初始化换发型项目：3个微服务代码 + 部署脚本

包含：
- hair_service_sd: 换发型/换发色算法服务 (端口 8801)
- photo_service: LoRA 训练调度服务 (端口 32678)
- stable-diffusion-webui: SD WebUI 推理服务 (端口 57860)
- kohya_ss_home: 训练环境代码
- meidaojia: 监控测试脚本
- setup.sh: 一键部署脚本 (conda环境恢复 + 配置生成 + 完整性检查)
- start_all_services.sh: 启动3个服务
- configure.ini.template: 路径模板化 (BASE_DIR自动推导)
- conda_envs/py310.yml: py310 环境定义

大文件 (weights/, models/, data/, conda_envs/*.tar.gz 等) 通过 .gitignore 排除，
由网盘单独上传。

stable-diffusion-webui/extensions-builtin/Lora/extra_networks_lora.py

@@ -0,0 +1,62 @@
+from modules import extra_networks, shared
+import networks
+
+
+class ExtraNetworkLora(extra_networks.ExtraNetwork):
+    def __init__(self):
+        super().__init__('lora')
+
+        self.errors = {}
+        """mapping of network names to the number of errors the network had during operation"""
+
+    remove_symbols = str.maketrans('', '', ":,")
+
+    def activate(self, p, params_list):
+        additional = shared.opts.sd_lora
+
+        self.errors.clear()
+
+        if additional != "None" and additional in networks.available_networks and not any(x for x in params_list if x.items[0] == additional):
+            p.all_prompts = [x + f"<lora:{additional}:{shared.opts.extra_networks_default_multiplier}>" for x in p.all_prompts]
+            params_list.append(extra_networks.ExtraNetworkParams(items=[additional, shared.opts.extra_networks_default_multiplier]))
+
+        names = []
+        te_multipliers = []
+        unet_multipliers = []
+        dyn_dims = []
+        for params in params_list:
+            assert params.items
+
+            names.append(params.positional[0])
+
+            te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0
+            te_multiplier = float(params.named.get("te", te_multiplier))
+
+            unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier
+            unet_multiplier = float(params.named.get("unet", unet_multiplier))
+
+            dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None
+            dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim
+
+            te_multipliers.append(te_multiplier)
+            unet_multipliers.append(unet_multiplier)
+            dyn_dims.append(dyn_dim)
+
+        networks.load_networks(names, te_multipliers, unet_multipliers, dyn_dims)
+
+        if shared.opts.lora_add_hashes_to_infotext:
+            if not getattr(p, "is_hr_pass", False) or not hasattr(p, "lora_hashes"):
+                p.lora_hashes = {}
+
+            for item in networks.loaded_networks:
+                if item.network_on_disk.shorthash and item.mentioned_name:
+                    p.lora_hashes[item.mentioned_name.translate(self.remove_symbols)] = item.network_on_disk.shorthash
+
+            if p.lora_hashes:
+                p.extra_generation_params["Lora hashes"] = ', '.join(f'{k}: {v}' for k, v in p.lora_hashes.items())
+
+    def deactivate(self, p):
+        if self.errors:
+            p.comment("Networks with errors: " + ", ".join(f"{k} ({v})" for k, v in self.errors.items()))
+
+            self.errors.clear()

stable-diffusion-webui/extensions-builtin/Lora/lora.py

@@ -0,0 +1,9 @@
+import networks
+
+list_available_loras = networks.list_available_networks
+
+available_loras = networks.available_networks
+available_lora_aliases = networks.available_network_aliases
+available_lora_hash_lookup = networks.available_network_hash_lookup
+forbidden_lora_aliases = networks.forbidden_network_aliases
+loaded_loras = networks.loaded_networks

stable-diffusion-webui/extensions-builtin/Lora/lora_logger.py

@@ -0,0 +1,33 @@
+import sys
+import copy
+import logging
+
+
+class ColoredFormatter(logging.Formatter):
+    COLORS = {
+        "DEBUG": "\033[0;36m",  # CYAN
+        "INFO": "\033[0;32m",  # GREEN
+        "WARNING": "\033[0;33m",  # YELLOW
+        "ERROR": "\033[0;31m",  # RED
+        "CRITICAL": "\033[0;37;41m",  # WHITE ON RED
+        "RESET": "\033[0m",  # RESET COLOR
+    }
+
+    def format(self, record):
+        colored_record = copy.copy(record)
+        levelname = colored_record.levelname
+        seq = self.COLORS.get(levelname, self.COLORS["RESET"])
+        colored_record.levelname = f"{seq}{levelname}{self.COLORS['RESET']}"
+        return super().format(colored_record)
+
+
+logger = logging.getLogger("lora")
+logger.propagate = False
+
+
+if not logger.handlers:
+    handler = logging.StreamHandler(sys.stdout)
+    handler.setFormatter(
+        ColoredFormatter("[%(name)s]-%(levelname)s: %(message)s")
+    )
+    logger.addHandler(handler)

