Add block dim(rank) feature

networks/extract_lora_from_models.py

@@ -145,8 +145,8 @@ def svd(args):
     lora_sd[lora_name + '.alpha'] = torch.tensor(down_weight.size()[0])
 
   # load state dict to LoRA and save it
-  lora_network_save = lora.create_network_from_weights(1.0, None, None, text_encoder_o, unet_o, weights_sd=lora_sd)
-  lora_network_save.apply_to(text_encoder_o, unet_o)        # create internal module references for state_dict
+  lora_network_save, lora_sd = lora.create_network_from_weights(1.0, None, None, text_encoder_o, unet_o, weights_sd=lora_sd)
+  lora_network_save.apply_to(text_encoder_o, unet_o)  # create internal module references for state_dict  
 
   info = lora_network_save.load_state_dict(lora_sd)
   print(f"Loading extracted LoRA weights: {info}")

networks/lora.py

@@ -143,6 +143,8 @@ class LoRAModule(torch.nn.Module):
 def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, unet, **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)
@@ -154,34 +156,50 @@ def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, un
         else:
             conv_alpha = float(conv_alpha)
 
-    """
-    block_dims = kwargs.get("block_dims")
-    block_alphas = None
+    # block dim/alpha/lr
+    block_dims = kwargs.get("block_dims", None)
+    down_lr_weight = kwargs.get("down_lr_weight", None)
+    mid_lr_weight = kwargs.get("mid_lr_weight", None)
+    up_lr_weight = kwargs.get("up_lr_weight", None)
+
+    # 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
+        )
+
+        # 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(kwargs.get("block_lr_zero_threshold", 0.0))
+        )
+
+        # 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
+        )
 
-    if block_dims is not None:
-    block_dims = [int(d) for d in block_dims.split(',')]
-    assert len(block_dims) == NUM_BLOCKS, f"Number of block dimensions is not same to {NUM_BLOCKS}"
-    block_alphas = kwargs.get("block_alphas")
-    if block_alphas is None:
-        block_alphas = [1] * len(block_dims)
     else:
-        block_alphas = [int(a) for a in block_alphas(',')]
-    assert len(block_alphas) == NUM_BLOCKS, f"Number of block alphas is not same to {NUM_BLOCKS}"
-
-    conv_block_dims = kwargs.get("conv_block_dims")
-    conv_block_alphas = None
-
-    if conv_block_dims is not None:
-    conv_block_dims = [int(d) for d in conv_block_dims.split(',')]
-    assert len(conv_block_dims) == NUM_BLOCKS, f"Number of block dimensions is not same to {NUM_BLOCKS}"
-    conv_block_alphas = kwargs.get("conv_block_alphas")
-    if conv_block_alphas is None:
-        conv_block_alphas = [1] * len(conv_block_dims)
-    else:
-        conv_block_alphas = [int(a) for a in conv_block_alphas(',')]
-    assert len(conv_block_alphas) == NUM_BLOCKS, f"Number of block alphas is not same to {NUM_BLOCKS}"
-    """
+        block_alphas = None
+        conv_block_dims = None
+        conv_block_alphas = None
 
+    # すごく引数が多いな ( ^ω^)･･･
     network = LoRANetwork(
         text_encoder,
         unet,
@@ -190,28 +208,219 @@ def create_network(multiplier, network_dim, network_alpha, vae, text_encoder, un
         alpha=network_alpha,
         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 some parameters are not set, use zero
-    up_lr_weight = kwargs.get("up_lr_weight", None)
-    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 = kwargs.get("down_lr_weight", None)
-    if down_lr_weight is not None:
-        if "," in down_lr_weight:
-            down_lr_weight = [(float(s) if s else 0.0) for s in down_lr_weight.split(",")]
-
-    mid_lr_weight = kwargs.get("mid_lr_weight", None)
-    if mid_lr_weight is not None:
-        mid_lr_weight = float(mid_lr_weight)
-
-    network.set_block_lr_weight(up_lr_weight, mid_lr_weight, down_lr_weight, float(kwargs.get("block_lr_zero_threshold", 0.0)))
+    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, **kwargs):
     if weights_sd is None:
         if os.path.splitext(file)[1] == ".safetensors":
@@ -242,8 +451,7 @@ def create_network_from_weights(multiplier, file, vae, text_encoder, unet, weigh
             modules_alpha = modules_dim[key]
 
