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README.md

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+# a1111-sd-webui-locon
+
+An extension for loading lycoris model in sd-webui. (include locon and loha)
+
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+# THIS EXTENSION IS NOT FOR ADDITIONAL NETWORK
+
+### LyCORIS
+https://github.com/KohakuBlueleaf/LyCORIS
+
+### usage
+Install and use locon model as lora model. <br>
+Make sure your sd-webui has built-in lora
+
+![image](https://user-images.githubusercontent.com/59680068/222327303-9ba4f702-5821-48db-a849-337dce9b11bb.png)

locon.py

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+'''
+https://github.com/KohakuBlueleaf/LoCon
+'''
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LoConModule(nn.Module):
+    """
+    modifed from kohya-ss/sd-scripts/networks/lora:LoRAModule
+    """
+
+    def __init__(self, lora_name, org_module: 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
+        self.lora_dim = lora_dim
+
+        if org_module.__class__.__name__ == 'Conv2d':
+            # For general LoCon
+            in_dim = org_module.in_channels
+            k_size = org_module.kernel_size
+            stride = org_module.stride
+            padding = org_module.padding
+            out_dim = org_module.out_channels
+            self.lora_down = nn.Conv2d(in_dim, lora_dim, k_size, stride, padding, bias=False)
+            self.lora_up = nn.Conv2d(lora_dim, out_dim, (1, 1), bias=False)
+        else:
+            in_dim = org_module.in_features
+            out_dim = org_module.out_features
+            self.lora_down = nn.Linear(in_dim, lora_dim, bias=False)
+            self.lora_up = nn.Linear(lora_dim, out_dim, bias=False)
+
+        if type(alpha) == torch.Tensor:
+            alpha = alpha.detach().float().numpy()  # without casting, bf16 causes error
+        alpha = 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
+
+    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):
+        return self.org_forward(x) + self.lora_up(self.lora_down(x)) * self.multiplier * self.scale

locon_compvis.py

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+'''
+Hijack version of kohya-ss/additional_networks/scripts/lora_compvis.py
+'''
+# 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 copy
+import math
+import re
+from typing import NamedTuple
+import torch
+from locon import LoConModule
+
+
+class LoRAInfo(NamedTuple):
+    lora_name: str
+    module_name: str
+    module: torch.nn.Module
+    multiplier: float
+    dim: int
+    alpha: float
+
+
+def create_network_and_apply_compvis(du_state_dict, multiplier_tenc, multiplier_unet, text_encoder, unet, **kwargs):
+    # get device and dtype from unet
+    for module in unet.modules():
+        if module.__class__.__name__ == "Linear":
+            param: torch.nn.Parameter = module.weight
+            # device = param.device
+            dtype = param.dtype
+            break
+
+    # get dims (rank) and alpha from state dict
+    # currently it is assumed all LoRA have same alpha. alpha may be different in future.
+    network_alpha = None
+    conv_alpha = None
+    network_dim = None
+    conv_dim = None
+    for key, value in du_state_dict.items():
+        if network_alpha is None and 'alpha' in key:
+            network_alpha = value
+        if network_dim is None and 'lora_down' in key and len(value.size()) == 2:
+            network_dim = value.size()[0]
+        if network_alpha is not None and network_dim is not None:
+            break
+    if network_alpha is None:
+        network_alpha = network_dim
+
+    print(f"dimension: {network_dim},\n"
+          f"alpha: {network_alpha},\n"
+          f"multiplier_unet: {multiplier_unet},\n"
+          f"multiplier_tenc: {multiplier_tenc}"
+          )
+    if network_dim is None:
+        print(f"The selected model is not LoRA or not trained by `sd-scripts`?")