stable-diffusion-webui/extensions-builtin/Lora/lora_patches.py

@@ -0,0 +1,31 @@
+import torch
+
+import networks
+from modules import patches
+
+
+class LoraPatches:
+    def __init__(self):
+        self.Linear_forward = patches.patch(__name__, torch.nn.Linear, 'forward', networks.network_Linear_forward)
+        self.Linear_load_state_dict = patches.patch(__name__, torch.nn.Linear, '_load_from_state_dict', networks.network_Linear_load_state_dict)
+        self.Conv2d_forward = patches.patch(__name__, torch.nn.Conv2d, 'forward', networks.network_Conv2d_forward)
+        self.Conv2d_load_state_dict = patches.patch(__name__, torch.nn.Conv2d, '_load_from_state_dict', networks.network_Conv2d_load_state_dict)
+        self.GroupNorm_forward = patches.patch(__name__, torch.nn.GroupNorm, 'forward', networks.network_GroupNorm_forward)
+        self.GroupNorm_load_state_dict = patches.patch(__name__, torch.nn.GroupNorm, '_load_from_state_dict', networks.network_GroupNorm_load_state_dict)
+        self.LayerNorm_forward = patches.patch(__name__, torch.nn.LayerNorm, 'forward', networks.network_LayerNorm_forward)
+        self.LayerNorm_load_state_dict = patches.patch(__name__, torch.nn.LayerNorm, '_load_from_state_dict', networks.network_LayerNorm_load_state_dict)
+        self.MultiheadAttention_forward = patches.patch(__name__, torch.nn.MultiheadAttention, 'forward', networks.network_MultiheadAttention_forward)
+        self.MultiheadAttention_load_state_dict = patches.patch(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict', networks.network_MultiheadAttention_load_state_dict)
+
+    def undo(self):
+        self.Linear_forward = patches.undo(__name__, torch.nn.Linear, 'forward')
+        self.Linear_load_state_dict = patches.undo(__name__, torch.nn.Linear, '_load_from_state_dict')
+        self.Conv2d_forward = patches.undo(__name__, torch.nn.Conv2d, 'forward')
+        self.Conv2d_load_state_dict = patches.undo(__name__, torch.nn.Conv2d, '_load_from_state_dict')
+        self.GroupNorm_forward = patches.undo(__name__, torch.nn.GroupNorm, 'forward')
+        self.GroupNorm_load_state_dict = patches.undo(__name__, torch.nn.GroupNorm, '_load_from_state_dict')
+        self.LayerNorm_forward = patches.undo(__name__, torch.nn.LayerNorm, 'forward')
+        self.LayerNorm_load_state_dict = patches.undo(__name__, torch.nn.LayerNorm, '_load_from_state_dict')
+        self.MultiheadAttention_forward = patches.undo(__name__, torch.nn.MultiheadAttention, 'forward')
+        self.MultiheadAttention_load_state_dict = patches.undo(__name__, torch.nn.MultiheadAttention, '_load_from_state_dict')
+

stable-diffusion-webui/extensions-builtin/Lora/lyco_helpers.py

@@ -0,0 +1,68 @@
+import torch
+
+
+def make_weight_cp(t, wa, wb):
+    temp = torch.einsum('i j k l, j r -> i r k l', t, wb)
+    return torch.einsum('i j k l, i r -> r j k l', temp, wa)
+
+
+def rebuild_conventional(up, down, shape, dyn_dim=None):
+    up = up.reshape(up.size(0), -1)
+    down = down.reshape(down.size(0), -1)
+    if dyn_dim is not None:
+        up = up[:, :dyn_dim]
+        down = down[:dyn_dim, :]
+    return (up @ down).reshape(shape)
+
+
+def rebuild_cp_decomposition(up, down, mid):
+    up = up.reshape(up.size(0), -1)
+    down = down.reshape(down.size(0), -1)
+    return torch.einsum('n m k l, i n, m j -> i j k l', mid, up, down)
+
+
+# copied from https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/lokr.py
+def factorization(dimension: int, factor:int=-1) -> tuple[int, int]:
+    '''
+    return a tuple of two value of input dimension decomposed by the number closest to factor
+    second value is higher or equal than first value.
+
+    In LoRA with Kroneckor Product, first value is a value for weight scale.
+    secon value is a value for weight.
+
+    Because of non-commutative property, A⊗B ≠ B⊗A. Meaning of two matrices is slightly different.
+
+    examples)
+    factor
+        -1               2                4               8               16               ...
+    127 -> 1, 127   127 -> 1, 127    127 -> 1, 127   127 -> 1, 127   127 -> 1, 127
+    128 -> 8, 16    128 -> 2, 64     128 -> 4, 32    128 -> 8, 16    128 -> 8, 16
+    250 -> 10, 25   250 -> 2, 125    250 -> 2, 125   250 -> 5, 50    250 -> 10, 25
+    360 -> 8, 45    360 -> 2, 180    360 -> 4, 90    360 -> 8, 45    360 -> 12, 30
+    512 -> 16, 32   512 -> 2, 256    512 -> 4, 128   512 -> 8, 64    512 -> 16, 32
+    1024 -> 32, 32  1024 -> 2, 512   1024 -> 4, 256  1024 -> 8, 128  1024 -> 16, 64
+    '''
+
+    if factor > 0 and (dimension % factor) == 0:
+        m = factor
+        n = dimension // factor
+        if m > n:
+            n, m = m, n
+        return m, n
+    if factor < 0:
+        factor = dimension
+    m, n = 1, dimension
+    length = m + n
+    while m<n:
+        new_m = m + 1
+        while dimension%new_m != 0:
+            new_m += 1
+        new_n = dimension // new_m
+        if new_m + new_n > length or new_m>factor:
+            break
+        else:
+            m, n = new_m, new_n
+    if m > n:
+        n, m = m, n
+    return m, n
+

