger.error( "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): logger.warning("down_weight or up_weight is too long. Parameters after %d-th are ignored." % max_len) logger.warning("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): logger.warning("down_weight or up_weight is too short. Parameters after %d-th are filled with 1." % max_len) logger.warning("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): logger.info("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] logger.info(f"down_lr_weight (shallower -> deeper, 浅い層->深い層): {down_lr_weight}") else: logger.info("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 logger.info(f"mid_lr_weight: {mid_lr_weight}") else: logger.info("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] logger.info(f"up_lr_weight (deeper -> shallower, 深い層->浅い層): {up_lr_weight}") else: logger.info("up_lr_weight: all 1.0, すべて1.0") return down_lr_weight, mid_lr_weight, up_lr_weight # lr_weightが0のblockをblock_dimsから除外する、外部から呼び出す可能性を考慮しておく 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 ): # set 0 to block dim without learning rate to remove the block 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 # 外部から呼び出す可能性を考慮しておく def get_block_index(lora_name: str) -> int: block_idx = -1 # invalid lora name 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 # 0に該当するLoRAは存在しない elif g[0] == "up": block_idx = LoRANetwork.NUM_OF_BLOCKS + 1 + idx elif "mid_block_" in lora_name: block_idx = LoRANetwork.NUM_OF_BLOCKS # idx=12 return block_idx # Create network from weights for inference, weights are not loaded here (because can be merged) 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") # get dim/alpha mapping 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 # logger.info(lora_name, value.size(), dim) # support old LoRA without alpha 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 ) # block lr 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 # フルモデル相当でのup,downの層の数 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" # SDXL: must starts with LORA_PREFIX_TEXT_ENCODER 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: logger.info(f"create LoRA network from weights") elif block_dims is not None: logger.info(f"create LoRA network from block_dims") logger.info(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}") logger.info(f"block_dims: {block_dims}") logger.info(f"block_alphas: {block_alphas}") if conv_block_dims is not None: logger.info(f"conv_block_dims: {conv_block_dims}") logger.info(f"conv_block_alphas: {conv_block_alphas}") else: logger.info(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}") logger.info(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: logger.info(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}") # create module instances def create_modules( is_unet: bool, text_encoder_idx: Optional[int], # None, 1, 2 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: # モジュール指定あり 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: # U-Netでblock_dims指定あり 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: # 通常、すべて対象とする 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: # skipした情報を出力 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] # create LoRA for text encoder # 毎回すべてのモジュールを作るのは無駄なので要検討 self.text_encoder_loras = [] skipped_te = [] for i, text_encoder in enumerate(text_encoders): if len(text_encoders) > 1: index = i + 1 logger.info(f"create LoRA for Text Encoder {index}:") else: index = None logger.info(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 logger.info(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.") # extend U-Net target modules if conv2d 3x3 is enabled, or load from weights 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) logger.info(f"create LoRA for U-Net: {len(self.unet_loras)} modules.") skipped = skipped_te + skipped_un if varbose and len(skipped) > 0: logger.warning( 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: logger.info(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 # 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 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: 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: 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_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: 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") # 層別学習率用に層ごとの学習率に対する倍率を定義する　引数の順番が逆だがとりあえず気にしない 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 # 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): self.requires_grad_(True) all_params = [] def enumerate_params(loras: List[LoRAModule]): params = [] for lora in loras: # params.extend(lora.parameters()) params.extend(lora.get_trainable_params()) 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: # 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) 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(): 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): # 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 from library import train_util # 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) # mask is a tensor with values from 0 to 1 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 # create masks mask = self.mask mask_dic = {} mask = mask.unsqueeze(0).unsqueeze(1) # b(1),c(1),h,w 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): # logger.info(mh, mw, mh * mw) m = torch.nn.functional.interpolate(mask, (mh, mw), mode="bilinear") # doesn't work in bf16 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: # add extra shape if h/w is not divisible by 2 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): # 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)