+        network_dim = 4
+        network_alpha = 1
+
+    # create, apply and load weights
+    network = LoConNetworkCompvis(
+        text_encoder, unet, du_state_dict,
+        multiplier_tenc = multiplier_tenc,
+        multiplier_unet = multiplier_unet,
+    )
+    state_dict = network.apply_lora_modules(du_state_dict)              # some weights are applied to text encoder
+    network.to(dtype)                                              # with this, if error comes from next line, the model will be used
+    info = network.load_state_dict(state_dict, strict=False)
+
+    # remove redundant warnings
+    if len(info.missing_keys) > 4:
+        missing_keys = []
+        alpha_count = 0
+        for key in info.missing_keys:
+            if 'alpha' not in key:
+                missing_keys.append(key)
+            else:
+                if alpha_count == 0:
+                    missing_keys.append(key)
+                alpha_count += 1
+        if alpha_count > 1:
+            missing_keys.append(
+                    f"... and {alpha_count-1} alphas. The model doesn't have alpha, use dim (rannk) as alpha. You can ignore this message.")
+
+        info = torch.nn.modules.module._IncompatibleKeys(missing_keys, info.unexpected_keys)
+
+    return network, info
+
+
+class LoConNetworkCompvis(torch.nn.Module):
+    # UNET_TARGET_REPLACE_MODULE = ["Transformer2DModel", "Attention"]
+    # TEXT_ENCODER_TARGET_REPLACE_MODULE = ["CLIPAttention", "CLIPMLP"]
+    LOCON_TARGET = ["ResBlock", "Downsample", "Upsample"]
+    UNET_TARGET_REPLACE_MODULE = ["SpatialTransformer"] + LOCON_TARGET  # , "Attention"]
+    TEXT_ENCODER_TARGET_REPLACE_MODULE = ["ResidualAttentionBlock", "CLIPAttention", "CLIPMLP"]
+
+    LORA_PREFIX_UNET = 'lora_unet'
+    LORA_PREFIX_TEXT_ENCODER = 'lora_te'
+
+    @classmethod
+    def convert_diffusers_name_to_compvis(cls, v2, du_name):
+        """
+        convert diffusers's LoRA name to CompVis
+        """
+        cv_name = None
+        if "lora_unet_" in du_name:
+            m = re.search(r"_down_blocks_(\d+)_attentions_(\d+)_(.+)", du_name)
+            if m:
+                du_block_index = int(m.group(1))
+                du_attn_index = int(m.group(2))
+                du_suffix = m.group(3)
+
+                cv_index = 1 + du_block_index * 3 + du_attn_index      # 1,2, 4,5, 7,8
+                cv_name = f"lora_unet_input_blocks_{cv_index}_1_{du_suffix}"
+                return cv_name
+            
+            m = re.search(r"_mid_block_attentions_(\d+)_(.+)", du_name)
+            if m:
+                du_suffix = m.group(2)
+                cv_name = f"lora_unet_middle_block_1_{du_suffix}"
+                return cv_name
+            
+            m = re.search(r"_up_blocks_(\d+)_attentions_(\d+)_(.+)", du_name)
+            if m:
+                du_block_index = int(m.group(1))
+                du_attn_index = int(m.group(2))
+                du_suffix = m.group(3)
+
+                cv_index = du_block_index * 3 + du_attn_index      # 3,4,5, 6,7,8, 9,10,11
+                cv_name = f"lora_unet_output_blocks_{cv_index}_1_{du_suffix}"
+                return cv_name
+            
+            m = re.search(r"_down_blocks_(\d+)_resnets_(\d+)_(.+)", du_name)
+            if m:
+                du_block_index = int(m.group(1))
+                du_res_index = int(m.group(2))
+                du_suffix = m.group(3)
+                cv_suffix = {
+                    'conv1': 'in_layers_2',
+                    'conv2': 'out_layers_3',
+                    'time_emb_proj': 'emb_layers_1',