stable-diffusion-webui/extensions-builtin/Lora/network.py

@@ -0,0 +1,228 @@
+from __future__ import annotations
+import os
+from collections import namedtuple
+import enum
+
+import torch.nn as nn
+import torch.nn.functional as F
+
+from modules import sd_models, cache, errors, hashes, shared
+import modules.models.sd3.mmdit
+
+NetworkWeights = namedtuple('NetworkWeights', ['network_key', 'sd_key', 'w', 'sd_module'])
+
+metadata_tags_order = {"ss_sd_model_name": 1, "ss_resolution": 2, "ss_clip_skip": 3, "ss_num_train_images": 10, "ss_tag_frequency": 20}
+
+
+class SdVersion(enum.Enum):
+    Unknown = 1
+    SD1 = 2
+    SD2 = 3
+    SDXL = 4
+
+
+class NetworkOnDisk:
+    def __init__(self, name, filename):
+        self.name = name
+        self.filename = filename
+        self.metadata = {}
+        self.is_safetensors = os.path.splitext(filename)[1].lower() == ".safetensors"
+
+        def read_metadata():
+            metadata = sd_models.read_metadata_from_safetensors(filename)
+
+            return metadata
+
+        if self.is_safetensors:
+            try:
+                self.metadata = cache.cached_data_for_file('safetensors-metadata', "lora/" + self.name, filename, read_metadata)
+            except Exception as e:
+                errors.display(e, f"reading lora {filename}")
+
+        if self.metadata:
+            m = {}
+            for k, v in sorted(self.metadata.items(), key=lambda x: metadata_tags_order.get(x[0], 999)):
+                m[k] = v
+
+            self.metadata = m
+
+        self.alias = self.metadata.get('ss_output_name', self.name)
+
+        self.hash = None
+        self.shorthash = None
+        self.set_hash(
+            self.metadata.get('sshs_model_hash') or
+            hashes.sha256_from_cache(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or
+            ''
+        )
+
+        self.sd_version = self.detect_version()
+
+    def detect_version(self):
+        if str(self.metadata.get('ss_base_model_version', "")).startswith("sdxl_"):
+            return SdVersion.SDXL
+        elif str(self.metadata.get('ss_v2', "")) == "True":
+            return SdVersion.SD2
+        elif len(self.metadata):
+            return SdVersion.SD1
+
+        return SdVersion.Unknown
+
+    def set_hash(self, v):
+        self.hash = v
+        self.shorthash = self.hash[0:12]
+
+        if self.shorthash:
+            import networks
+            networks.available_network_hash_lookup[self.shorthash] = self
+
+    def read_hash(self):
+        if not self.hash:
+            self.set_hash(hashes.sha256(self.filename, "lora/" + self.name, use_addnet_hash=self.is_safetensors) or '')
+
+    def get_alias(self):
+        import networks
+        if shared.opts.lora_preferred_name == "Filename" or self.alias.lower() in networks.forbidden_network_aliases:
+            return self.name
+        else:
+            return self.alias
+
+
+class Network:  # LoraModule
+    def __init__(self, name, network_on_disk: NetworkOnDisk):
+        self.name = name
+        self.network_on_disk = network_on_disk
+        self.te_multiplier = 1.0
+        self.unet_multiplier = 1.0
+        self.dyn_dim = None
+        self.modules = {}
+        self.bundle_embeddings = {}
+        self.mtime = None
+
+        self.mentioned_name = None
+        """the text that was used to add the network to prompt - can be either name or an alias"""
+
+
+class ModuleType:
+    def create_module(self, net: Network, weights: NetworkWeights) -> Network | None:
+        return None
+
+
+class NetworkModule:
+    def __init__(self, net: Network, weights: NetworkWeights):
+        self.network = net
+        self.network_key = weights.network_key
+        self.sd_key = weights.sd_key
+        self.sd_module = weights.sd_module
+
+        if isinstance(self.sd_module, modules.models.sd3.mmdit.QkvLinear):
+            s = self.sd_module.weight.shape
+            self.shape = (s[0] // 3, s[1])
+        elif hasattr(self.sd_module, 'weight'):
+            self.shape = self.sd_module.weight.shape
+        elif isinstance(self.sd_module, nn.MultiheadAttention):
+            # For now, only self-attn use Pytorch's MHA
+            # So assume all qkvo proj have same shape
+            self.shape = self.sd_module.out_proj.weight.shape
+        else:
+            self.shape = None
+
+        self.ops = None
+        self.extra_kwargs = {}
+        if isinstance(self.sd_module, nn.Conv2d):
+            self.ops = F.conv2d
+            self.extra_kwargs = {
+                'stride': self.sd_module.stride,
+                'padding': self.sd_module.padding
+            }
+        elif isinstance(self.sd_module, nn.Linear):
+            self.ops = F.linear
+        elif isinstance(self.sd_module, nn.LayerNorm):
+            self.ops = F.layer_norm
+            self.extra_kwargs = {
+                'normalized_shape': self.sd_module.normalized_shape,
+                'eps': self.sd_module.eps
+            }
+        elif isinstance(self.sd_module, nn.GroupNorm):
+            self.ops = F.group_norm
+            self.extra_kwargs = {
+                'num_groups': self.sd_module.num_groups,
+                'eps': self.sd_module.eps
+            }
+
+        self.dim = None
+        self.bias = weights.w.get("bias")
+        self.alpha = weights.w["alpha"].item() if "alpha" in weights.w else None
+        self.scale = weights.w["scale"].item() if "scale" in weights.w else None
+
+        self.dora_scale = weights.w.get("dora_scale", None)
+        self.dora_norm_dims = len(self.shape) - 1
+
+    def multiplier(self):
+        if 'transformer' in self.sd_key[:20]:
+            return self.network.te_multiplier
+        else:
+            return self.network.unet_multiplier
+
+    def calc_scale(self):
+        if self.scale is not None:
+            return self.scale
+        if self.dim is not None and self.alpha is not None:
+            return self.alpha / self.dim
+
+        return 1.0
+
+    def apply_weight_decompose(self, updown, orig_weight):
+        # Match the device/dtype
+        orig_weight = orig_weight.to(updown.dtype)
+        dora_scale = self.dora_scale.to(device=orig_weight.device, dtype=updown.dtype)
+        updown = updown.to(orig_weight.device)
+
+        merged_scale1 = updown + orig_weight
+        merged_scale1_norm = (
+            merged_scale1.transpose(0, 1)
+            .reshape(merged_scale1.shape[1], -1)
+            .norm(dim=1, keepdim=True)
+            .reshape(merged_scale1.shape[1], *[1] * self.dora_norm_dims)
+            .transpose(0, 1)
+        )
+
+        dora_merged = (
+            merged_scale1 * (dora_scale / merged_scale1_norm)
+        )
+        final_updown = dora_merged - orig_weight
+        return final_updown
+
+    def finalize_updown(self, updown, orig_weight, output_shape, ex_bias=None):
+        if self.bias is not None:
+            updown = updown.reshape(self.bias.shape)
+            updown += self.bias.to(orig_weight.device, dtype=updown.dtype)
+            updown = updown.reshape(output_shape)
+
+        if len(output_shape) == 4:
+            updown = updown.reshape(output_shape)
+
+        if orig_weight.size().numel() == updown.size().numel():
+            updown = updown.reshape(orig_weight.shape)
+
+        if ex_bias is not None:
+            ex_bias = ex_bias * self.multiplier()
+
+        updown = updown * self.calc_scale()
+
+        if self.dora_scale is not None:
+            updown = self.apply_weight_decompose(updown, orig_weight)
+
+        return updown * self.multiplier(), ex_bias
+
+    def calc_updown(self, target):
+        raise NotImplementedError()
+
+    def forward(self, x, y):
+        """A general forward implementation for all modules"""
+        if self.ops is None:
+            raise NotImplementedError()
+        else:
+            updown, ex_bias = self.calc_updown(self.sd_module.weight)
+            return y + self.ops(x, weight=updown, bias=ex_bias, **self.extra_kwargs)
+

