import diffusers import diffusers.models.lora as diffusers_lora # from modules import shared import modules.shared as shared lora_state = { # TODO Lora state for Diffusers 'multiplier': [], 'active': False, 'loaded': 0, 'all_loras': [] } def unload_diffusers_lora(): try: pipe = shared.sd_model if shared.opts.diffusers_lora_loader == "diffusers default": pipe.unload_lora_weights() pipe._remove_text_encoder_monkey_patch() # pylint: disable=W0212 proc_cls_name = next(iter(pipe.unet.attn_processors.values())).__class__.__name__ non_lora_proc_cls = getattr(diffusers.models.attention_processor, proc_cls_name)#[len("LORA"):]) pipe.unet.set_attn_processor(non_lora_proc_cls()) # shared.log.debug('Diffusers LoRA unloaded') else: lora_state['all_loras'].reverse() lora_state['multiplier'].reverse() for i, lora_network in enumerate(lora_state['all_loras']): if shared.opts.diffusers_lora_loader == "merge and apply": lora_network.restore_from(multiplier=lora_state['multiplier'][i]) if shared.opts.diffusers_lora_loader == "sequential apply": lora_network.unapply_to() lora_state['active'] = False lora_state['loaded'] = 0 lora_state['all_loras'] = [] lora_state['multiplier'] = [] except Exception as e: shared.log.error(f"Diffusers LoRA unloading failed: {e}") def load_diffusers_lora(name, lora, strength = 1.0): try: pipe = shared.sd_model lora_state['active'] = True lora_state['loaded'] += 1 lora_state['multiplier'].append(strength) if shared.opts.diffusers_lora_loader == "diffusers default": pipe.load_lora_weights(lora.filename, cache_dir=shared.opts.diffusers_dir, local_files_only=True, lora_scale=strength) shared.log.info(f"LoRA loaded: {name} {lora_state['multiplier']}") else: from safetensors.torch import load_file lora_sd = load_file(lora.filename) if "XL" in pipe.__class__.__name__: text_encoders = [pipe.text_encoder, pipe.text_encoder_2] else: text_encoders = pipe.text_encoder lora_network: LoRANetwork = create_network_from_weights(text_encoders, pipe.unet, lora_sd, multiplier=strength) lora_network.load_state_dict(lora_sd) if shared.opts.diffusers_lora_loader == "merge and apply": lora_network.merge_to(multiplier=strength) if shared.opts.diffusers_lora_loader == "sequential apply": lora_network.to(shared.device, dtype=pipe.unet.dtype) lora_network.apply_to(multiplier=strength) lora_state['all_loras'].append(lora_network) shared.log.info(f"LoRA loaded: {name}:{strength} loader={shared.opts.diffusers_lora_loader}") except Exception as e: shared.log.error(f"Diffusers LoRA loading failed: {name} {e}") # Diffusersで動くLoRA。このファイル単独で完結する。 # LoRA module for Diffusers. This file works independently. import bisect import math from typing import Any, Dict, List, Mapping, Optional, Union from diffusers import UNet2DConditionModel from tqdm import tqdm from transformers import CLIPTextModel import torch def make_unet_conversion_map() -> Dict[str, str]: unet_conversion_map_layer = [] for i in range(3): # num_blocks is 3 in sdxl # loop over downblocks/upblocks for j in range(2): # loop over resnets/attentions for downblocks hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}." sd_down_res_prefix = f"input_blocks.{3*i + j + 1}.0." unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix)) if i < 3: # no attention layers in down_blocks.3 hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}." sd_down_atn_prefix = f"input_blocks.{3*i + j + 1}.1." unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix)) for j in range(3): # loop over resnets/attentions for upblocks hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}." sd_up_res_prefix = f"output_blocks.{3*i + j}.0." unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix)) # if i > 0: commentout for sdxl # no attention layers in up_blocks.0 hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}." sd_up_atn_prefix = f"output_blocks.{3*i + j}.1." unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix)) if i < 3: # no downsample in down_blocks.3 hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv." sd_downsample_prefix = f"input_blocks.{3*(i+1)}.0.op." unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix)) # no upsample in up_blocks.3 hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0." sd_upsample_prefix = f"output_blocks.{3*i + 2}.{2}." # change for sdxl unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix)) hf_mid_atn_prefix = "mid_block.attentions.0." sd_mid_atn_prefix = "middle_block.1." unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix)) for j in range(2): hf_mid_res_prefix = f"mid_block.resnets.{j}." sd_mid_res_prefix = f"middle_block.