+                    'conv_shortcut': 'skip_connection'
+                }[du_suffix]
+
+                cv_index = 1 + du_block_index * 3 + du_res_index      # 1,2, 4,5, 7,8
+                cv_name = f"lora_unet_input_blocks_{cv_index}_0_{cv_suffix}"
+                return cv_name
+            
+            m = re.search(r"_down_blocks_(\d+)_downsamplers_0_conv", du_name)
+            if m:
+                block_index = int(m.group(1))
+                cv_index = 3 + block_index * 3
+                cv_name = f"lora_unet_input_blocks_{cv_index}_0_op"
+                return cv_name
+            
+            m = re.search(r"_mid_block_resnets_(\d+)_(.+)", du_name)
+            if m:
+                index = int(m.group(1))
+                du_suffix = m.group(2)
+                cv_suffix = {
+                    'conv1': 'in_layers_2',
+                    'conv2': 'out_layers_3',
+                    'time_emb_proj': 'emb_layers_1',
+                    'conv_shortcut': 'skip_connection'
+                }[du_suffix]
+                cv_name = f"lora_unet_middle_block_{index*2}_{cv_suffix}"
+                return cv_name
+            
+            m = re.search(r"_up_blocks_(\d+)_resnets_(\d+)_(.+)", du_name)
+            if m:
+                du_block_index = int(m.group(1))
+                du_res_index = int(m.group(2))
+                du_suffix = m.group(3)
+                cv_suffix = {
+                    'conv1': 'in_layers_2',
+                    'conv2': 'out_layers_3',
+                    'time_emb_proj': 'emb_layers_1',
+                    'conv_shortcut': 'skip_connection'
+                }[du_suffix]
+
+                cv_index = du_block_index * 3 + du_res_index      # 1,2, 4,5, 7,8
+                cv_name = f"lora_unet_output_blocks_{cv_index}_0_{cv_suffix}"
+                return cv_name
+            
+            m = re.search(r"_up_blocks_(\d+)_upsamplers_0_conv", du_name)
+            if m:
+                block_index = int(m.group(1))
+                cv_index = block_index * 3 + 2
+                cv_name = f"lora_unet_output_blocks_{cv_index}_{bool(block_index)+1}_conv"
+                return cv_name
+            
+        elif "lora_te_" in du_name:
+            m = re.search(r"_model_encoder_layers_(\d+)_(.+)", du_name)
+            if m:
+                du_block_index = int(m.group(1))
+                du_suffix = m.group(2)
+
+                cv_index = du_block_index
+                if v2:
+                    if 'mlp_fc1' in du_suffix:
+                        cv_name = f"lora_te_wrapped_model_transformer_resblocks_{cv_index}_{du_suffix.replace('mlp_fc1', 'mlp_c_fc')}"
+                    elif 'mlp_fc2' in du_suffix:
+                        cv_name = f"lora_te_wrapped_model_transformer_resblocks_{cv_index}_{du_suffix.replace('mlp_fc2', 'mlp_c_proj')}"
+                    elif 'self_attn':
+                        # handled later
+                        cv_name = f"lora_te_wrapped_model_transformer_resblocks_{cv_index}_{du_suffix.replace('self_attn', 'attn')}"
+                else:
+                    cv_name = f"lora_te_wrapped_transformer_text_model_encoder_layers_{cv_index}_{du_suffix}"
+
+        assert cv_name is not None, f"conversion failed: {du_name}. the model may not be trained by `sd-scripts`."
+        return cv_name
+
+    @classmethod
+    def convert_state_dict_name_to_compvis(cls, v2, state_dict):
+        """
+        convert keys in state dict to load it by load_state_dict
+        """
+        new_sd = {}
+        for key, value in state_dict.items():
+            tokens = key.split('.')