stable-diffusion-webui/extensions-builtin/Lora/network_full.py

@@ -0,0 +1,27 @@
+import network
+
+
+class ModuleTypeFull(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["diff"]):
+            return NetworkModuleFull(net, weights)
+
+        return None
+
+
+class NetworkModuleFull(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.weight = weights.w.get("diff")
+        self.ex_bias = weights.w.get("diff_b")
+
+    def calc_updown(self, orig_weight):
+        output_shape = self.weight.shape
+        updown = self.weight.to(orig_weight.device)
+        if self.ex_bias is not None:
+            ex_bias = self.ex_bias.to(orig_weight.device)
+        else:
+            ex_bias = None
+
+        return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)

stable-diffusion-webui/extensions-builtin/Lora/network_glora.py

@@ -0,0 +1,33 @@
+
+import network
+
+class ModuleTypeGLora(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["a1.weight", "a2.weight", "alpha", "b1.weight", "b2.weight"]):
+            return NetworkModuleGLora(net, weights)
+
+        return None
+
+# adapted from https://github.com/KohakuBlueleaf/LyCORIS
+class NetworkModuleGLora(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        if hasattr(self.sd_module, 'weight'):
+            self.shape = self.sd_module.weight.shape
+
+        self.w1a = weights.w["a1.weight"]
+        self.w1b = weights.w["b1.weight"]
+        self.w2a = weights.w["a2.weight"]
+        self.w2b = weights.w["b2.weight"]
+
+    def calc_updown(self, orig_weight):
+        w1a = self.w1a.to(orig_weight.device)
+        w1b = self.w1b.to(orig_weight.device)
+        w2a = self.w2a.to(orig_weight.device)
+        w2b = self.w2b.to(orig_weight.device)
+
+        output_shape = [w1a.size(0), w1b.size(1)]
+        updown = ((w2b @ w1b) + ((orig_weight.to(dtype = w1a.dtype) @ w2a) @ w1a))
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