{2*j}." unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix)) unet_conversion_map_resnet = [ # (stable-diffusion, HF Diffusers) ("in_layers.0.", "norm1."), ("in_layers.2.", "conv1."), ("out_layers.0.", "norm2."), ("out_layers.3.", "conv2."), ("emb_layers.1.", "time_emb_proj."), ("skip_connection.", "conv_shortcut."), ] unet_conversion_map = [] for sd, hf in unet_conversion_map_layer: if "resnets" in hf: for sd_res, hf_res in unet_conversion_map_resnet: unet_conversion_map.append((sd + sd_res, hf + hf_res)) else: unet_conversion_map.append((sd, hf)) for j in range(2): hf_time_embed_prefix = f"time_embedding.linear_{j+1}." sd_time_embed_prefix = f"time_embed.{j*2}." unet_conversion_map.append((sd_time_embed_prefix, hf_time_embed_prefix)) for j in range(2): hf_label_embed_prefix = f"add_embedding.linear_{j+1}." sd_label_embed_prefix = f"label_emb.0.{j*2}." unet_conversion_map.append((sd_label_embed_prefix, hf_label_embed_prefix)) unet_conversion_map.append(("input_blocks.0.0.", "conv_in.")) unet_conversion_map.append(("out.0.", "conv_norm_out.")) unet_conversion_map.append(("out.2.", "conv_out.")) sd_hf_conversion_map = {sd.replace(".", "_")[:-1]: hf.replace(".", "_")[:-1] for sd, hf in unet_conversion_map} return sd_hf_conversion_map UNET_CONVERSION_MAP = make_unet_conversion_map() 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, ): """if alpha == 0 or None, alpha is rank (no scaling).""" super().__init__() self.lora_name = lora_name if isinstance(org_module, diffusers_lora.LoRACompatibleConv): #Modified to support Diffusers>=0.19.2 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 self.lora_dim = lora_dim if isinstance(org_module, diffusers_lora.LoRACompatibleConv): #Modified to support Diffusers>=0.19.2 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 isinstance(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)) # 勾配計算に含めない / not included in gradient calculation # 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] self.enabled = True self.network: LoRANetwork = None self.org_forward = None # override org_module's forward method def apply_to(self, multiplier=None): if multiplier is not None: self.multiplier = multiplier if self.org_forward is None: self.org_forward = self.org_module[0].forward self.org_module[0].forward = self.forward # restore org_module's forward method def unapply_to(self): if self.org_forward is not None: self.org_module[0].forward = self.org_forward # forward with lora def forward(self, x): if not self.enabled: return self.org_forward(x) return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale def set_network(self, network): self.network = network # merge lora weight to org weight def merge_to(self, multiplier=1.0): # get lora weight lora_weight = self.get_weight(multiplier) # get org weight org_sd = self.org_module[0].state_dict() org_weight = org_sd["weight"] weight = org_weight + lora_weight.to(org_weight.device, dtype=org_weight.dtype) # set weight to org_module org_sd["weight"] = weight self.org_module[0].load_state_dict(org_sd) # restore org weight from lora weight def restore_from(self, multiplier=1.0): # get lora weight lora_weight = self.get_weight(multiplier) # get org weight org_sd = self.org_module[0].state_dict() org_weight = org_sd["weight"] weight = org_weight - lora_weight.to(org_weight.device, dtype=org_weight.dtype) # set weight to org_module org_sd["weight"] = weight self.org_module[0].load_state_dict(org_sd) # return lora weight 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 # Create network from weights for inference, weights are not loaded here def create_network_from_weights( text_encoder: Union[CLIPTextModel, List[CLIPTextModel]], unet: UNet2DConditionModel, weights_sd: Dict, multiplier: float = 1.0 ): # 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] return LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha) def merge_lora_weights(pipe, weights_sd: Dict, multiplier: float = 1.0): text_encoders = [pipe.text_encoder, pipe.text_encoder_2] if hasattr(pipe, "text_encoder_2") else [pipe.text_encoder] unet = pipe.unet lora_network = create_network_from_weights(text_encoders, unet, weights_sd, multiplier=multiplier) lora_network.load_state_dict(weights_sd) lora_network.merge_to(multiplier=multiplier) # block weightや学習に対応しない簡易版 / simple version without block weight and training class LoRANetwork(torch.nn.Module): # pylint: disable=abstract-method 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: UNet2DConditionModel, multiplier: float = 1.0, modules_dim: Optional[Dict[str, int]] = None, modules_alpha: Optional[Dict[str, int]] = None, varbose: Optional[bool] = False, # pylint: disable=unused-argument ) -> None: super().