+            compvis_name = LoConNetworkCompvis.convert_diffusers_name_to_compvis(v2, tokens[0])
+            new_key = compvis_name + '.' + '.'.join(tokens[1:])
+            new_sd[new_key] = value
+
+        return new_sd
+
+    def __init__(self, text_encoder, unet, du_state_dict, multiplier_tenc=1.0, multiplier_unet=1.0) -> None:
+        super().__init__()
+        self.multiplier_unet = multiplier_unet
+        self.multiplier_tenc = multiplier_tenc
+
+        # create module instances
+        for name, module in text_encoder.named_modules():
+            for child_name, child_module in module.named_modules():
+                if child_module.__class__.__name__ == 'MultiheadAttention':
+                    self.v2 = True
+                    break
+            else:
+                continue
+            break
+        else:
+            self.v2 = False
+        comp_state_dict = {}
+
+        def create_modules(prefix, root_module: torch.nn.Module, target_replace_modules, multiplier):
+            nonlocal comp_state_dict
+            loras = []
+            replaced_modules = []
+            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():
+                        layer = child_module.__class__.__name__
+                        lora_name = prefix + '.' + name + '.' + child_name
+                        lora_name = lora_name.replace('.', '_')
+                        if layer == "Linear" or layer == "Conv2d":
+                            if '_resblocks_23_' in lora_name:  # ignore last block in StabilityAi Text Encoder
+                                break
+                            if f'{lora_name}.lora_down.weight' not in comp_state_dict:
+                                if module.__class__.__name__ in LoConNetworkCompvis.LOCON_TARGET:
+                                    continue
+                                else:
+                                    print(f'Cannot find: "{lora_name}", skipped')
+                                    continue
+                            rank = comp_state_dict[f'{lora_name}.lora_down.weight'].shape[0]
+                            alpha = comp_state_dict.get(f'{lora_name}.alpha', torch.tensor(rank)).item()
+                            lora = LoConModule(lora_name, child_module, multiplier, rank, alpha)
+                            loras.append(lora)
+
+                            replaced_modules.append(child_module)
+                        elif child_module.__class__.__name__ == "MultiheadAttention":
+                            # make four modules: not replacing forward method but merge weights
+                            self.v2 = True
+                            for suffix in ['q', 'k', 'v', 'out']:
+                                module_name = prefix + '.' + name + '.' + child_name          # ~.attn
+                                module_name = module_name.replace('.', '_')
+                                if '_resblocks_23_' in module_name:                           # ignore last block in StabilityAi Text Encoder
+                                    break
+                                lora_name = module_name + '_' + suffix
+                                lora_info = LoRAInfo(lora_name, module_name, child_module, multiplier, 0, 0)
+                                loras.append(lora_info)
+
+                                replaced_modules.append(child_module)
+            return loras, replaced_modules
+        
+        for k,v in LoConNetworkCompvis.convert_state_dict_name_to_compvis(self.v2, du_state_dict).items():
+            comp_state_dict[k] = v
+
+        self.text_encoder_loras, te_rep_modules = create_modules(
+            LoConNetworkCompvis.LORA_PREFIX_TEXT_ENCODER,
+            text_encoder, 
+            LoConNetworkCompvis.TEXT_ENCODER_TARGET_REPLACE_MODULE, 
+            self.multiplier_tenc
+        )
+        print(f"create LoCon for Text Encoder: {len(self.text_encoder_loras)} modules.")
+        
+        self.unet_loras, unet_rep_modules = create_modules(
+            LoConNetworkCompvis.LORA_PREFIX_UNET, 
+            unet, 
+            LoConNetworkCompvis.UNET_TARGET_REPLACE_MODULE, 
+            self.multiplier_unet
+        )
+        print(f"create LoCon for U-Net: {len(self.unet_loras)} modules.")
+
+        # make backup of original forward/weights, if multiple modules are applied, do in 1st module only
+        backed_up = False                     # messaging purpose only
+        for rep_module in te_rep_modules + unet_rep_modules:
+            if rep_module.__class__.__name__ == "MultiheadAttention":      # multiple MHA modules are in list, prevent to backed up forward
+                if not hasattr(rep_module, "_lora_org_weights"):
+                    # avoid updating of original weights. state_dict is reference to original weights
+                    rep_module._lora_org_weights = copy.deepcopy(rep_module.state_dict())
+                    backed_up = True
+            elif not hasattr(rep_module, "_lora_org_forward"):
+                rep_module._lora_org_forward = rep_module.forward
+                backed_up = True
+        if backed_up:
+            print("original forward/weights is backed up.")