stable-diffusion-webui/extensions-builtin/Lora/network_hada.py

@@ -0,0 +1,55 @@
+import lyco_helpers
+import network
+
+
+class ModuleTypeHada(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["hada_w1_a", "hada_w1_b", "hada_w2_a", "hada_w2_b"]):
+            return NetworkModuleHada(net, weights)
+
+        return None
+
+
+class NetworkModuleHada(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        if hasattr(self.sd_module, 'weight'):
+            self.shape = self.sd_module.weight.shape
+
+        self.w1a = weights.w["hada_w1_a"]
+        self.w1b = weights.w["hada_w1_b"]
+        self.dim = self.w1b.shape[0]
+        self.w2a = weights.w["hada_w2_a"]
+        self.w2b = weights.w["hada_w2_b"]
+
+        self.t1 = weights.w.get("hada_t1")
+        self.t2 = weights.w.get("hada_t2")
+
+    def calc_updown(self, orig_weight):
+        w1a = self.w1a.to(orig_weight.device)
+        w1b = self.w1b.to(orig_weight.device)
+        w2a = self.w2a.to(orig_weight.device)
+        w2b = self.w2b.to(orig_weight.device)
+
+        output_shape = [w1a.size(0), w1b.size(1)]
+
+        if self.t1 is not None:
+            output_shape = [w1a.size(1), w1b.size(1)]
+            t1 = self.t1.to(orig_weight.device)
+            updown1 = lyco_helpers.make_weight_cp(t1, w1a, w1b)
+            output_shape += t1.shape[2:]
+        else:
+            if len(w1b.shape) == 4:
+                output_shape += w1b.shape[2:]
+            updown1 = lyco_helpers.rebuild_conventional(w1a, w1b, output_shape)
+
+        if self.t2 is not None:
+            t2 = self.t2.to(orig_weight.device)
+            updown2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
+        else:
+            updown2 = lyco_helpers.rebuild_conventional(w2a, w2b, output_shape)
+
+        updown = updown1 * updown2
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

stable-diffusion-webui/extensions-builtin/Lora/network_ia3.py

@@ -0,0 +1,30 @@
+import network
+
+
+class ModuleTypeIa3(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["weight"]):
+            return NetworkModuleIa3(net, weights)
+
+        return None
+
+
+class NetworkModuleIa3(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.w = weights.w["weight"]
+        self.on_input = weights.w["on_input"].item()
+
+    def calc_updown(self, orig_weight):
+        w = self.w.to(orig_weight.device)
+
+        output_shape = [w.size(0), orig_weight.size(1)]
+        if self.on_input:
+            output_shape.reverse()
+        else:
+            w = w.reshape(-1, 1)
+
+        updown = orig_weight * w
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

stable-diffusion-webui/extensions-builtin/Lora/network_lokr.py

@@ -0,0 +1,64 @@
+import torch
+
+import lyco_helpers
+import network
+
+
+class ModuleTypeLokr(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        has_1 = "lokr_w1" in weights.w or ("lokr_w1_a" in weights.w and "lokr_w1_b" in weights.w)
+        has_2 = "lokr_w2" in weights.w or ("lokr_w2_a" in weights.w and "lokr_w2_b" in weights.w)
+        if has_1 and has_2:
+            return NetworkModuleLokr(net, weights)
+
+        return None
+
+
+def make_kron(orig_shape, w1, w2):
+    if len(w2.shape) == 4:
+        w1 = w1.unsqueeze(2).unsqueeze(2)
+    w2 = w2.contiguous()
+    return torch.kron(w1, w2).reshape(orig_shape)
+
+
+class NetworkModuleLokr(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.w1 = weights.w.get("lokr_w1")
+        self.w1a = weights.w.get("lokr_w1_a")
+        self.w1b = weights.w.get("lokr_w1_b")
+        self.dim = self.w1b.shape[0] if self.w1b is not None else self.dim
+        self.w2 = weights.w.get("lokr_w2")
+        self.w2a = weights.w.get("lokr_w2_a")
+        self.w2b = weights.w.get("lokr_w2_b")
+        self.dim = self.w2b.shape[0] if self.w2b is not None else self.dim
+        self.t2 = weights.w.get("lokr_t2")
+
+    def calc_updown(self, orig_weight):
+        if self.w1 is not None:
+            w1 = self.w1.to(orig_weight.device)
+        else:
+            w1a = self.w1a.to(orig_weight.device)
+            w1b = self.w1b.to(orig_weight.device)
+            w1 = w1a @ w1b
+
+        if self.w2 is not None:
+            w2 = self.w2.to(orig_weight.device)
+        elif self.t2 is None:
+            w2a = self.w2a.to(orig_weight.device)
+            w2b = self.w2b.to(orig_weight.device)
+            w2 = w2a @ w2b
+        else:
+            t2 = self.t2.to(orig_weight.device)
+            w2a = self.w2a.to(orig_weight.device)
+            w2b = self.w2b.to(orig_weight.device)
+            w2 = lyco_helpers.make_weight_cp(t2, w2a, w2b)
+
+        output_shape = [w1.size(0) * w2.size(0), w1.size(1) * w2.size(1)]
+        if len(orig_weight.shape) == 4:
+            output_shape = orig_weight.shape
+
+        updown = make_kron(output_shape, w1, w2)
+
+        return self.finalize_updown(updown, orig_weight, output_shape)