     network = LoRANetwork(text_encoder, unet, multiplier=multiplier, modules_dim=modules_dim, modules_alpha=modules_alpha)
-    network.weights_sd = weights_sd
-    return network
+    return network, weights_sd
 
 
 class LoRANetwork(torch.nn.Module):
@@ -265,9 +473,22 @@ class LoRANetwork(torch.nn.Module):
         alpha=1,
         conv_lora_dim=None,
         conv_alpha=None,
+        block_dims=None,
+        block_alphas=None,
+        conv_block_dims=None,
+        conv_block_alphas=None,
         modules_dim=None,
         modules_alpha=None,
+        varbose=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
 
@@ -278,62 +499,83 @@ class LoRANetwork(torch.nn.Module):
 
         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"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}")
-
-        self.apply_to_conv2d_3x3 = self.conv_lora_dim is not None
-        if self.apply_to_conv2d_3x3:
-            if self.conv_alpha is None:
-                self.conv_alpha = self.alpha
-            print(f"apply LoRA to Conv2d with kernel size (3,3). dim (rank): {self.conv_lora_dim}, alpha: {self.conv_alpha}")
+            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(prefix, root_module: torch.nn.Module, target_replace_modules) -> List[LoRAModule]:
+        def create_modules(is_unet, root_module: torch.nn.Module, target_replace_modules) -> List[LoRAModule]:
+            prefix = LoRANetwork.LORA_PREFIX_UNET if is_unet else LoRANetwork.LORA_PREFIX_TEXT_ENCODER
             loras = []
+            skipped = []
             for name, module in root_module.named_modules():
                 if module.__class__.__name__ in target_replace_modules:
-                    # TODO get block index here
                     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:
-                                if lora_name not in modules_dim:
-                                    continue  # no LoRA module in this weights file
-                                dim = modules_dim[lora_name]
-                                alpha = modules_alpha[lora_name]
+                                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:
+                                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:
                                 if is_linear or is_conv2d_1x1:
                                     dim = self.lora_dim
                                     alpha = self.alpha
-                                elif self.apply_to_conv2d_3x3:
+                                elif self.conv_lora_dim is not None:
                                     dim = self.conv_lora_dim
                                     alpha = self.conv_alpha
-                                else:
-                                    continue
+
+                            if dim is None or dim == 0:
+                                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 = LoRAModule(lora_name, child_module, self.multiplier, dim, alpha)
                             loras.append(lora)
-            return loras
+            return loras, skipped
 
-        self.text_encoder_loras = create_modules(
-            LoRANetwork.LORA_PREFIX_TEXT_ENCODER, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE
-        )
+        self.text_encoder_loras, skipped_te = create_modules(False, text_encoder, LoRANetwork.TEXT_ENCODER_TARGET_REPLACE_MODULE)
         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:
+        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 = create_modules(LoRANetwork.LORA_PREFIX_UNET, unet, target_modules)
+        self.unet_loras, skipped_un = create_modules(True, unet, target_modules)
         print(f"create LoRA for U-Net: {len(self.unet_loras)} modules.")
 