+
+        # 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 restore(self, text_encoder, unet):
+        # restore forward/weights from property for all modules
+        restored = False                        # messaging purpose only
+        modules = []
+        modules.extend(text_encoder.modules())
+        modules.extend(unet.modules())
+        for module in modules:
+            if hasattr(module, "_lora_org_forward"):
+                module.forward = module._lora_org_forward
+                del module._lora_org_forward
+                restored = True
+            if hasattr(module, "_lora_org_weights"):        # module doesn't have forward and weights at same time currently, but supports it for future changing
+                module.load_state_dict(module._lora_org_weights)
+                del module._lora_org_weights
+                restored = True
+
+        if restored:
+            print("original forward/weights is restored.")
+
+    def apply_lora_modules(self, du_state_dict):
+        # conversion 1st step: convert names in state_dict
+        state_dict = LoConNetworkCompvis.convert_state_dict_name_to_compvis(self.v2, du_state_dict)
+
+        # check state_dict has text_encoder or unet
+        weights_has_text_encoder = weights_has_unet = False
+        for key in state_dict.keys():
+            if key.startswith(LoConNetworkCompvis.LORA_PREFIX_TEXT_ENCODER):
+                weights_has_text_encoder = True
+            elif key.startswith(LoConNetworkCompvis.LORA_PREFIX_UNET):
+                weights_has_unet = True
+            if weights_has_text_encoder and weights_has_unet:
+                break
+
+        apply_text_encoder = weights_has_text_encoder
+        apply_unet = weights_has_unet
+
+        if apply_text_encoder:
+            print("enable LoCon for text encoder")
+        else:
+            self.text_encoder_loras = []
+
+        if apply_unet:
+            print("enable LoCon for U-Net")
+        else:
+            self.unet_loras = []
+
+        # add modules to network: this makes state_dict can be got from LoRANetwork
+        mha_loras = {}
+        for lora in self.text_encoder_loras + self.unet_loras:
+            if type(lora) == LoConModule:
+                lora.apply_to()                           # ensure remove reference to original Linear: reference makes key of state_dict
+                self.add_module(lora.lora_name, lora)
+            else:
+                # SD2.x MultiheadAttention merge weights to MHA weights
+                lora_info: LoRAInfo = lora
+                if lora_info.module_name not in mha_loras:
+                    mha_loras[lora_info.module_name] = {}
+
+                lora_dic = mha_loras[lora_info.module_name]
+                lora_dic[lora_info.lora_name] = lora_info
+                if len(lora_dic) == 4:
+                    # calculate and apply
+                    w_q_dw = state_dict.get(lora_info.module_name + '_q_proj.lora_down.weight')
+                    if w_q_dw is not None:                       # corresponding LoRa module exists
+                        w_q_up = state_dict[lora_info.module_name + '_q_proj.lora_up.weight']
+                        w_q_ap = state_dict.get(lora_info.module_name + '_q_proj.alpha', None)
+                        w_k_dw = state_dict[lora_info.module_name + '_k_proj.lora_down.weight']
+                        w_k_up = state_dict[lora_info.module_name + '_k_proj.lora_up.weight']
+                        w_k_ap = state_dict.get(lora_info.module_name + '_k_proj.alpha', None)
+                        w_v_dw = state_dict[lora_info.module_name + '_v_proj.lora_down.weight']
+                        w_v_up = state_dict[lora_info.module_name + '_v_proj.lora_up.weight']
+                        w_v_ap = state_dict.get(lora_info.module_name + '_v_proj.alpha', None)
+                        w_out_dw = state_dict[lora_info.module_name + '_out_proj.lora_down.weight']
+                        w_out_up = state_dict[lora_info.module_name + '_out_proj.lora_up.weight']
+                        w_out_ap = state_dict.get(lora_info.module_name + '_out_proj.alpha', None)
+
+                        sd = lora_info.module.state_dict()
+                        qkv_weight = sd['in_proj_weight']
+                        out_weight = sd['out_proj.weight']