stable-diffusion-webui/extensions-builtin/Lora/network_lora.py

@@ -0,0 +1,94 @@
+import torch
+
+import lyco_helpers
+import modules.models.sd3.mmdit
+import network
+from modules import devices
+
+
+class ModuleTypeLora(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["lora_up.weight", "lora_down.weight"]):
+            return NetworkModuleLora(net, weights)
+
+        if all(x in weights.w for x in ["lora_A.weight", "lora_B.weight"]):
+            w = weights.w.copy()
+            weights.w.clear()
+            weights.w.update({"lora_up.weight": w["lora_B.weight"], "lora_down.weight": w["lora_A.weight"]})
+
+            return NetworkModuleLora(net, weights)
+
+        return None
+
+
+class NetworkModuleLora(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.up_model = self.create_module(weights.w, "lora_up.weight")
+        self.down_model = self.create_module(weights.w, "lora_down.weight")
+        self.mid_model = self.create_module(weights.w, "lora_mid.weight", none_ok=True)
+
+        self.dim = weights.w["lora_down.weight"].shape[0]
+
+    def create_module(self, weights, key, none_ok=False):
+        weight = weights.get(key)
+
+        if weight is None and none_ok:
+            return None
+
+        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear, torch.nn.MultiheadAttention, modules.models.sd3.mmdit.QkvLinear]
+        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
+
+        if is_linear:
+            weight = weight.reshape(weight.shape[0], -1)
+            module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
+        elif is_conv and key == "lora_down.weight" or key == "dyn_up":
+            if len(weight.shape) == 2:
+                weight = weight.reshape(weight.shape[0], -1, 1, 1)
+
+            if weight.shape[2] != 1 or weight.shape[3] != 1:
+                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
+            else:
+                module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+        elif is_conv and key == "lora_mid.weight":
+            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
+        elif is_conv and key == "lora_up.weight" or key == "dyn_down":
+            module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
+        else:
+            raise AssertionError(f'Lora layer {self.network_key} matched a layer with unsupported type: {type(self.sd_module).__name__}')
+
+        with torch.no_grad():
+            if weight.shape != module.weight.shape:
+                weight = weight.reshape(module.weight.shape)
+            module.weight.copy_(weight)
+
+        module.to(device=devices.cpu, dtype=devices.dtype)
+        module.weight.requires_grad_(False)
+
+        return module
+
+    def calc_updown(self, orig_weight):
+        up = self.up_model.weight.to(orig_weight.device)
+        down = self.down_model.weight.to(orig_weight.device)
+
+        output_shape = [up.size(0), down.size(1)]
+        if self.mid_model is not None:
+            # cp-decomposition
+            mid = self.mid_model.weight.to(orig_weight.device)
+            updown = lyco_helpers.rebuild_cp_decomposition(up, down, mid)
+            output_shape += mid.shape[2:]
+        else:
+            if len(down.shape) == 4:
+                output_shape += down.shape[2:]
+            updown = lyco_helpers.rebuild_conventional(up, down, output_shape, self.network.dyn_dim)
+
+        return self.finalize_updown(updown, orig_weight, output_shape)
+
+    def forward(self, x, y):
+        self.up_model.to(device=devices.device)
+        self.down_model.to(device=devices.device)
+
+        return y + self.up_model(self.down_model(x)) * self.multiplier() * self.calc_scale()
+
+

stable-diffusion-webui/extensions-builtin/Lora/network_norm.py

@@ -0,0 +1,28 @@
+import network
+
+
+class ModuleTypeNorm(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["w_norm", "b_norm"]):
+            return NetworkModuleNorm(net, weights)
+
+        return None
+
+
+class NetworkModuleNorm(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+        super().__init__(net, weights)
+
+        self.w_norm = weights.w.get("w_norm")
+        self.b_norm = weights.w.get("b_norm")
+
+    def calc_updown(self, orig_weight):
+        output_shape = self.w_norm.shape
+        updown = self.w_norm.to(orig_weight.device)
+
+        if self.b_norm is not None:
+            ex_bias = self.b_norm.to(orig_weight.device)
+        else:
+            ex_bias = None
+
+        return self.finalize_updown(updown, orig_weight, output_shape, ex_bias)