-        self.weights_sd = None
+        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
@@ -351,39 +593,7 @@ class LoRANetwork(torch.nn.Module):
         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, safe_open
-
-            self.weights_sd = load_file(file)
-        else:
-            self.weights_sd = torch.load(file, map_location="cpu")
-
-    def apply_to(self, text_encoder, unet, apply_text_encoder=None, apply_unet=None):
-        if self.weights_sd:
-            weights_has_text_encoder = weights_has_unet = False
-            for key in self.weights_sd.keys():
-                if key.startswith(LoRANetwork.LORA_PREFIX_TEXT_ENCODER):
-                    weights_has_text_encoder = True
-                elif key.startswith(LoRANetwork.LORA_PREFIX_UNET):
-                    weights_has_unet = True
-
-            if apply_text_encoder is None:
-                apply_text_encoder = weights_has_text_encoder
-            else:
-                assert (
-                    apply_text_encoder == weights_has_text_encoder
-                ), f"text encoder weights: {weights_has_text_encoder} but text encoder flag: {apply_text_encoder} / 重みとText Encoderのフラグが矛盾しています"
-
-            if apply_unet is None:
-                apply_unet = weights_has_unet
-            else:
-                assert (
-                    apply_unet == weights_has_unet
-                ), f"u-net weights: {weights_has_unet} but u-net flag: {apply_unet} / 重みとU-Netのフラグが矛盾しています"
-        else:
-            assert apply_text_encoder is not None and apply_unet is not None, f"internal error: flag not set"
-
+    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:
@@ -394,32 +604,14 @@ class LoRANetwork(torch.nn.Module):
         else:
             self.unet_loras = []
 
-        skipped = []
         for lora in self.text_encoder_loras + self.unet_loras:
-            if self.block_lr and self.get_lr_weight(lora) == 0:  # no LR weight
-                skipped.append(lora.lora_name)
-                continue
             lora.apply_to()
             self.add_module(lora.lora_name, lora)
 
-        if len(skipped) > 0:
-            print(
-                f"because block_lr_weight is 0, {len(skipped)} LoRA modules are skipped / block_lr_weight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}")
-
-        if self.weights_sd:
-            # if some weights are not in state dict, it is ok because initial LoRA does nothing (lora_up is initialized by zeros)
-            info = self.load_state_dict(self.weights_sd, False)
-            print(f"weights are loaded: {info}")
-
     # TODO refactor to common function with apply_to
-    def merge_to(self, text_encoder, unet, dtype, device):
-        assert self.weights_sd is not None, "weights are not loaded"
-
+    def merge_to(self, text_encoder, unet, weights_sd, dtype, device):
         apply_text_encoder = apply_unet = False
-        for key in self.weights_sd.keys():
+        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):
@@ -437,9 +629,9 @@ class LoRANetwork(torch.nn.Module):
 
         for lora in self.text_encoder_loras + self.unet_loras:
             sd_for_lora = {}
-            for key in self.weights_sd.keys():
+            for key in weights_sd.keys():
                 if key.startswith(lora.lora_name):
-                    sd_for_lora[key[len(lora.lora_name) + 1 :]] = self.weights_sd[key]
+                    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")
@@ -447,113 +639,18 @@ class LoRANetwork(torch.nn.Module):
     # 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
     def set_block_lr_weight(
         self,
-        up_lr_weight: Union[List[float], str] = None,
+        up_lr_weight: List[float] = None,
         mid_lr_weight: float = None,
-        down_lr_weight: Union[List[float], str] = None,
-        zero_threshold: float = 0.0,
+        down_lr_weight: List[float] = None,
     ):
-        # 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
-
-        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。")
-            self.block_lr = True
-
-            if down_lr_weight != None:
-                self.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):", self.down_lr_weight)
-            else:
-                print("down_lr_weight: all 1.0, すべて1.0")
-
-            if mid_lr_weight != None:
-                self.mid_lr_weight = mid_lr_weight if mid_lr_weight > zero_threshold else 0
-                print("mid_lr_weight:", self.mid_lr_weight)
-            else:
-                print("mid_lr_weight: 1.0")
-
-            if up_lr_weight != None:
-                self.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):", self.up_lr_weight)
-            else:
-                print("up_lr_weight: all 1.0, すべて1.0")
-
-        return
-
-    def get_block_index(self, lora: LoRAModule) -> int:
-        block_idx = -1  # invalid lora name
-
-        m = RE_UPDOWN.search(lora.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.lora_name:
-            block_idx = LoRANetwork.NUM_OF_BLOCKS  # idx=12
-
-        return block_idx
+        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 = self.get_block_index(lora)
+        block_idx = get_block_index(lora.lora_name)
         if block_idx < 0:
             return lr_weight
 
@@ -590,7 +687,7 @@ class LoRANetwork(torch.nn.Module):
                 # 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 = self.get_block_index(lora)
+                    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)