+                        dev = qkv_weight.device
+
+                        def merge_weights(weight, up_weight, down_weight, alpha=None):
+                            # calculate in float
+                            if alpha is None:
+                                alpha = down_weight.shape[0]
+                            alpha = float(alpha)
+                            scale = alpha / down_weight.shape[0]
+                            dtype = weight.dtype
+                            weight = weight.float() + lora_info.multiplier * (up_weight.to(dev, dtype=torch.float) @ down_weight.to(dev, dtype=torch.float)) * scale
+                            weight = weight.to(dtype)
+                            return weight
+
+                        q_weight, k_weight, v_weight = torch.chunk(qkv_weight, 3)
+                        if q_weight.size()[1] == w_q_up.size()[0]:
+                            q_weight = merge_weights(q_weight, w_q_up, w_q_dw, w_q_ap)
+                            k_weight = merge_weights(k_weight, w_k_up, w_k_dw, w_k_ap)
+                            v_weight = merge_weights(v_weight, w_v_up, w_v_dw, w_v_ap)
+                            qkv_weight = torch.cat([q_weight, k_weight, v_weight])
+
+                            out_weight = merge_weights(out_weight, w_out_up, w_out_dw, w_out_ap)
+
+                            sd['in_proj_weight'] = qkv_weight.to(dev)
+                            sd['out_proj.weight'] = out_weight.to(dev)
+
+                            lora_info.module.load_state_dict(sd)
+                        else:
+                            # different dim, version mismatch
+                            print(f"shape of weight is different: {lora_info.module_name}. SD version may be different")
+
+                        for t in ["q", "k", "v", "out"]:
+                            del state_dict[f"{lora_info.module_name}_{t}_proj.lora_down.weight"]
+                            del state_dict[f"{lora_info.module_name}_{t}_proj.lora_up.weight"]
+                            alpha_key = f"{lora_info.module_name}_{t}_proj.alpha"
+                            if alpha_key in state_dict:
+                                del state_dict[alpha_key]
+                    else:
+                        # corresponding weight not exists: version mismatch
+                        pass
+
+        # conversion 2nd step: convert weight's shape (and handle wrapped)
+        state_dict = self.convert_state_dict_shape_to_compvis(state_dict)
+
+        return state_dict
+
+    def convert_state_dict_shape_to_compvis(self, state_dict):
+        # shape conversion
+        current_sd = self.state_dict()        # to get target shape
+        wrapped = False
+        count = 0
+        for key in list(state_dict.keys()):
+            if key not in current_sd:
+                continue                          # might be error or another version
+            if "wrapped" in key:
+                wrapped = True
+
+            value: torch.Tensor = state_dict[key]
+            if value.size() != current_sd[key].size():
+                # print(key, value.size(), current_sd[key].size())
+                # print(f"convert weights shape: {key}, from: {value.size()}, {len(value.size())}")
+                count += 1
+                if '.alpha' in key:
+                    assert value.size().numel() == 1
+                    value = torch.tensor(value.item())
+                elif len(value.size()) == 4:
+                    value = value.squeeze(3).squeeze(2)
+                else:
+                    value = value.unsqueeze(2).unsqueeze(3)
+                state_dict[key] = value
+            if tuple(value.size()) != tuple(current_sd[key].size()):
+                print(
+                        f"weight's shape is different: {key} expected {current_sd[key].size()} found {value.size()}. SD version may be different")
+                del state_dict[key]
+        print(f"shapes for {count} weights are converted.")
+
+        # convert wrapped
+        if not wrapped:
+            print("remove 'wrapped' from keys")
+            for key in list(state_dict.keys()):
+                if "_wrapped_" in key:
+                    new_key = key.replace("_wrapped_", "_")
+                    state_dict[new_key] = state_dict[key]
+                    del state_dict[key]
+
+        return state_dict