stable-diffusion-webui/extensions-builtin/Lora/network_oft.py

@@ -0,0 +1,118 @@
+import torch
+import network
+from einops import rearrange
+
+
+class ModuleTypeOFT(network.ModuleType):
+    def create_module(self, net: network.Network, weights: network.NetworkWeights):
+        if all(x in weights.w for x in ["oft_blocks"]) or all(x in weights.w for x in ["oft_diag"]):
+            return NetworkModuleOFT(net, weights)
+
+        return None
+
+# Supports both kohya-ss' implementation of COFT  https://github.com/kohya-ss/sd-scripts/blob/main/networks/oft.py
+# and KohakuBlueleaf's implementation of OFT/COFT https://github.com/KohakuBlueleaf/LyCORIS/blob/dev/lycoris/modules/diag_oft.py
+class NetworkModuleOFT(network.NetworkModule):
+    def __init__(self,  net: network.Network, weights: network.NetworkWeights):
+
+        super().__init__(net, weights)
+
+        self.lin_module = None
+        self.org_module: list[torch.Module] = [self.sd_module]
+
+        self.scale = 1.0
+        self.is_R = False
+        self.is_boft = False
+
+        # kohya-ss/New LyCORIS OFT/BOFT
+        if "oft_blocks" in weights.w.keys():
+            self.oft_blocks = weights.w["oft_blocks"] # (num_blocks, block_size, block_size)
+            self.alpha = weights.w.get("alpha", None) # alpha is constraint
+            self.dim = self.oft_blocks.shape[0] # lora dim
+        # Old LyCORIS OFT
+        elif "oft_diag" in weights.w.keys():
+            self.is_R = True
+            self.oft_blocks = weights.w["oft_diag"]
+            # self.alpha is unused
+            self.dim = self.oft_blocks.shape[1] # (num_blocks, block_size, block_size)
+
+        is_linear = type(self.sd_module) in [torch.nn.Linear, torch.nn.modules.linear.NonDynamicallyQuantizableLinear]
+        is_conv = type(self.sd_module) in [torch.nn.Conv2d]
+        is_other_linear = type(self.sd_module) in [torch.nn.MultiheadAttention] # unsupported
+
+        if is_linear:
+            self.out_dim = self.sd_module.out_features
+        elif is_conv:
+            self.out_dim = self.sd_module.out_channels
+        elif is_other_linear:
+            self.out_dim = self.sd_module.embed_dim
+
+        # LyCORIS BOFT
+        if self.oft_blocks.dim() == 4:
+            self.is_boft = True
+        self.rescale = weights.w.get('rescale', None)
+        if self.rescale is not None and not is_other_linear:
+            self.rescale = self.rescale.reshape(-1, *[1]*(self.org_module[0].weight.dim() - 1))
+
+        self.num_blocks = self.dim
+        self.block_size = self.out_dim // self.dim
+        self.constraint = (0 if self.alpha is None else self.alpha) * self.out_dim
+        if self.is_R:
+            self.constraint = None
+            self.block_size = self.dim
+            self.num_blocks = self.out_dim // self.dim
+        elif self.is_boft:
+            self.boft_m = self.oft_blocks.shape[0]
+            self.num_blocks = self.oft_blocks.shape[1]
+            self.block_size = self.oft_blocks.shape[2]
+            self.boft_b = self.block_size
+
+    def calc_updown(self, orig_weight):
+        oft_blocks = self.oft_blocks.to(orig_weight.device)
+        eye = torch.eye(self.block_size, device=oft_blocks.device)
+
+        if not self.is_R:
+            block_Q = oft_blocks - oft_blocks.transpose(-1, -2) # ensure skew-symmetric orthogonal matrix
+            if self.constraint != 0:
+                norm_Q = torch.norm(block_Q.flatten())
+                new_norm_Q = torch.clamp(norm_Q, max=self.constraint.to(oft_blocks.device))
+                block_Q = block_Q * ((new_norm_Q + 1e-8) / (norm_Q + 1e-8))
+            oft_blocks = torch.matmul(eye + block_Q, (eye - block_Q).float().inverse())
+
+        R = oft_blocks.to(orig_weight.device)
+
+        if not self.is_boft:
+            # This errors out for MultiheadAttention, might need to be handled up-stream
+            merged_weight = rearrange(orig_weight, '(k n) ... -> k n ...', k=self.num_blocks, n=self.block_size)
+            merged_weight = torch.einsum(
+                'k n m, k n ... -> k m ...',
+                R,
+                merged_weight
+            )
+            merged_weight = rearrange(merged_weight, 'k m ... -> (k m) ...')
+        else:
+            # TODO: determine correct value for scale
+            scale = 1.0
+            m = self.boft_m
+            b = self.boft_b
+            r_b = b // 2
+            inp = orig_weight
+            for i in range(m):
+                bi = R[i] # b_num, b_size, b_size
+                if i == 0:
+                    # Apply multiplier/scale and rescale into first weight
+                    bi = bi * scale + (1 - scale) * eye
+                inp = rearrange(inp, "(c g k) ... -> (c k g) ...", g=2, k=2**i * r_b)
+                inp = rearrange(inp, "(d b) ... -> d b ...", b=b)
+                inp = torch.einsum("b i j, b j ... -> b i ...", bi, inp)
+                inp = rearrange(inp, "d b ... -> (d b) ...")
+                inp = rearrange(inp, "(c k g) ... -> (c g k) ...", g=2, k=2**i * r_b)
+            merged_weight = inp
+
+        # Rescale mechanism
+        if self.rescale is not None:
+            merged_weight = self.rescale.to(merged_weight) * merged_weight
+
+        updown = merged_weight.to(orig_weight.device) - orig_weight.to(merged_weight.dtype)
+        output_shape = orig_weight.shape
+        return self.finalize_updown(updown, orig_weight, output_shape)

