# 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 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 RE_UPDOWN = re.compile(r"(up|down)_blocks_(\d+)_(resnets|upsamplers|downsamplers|attentions)_(\d+)_") 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: # print(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)) 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 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) # print(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): # print("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) # print("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 :] # print("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 # print("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): # print("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) # print("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 # print("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 # print("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: print(f"block_dims is not specified. all dims are set to {network_dim} / block_dimsが指定されていません。すべてのdimは{network_dim}になります") block_dims = [network_dim] * num_total_blocks if block_alphas is not None: block_alphas = parse_floats(block_alphas) assert ( len(block_alphas) == num_total_blocks ), f"block_alphas must have {num_total_blocks} elements / block_alphasは{num_total_blocks}個指定してください" else: print( f"block_alphas is not specified. all alphas are set to {network_alpha} / block_alphasが指定されていません。すべてのalphaは{network_alpha}になります" ) block_alphas = [network_alpha] * num_total_blocks # 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 print( f"conv_block_alphas is not specified. all alphas are set to {conv_alpha} / conv_block_alphasが指定されていません。すべてのalphaは{conv_alpha}になります" ) conv_block_alphas = [conv_alpha] * num_total_blocks else: if conv_dim is not None: print( f"conv_dim/alpha for all blocks are set to {conv_dim} and {conv_alpha} / すべてのブロックのconv_dimとalphaは{conv_dim}および{conv_alpha}になります" ) conv_block_dims = [conv_dim] * num_total_blocks conv_block_alphas = [conv_alpha] * num_total_blocks else: conv_block_dims = None conv_block_alphas = None return block_dims, block_alphas, conv_block_dims, conv_block_alphas # 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: print( "Unknown lr_weight argument %s is used. Valid arguments: / 不明なlr_weightの引数 %s が使われました。有効な引数:\n\tcosine, sine, linear, reverse_linear, zeros" % (name) ) return None if type(down_lr_weight) == str: down_lr_weight = get_list(down_lr_weight) if type(up_lr_weight) == str: up_lr_weight = get_list(up_lr_weight) if (up_lr_weight != None and len(up_lr_weight) > max_len) or (down_lr_weight != None and len(down_lr_weight) > max_len): print("down_weight or up_weight is too long. Parameters after %d-th are ignored." % max_len) print("down_weightもしくはup_weightが長すぎます。%d個目以降のパラメータは無視されます。" % max_len) up_lr_weight = up_lr_weight[:max_len] down_lr_weight = down_lr_weight[:max_len] if (up_lr_weight != None and len(up_lr_weight) < max_len) or (down_lr_weight != None and len(down_lr_weight) < max_len): print("down_weight or up_weight is too short. Parameters after %d-th are filled with 1." % max_len) print("down_weightもしくはup_weightが短すぎます。%d個目までの不足したパラメータは1で補われます。" % max_len) if down_lr_weight != None and len(down_lr_weight) < max_len: down_lr_weight = down_lr_weight + [1.0] * (max_len - len(down_lr_weight)) if up_lr_weight != None and len(up_lr_weight) < max_len: up_lr_weight = up_lr_weight + [1.0] * (max_len - len(up_lr_weight)) if (up_lr_weight != None) or (mid_lr_weight != None) or (down_lr_weight != None): print("apply block learning rate / 階層別学習率を適用します。") if down_lr_weight != None: down_lr_weight = [w if w > zero_threshold else 0 for w in down_lr_weight] print("down_lr_weight (shallower -> deeper, 浅い層->深い層):", down_lr_weight) else: print("down_lr_weight: all 1.0, すべて1.0") if mid_lr_weight != None: mid_lr_weight = mid_lr_weight if mid_lr_weight > zero_threshold else 0 print("mid_lr_weight:", mid_lr_weight) else: print("mid_lr_weight: 1.0") if up_lr_weight != None: up_lr_weight = [w if w > zero_threshold else 0 for w in up_lr_weight] print("up_lr_weight (deeper -> shallower, 深い層->浅い層):", up_lr_weight) else: print("up_lr_weight: all 1.0, すべて1.0") return down_lr_weight, mid_lr_weight, up_lr_weight # 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 # print(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: print(f"create LoRA network from weights") elif block_dims is not None: print(f"create LoRA network from block_dims") print(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}") print(f"block_dims: {block_dims}") print(f"block_alphas: {block_alphas}") if conv_block_dims is not None: print(f"conv_block_dims: {conv_block_dims}") print(f"conv_block_alphas: {conv_block_alphas}") else: print(f"create LoRA network. base dim (rank): {lora_dim}, alpha: {alpha}") print(f"neuron dropout: p={self.dropout}, rank dropout: p={self.rank_dropout}, module dropout: p={self.module_dropout}") if self.conv_lora_dim is not None: print(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}") # 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] print(text_encoders) # 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 print(f"create LoRA for Text Encoder {index}:") else: index = None print(f"create LoRA for Text Encoder:") print(text_encoder) text_encoder_loras, skipped = create_modules(False, index, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE) self.text_encoder_loras.extend(text_encoder_loras) skipped_te += skipped print(f"create LoRA for Text Encoder: {len(self.text_encoder_loras)} modules.") # 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) print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.") skipped = skipped_te + skipped_un if varbose and len(skipped) > 0: print( f"because block_lr_weight is 0 or dim (rank) is 0, {len(skipped)} LoRA modules are skipped / block_lr_weightまたはdim (rank)が0の為、次の{len(skipped)}個のLoRAモジュールはスキップされます:" ) for name in skipped: print(f"\t{name}") self.up_lr_weight: List[float] = None self.down_lr_weight: List[float] = None self.mid_lr_weight: float = None self.block_lr = False # 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: print("enable LoRA for text encoder") else: self.text_encoder_loras = [] if apply_unet: print("enable LoRA for U-Net") else: self.unet_loras = [] for lora in self.text_encoder_loras + self.unet_loras: lora.apply_to() self.add_module(lora.lora_name, lora) # 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: print("enable LoRA for text encoder") else: self.text_encoder_loras = [] if apply_unet: print("enable LoRA for U-Net") else: self.unet_loras = [] for lora in self.text_encoder_loras + self.unet_loras: sd_for_lora = {} for key in weights_sd.keys(): if key.startswith(lora.lora_name): sd_for_lora[key[len(lora.lora_name) + 1 :]] = weights_sd[key] lora.merge_to(sd_for_lora, dtype, device) print(f"weights are merged") # 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): params = [] for lora in loras: params.extend(lora.parameters()) return params if self.text_encoder_loras: param_data = {"params": enumerate_params(self.text_encoder_loras)} if text_encoder_lr is not None: param_data["lr"] = text_encoder_lr all_params.append(param_data) if self.unet_loras: if self.block_lr: # 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): # print(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)