__init__() self.multiplier = multiplier # shared.log.debug("create LoRA network from weights") # convert SDXL Stability AI's U-Net modules to Diffusers converted = self.convert_unet_modules(modules_dim, modules_alpha) if converted: shared.log.debug(f"LoRA convert: modules={converted} SDXL SAI/SGM to Diffusers") # 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 = isinstance(child_module, (torch.nn.Linear, diffusers_lora.LoRACompatibleLinear)) #Modified to support Diffusers>=0.19.2 is_conv2d = isinstance(child_module, (torch.nn.Conv2d, diffusers_lora.LoRACompatibleConv)) #Modified to support Diffusers>=0.19.2 if is_linear or is_conv2d: lora_name = prefix + "." + name + "." + child_name lora_name = lora_name.replace(".", "_") if lora_name not in modules_dim: # print(f"skipped {lora_name} (not found in modules_dim)") skipped.append(lora_name) continue dim = modules_dim[lora_name] alpha = modules_alpha[lora_name] lora = LoRAModule( lora_name, child_module, self.multiplier, dim, alpha, ) 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 / it is wasteful to create all modules every time, need to consider self.text_encoder_loras: List[LoRAModule] = [] skipped_te = [] for i, text_encoder in enumerate(text_encoders): if len(text_encoders) > 1: index = i + 1 else: index = None 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 # extend U-Net target modules to include Conv2d 3x3 target_modules = LoRANetwork.UNET_TARGET_REPLACE_MODULE + LoRANetwork.UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 self.unet_loras: List[LoRAModule] self.unet_loras, skipped_un = create_modules(True, None, unet, target_modules) shared.log.debug(f"LoRA modules loaded/skipped: te={len(self.text_encoder_loras)}/{len(skipped_te)} unet={len(self.unet_loras)}/skip={len(skipped_un)}") # assertion names = set() for lora in self.text_encoder_loras + self.unet_loras: names.add(lora.lora_name) for lora_name in modules_dim.keys(): assert lora_name in names, f"{lora_name} is not found in created LoRA modules." # make to work load_state_dict for lora in self.text_encoder_loras + self.unet_loras: self.add_module(lora.lora_name, lora) # SDXL: convert SDXL Stability AI's U-Net modules to Diffusers def convert_unet_modules(self, modules_dim, modules_alpha): converted_count = 0 not_converted_count = 0 map_keys = list(UNET_CONVERSION_MAP.keys()) map_keys.sort() for key in list(modules_dim.keys()): if key.startswith(LoRANetwork.LORA_PREFIX_UNET + "_"): search_key = key.replace(LoRANetwork.LORA_PREFIX_UNET + "_", "") position = bisect.bisect_right(map_keys, search_key) map_key = map_keys[position - 1] if search_key.startswith(map_key): new_key = key.replace(map_key, UNET_CONVERSION_MAP[map_key]) modules_dim[new_key] = modules_dim[key] modules_alpha[new_key] = modules_alpha[key] del modules_dim[key] del modules_alpha[key] converted_count += 1 else: not_converted_count += 1 assert ( converted_count == 0 or not_converted_count == 0 ), f"some modules are not converted: {converted_count} converted, {not_converted_count} not converted" return converted_count def set_multiplier(self, multiplier): self.multiplier = multiplier for lora in self.text_encoder_loras + self.unet_loras: lora.multiplier = self.multiplier def apply_to(self, multiplier=1.0, apply_text_encoder=True, apply_unet=True): if apply_text_encoder: # shared.log.debug("LoRA apply for text encoder") for lora in self.text_encoder_loras: lora.apply_to(multiplier) if apply_unet: # shared.log.debug("LoRA apply for U-Net") for lora in self.unet_loras: lora.apply_to(multiplier) def unapply_to(self): for lora in self.text_encoder_loras + self.unet_loras: lora.unapply_to() def merge_to(self, multiplier=1.0): # shared.log.debug("LoRA merge weights for text encoder") for lora in tqdm(self.text_encoder_loras + self.unet_loras): lora.merge_to(multiplier) def restore_from(self, multiplier=1.0): # shared.log.debug("LoRA restore weights") for lora in tqdm(self.text_encoder_loras + self.unet_loras): lora.restore_from(multiplier) def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True): # convert SDXL Stability AI's state dict to Diffusers' based state dict map_keys = list(UNET_CONVERSION_MAP.keys()) # prefix of U-Net modules map_keys.sort() for key in list(state_dict.keys()): if key.startswith(LoRANetwork.LORA_PREFIX_UNET + "_"): search_key = key.replace(LoRANetwork.LORA_PREFIX_UNET + "_", "") position = bisect.bisect_right(map_keys, search_key) map_key = map_keys[position - 1] if search_key.startswith(map_key): new_key = key.replace(map_key, UNET_CONVERSION_MAP[map_key]) state_dict[new_key] = state_dict[key] del state_dict[key] # in case of V2, some weights have different shape, so we need to convert them # because V2 LoRA is based on U-Net created by use_linear_projection=False my_state_dict = self.state_dict() for key in state_dict.keys(): if state_dict[key].size() != my_state_dict[key].size(): # print(f"convert {key} from {state_dict[key].size()} to {my_state_dict[key].size()}") state_dict[key] = state_dict[key].view(my_state_dict[key].size()) return super().load_state_dict(state_dict, strict)