stable-diffusion-webui/extensions-builtin/Lora/networks.py

@@ -0,0 +1,737 @@
+from __future__ import annotations
+import gradio as gr
+import logging
+import os
+import re
+
+import lora_patches
+import network
+import network_lora
+import network_glora
+import network_hada
+import network_ia3
+import network_lokr
+import network_full
+import network_norm
+import network_oft
+
+import torch
+from typing import Union
+
+from modules import shared, devices, sd_models, errors, scripts, sd_hijack
+import modules.textual_inversion.textual_inversion as textual_inversion
+import modules.models.sd3.mmdit
+
+from lora_logger import logger
+
+module_types = [
+    network_lora.ModuleTypeLora(),
+    network_hada.ModuleTypeHada(),
+    network_ia3.ModuleTypeIa3(),
+    network_lokr.ModuleTypeLokr(),
+    network_full.ModuleTypeFull(),
+    network_norm.ModuleTypeNorm(),
+    network_glora.ModuleTypeGLora(),
+    network_oft.ModuleTypeOFT(),
+]
+
+
+re_digits = re.compile(r"\d+")
+re_x_proj = re.compile(r"(.*)_([qkv]_proj)$")
+re_compiled = {}
+
+suffix_conversion = {
+    "attentions": {},
+    "resnets": {
+        "conv1": "in_layers_2",
+        "conv2": "out_layers_3",
+        "norm1": "in_layers_0",
+        "norm2": "out_layers_0",
+        "time_emb_proj": "emb_layers_1",
+        "conv_shortcut": "skip_connection",
+    }
+}
+
+
+def convert_diffusers_name_to_compvis(key, is_sd2):
+    def match(match_list, regex_text):
+        regex = re_compiled.get(regex_text)
+        if regex is None:
+            regex = re.compile(regex_text)
+            re_compiled[regex_text] = regex
+
+        r = re.match(regex, key)
+        if not r:
+            return False
+
+        match_list.clear()
+        match_list.extend([int(x) if re.match(re_digits, x) else x for x in r.groups()])
+        return True
+
+    m = []
+
+    if match(m, r"lora_unet_conv_in(.*)"):
+        return f'diffusion_model_input_blocks_0_0{m[0]}'
+
+    if match(m, r"lora_unet_conv_out(.*)"):
+        return f'diffusion_model_out_2{m[0]}'
+
+    if match(m, r"lora_unet_time_embedding_linear_(\d+)(.*)"):
+        return f"diffusion_model_time_embed_{m[0] * 2 - 2}{m[1]}"
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_input_blocks_{1 + m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_mid_block_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[0], {}).get(m[2], m[2])
+        return f"diffusion_model_middle_block_{1 if m[0] == 'attentions' else m[1] * 2}_{suffix}"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_(attentions|resnets)_(\d+)_(.+)"):
+        suffix = suffix_conversion.get(m[1], {}).get(m[3], m[3])
+        return f"diffusion_model_output_blocks_{m[0] * 3 + m[2]}_{1 if m[1] == 'attentions' else 0}_{suffix}"
+
+    if match(m, r"lora_unet_down_blocks_(\d+)_downsamplers_0_conv"):
+        return f"diffusion_model_input_blocks_{3 + m[0] * 3}_0_op"
+
+    if match(m, r"lora_unet_up_blocks_(\d+)_upsamplers_0_conv"):
+        return f"diffusion_model_output_blocks_{2 + m[0] * 3}_{2 if m[0]>0 else 1}_conv"
+
+    if match(m, r"lora_te_text_model_encoder_layers_(\d+)_(.+)"):
+        if is_sd2:
+            if 'mlp_fc1' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+            elif 'mlp_fc2' in m[1]:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+            else:
+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
+
+        return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"
+
+    if match(m, r"lora_te2_text_model_encoder_layers_(\d+)_(.+)"):
+        if 'mlp_fc1' in m[1]:
+            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"
+        elif 'mlp_fc2' in m[1]:
+            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"
+        else:
+            return f"1_model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"
+
+    return key
+
+
+def assign_network_names_to_compvis_modules(sd_model):
+    network_layer_mapping = {}
+
+    if shared.sd_model.is_sdxl:
+        for i, embedder in enumerate(shared.sd_model.conditioner.embedders):
+            if not hasattr(embedder, 'wrapped'):
+                continue
+
+            for name, module in embedder.wrapped.named_modules():
+                network_name = f'{i}_{name.replace(".", "_")}'
+                network_layer_mapping[network_name] = module
+                module.network_layer_name = network_name
+    else:
+        cond_stage_model = getattr(shared.sd_model.cond_stage_model, 'wrapped', shared.sd_model.cond_stage_model)
+
+        for name, module in cond_stage_model.named_modules():
+            network_name = name.replace(".", "_")
+            network_layer_mapping[network_name] = module
+            module.network_layer_name = network_name
+
+    for name, module in shared.sd_model.model.named_modules():
+        network_name = name.replace(".", "_")
+        network_layer_mapping[network_name] = module
+        module.network_layer_name = network_name
+
+    sd_model.network_layer_mapping = network_layer_mapping
+
+
+class BundledTIHash(str):
+    def __init__(self, hash_str):
+        self.hash = hash_str
+
+    def __str__(self):
+        return self.hash if shared.opts.lora_bundled_ti_to_infotext else ''
+
+
+def load_network(name, network_on_disk):
+    net = network.Network(name, network_on_disk)
+    net.mtime = os.path.getmtime(network_on_disk.filename)
+
+    sd = sd_models.read_state_dict(network_on_disk.filename)
+
+    # this should not be needed but is here as an emergency fix for an unknown error people are experiencing in 1.2.0
+    if not hasattr(shared.sd_model, 'network_layer_mapping'):
+        assign_network_names_to_compvis_modules(shared.sd_model)
+
+    keys_failed_to_match = {}
+    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.network_layer_mapping
+    if hasattr(shared.sd_model, 'diffusers_weight_map'):
+        diffusers_weight_map = shared.sd_model.diffusers_weight_map
+    elif hasattr(shared.sd_model, 'diffusers_weight_mapping'):
+        diffusers_weight_map = {}
+        for k, v in shared.sd_model.diffusers_weight_mapping():
+            diffusers_weight_map[k] = v
+        shared.sd_model.diffusers_weight_map = diffusers_weight_map
+    else:
+        diffusers_weight_map = None
+
+    matched_networks = {}
+    bundle_embeddings = {}
+
+    for key_network, weight in sd.items():
+
+        if diffusers_weight_map:
+            key_network_without_network_parts, network_name, network_weight = key_network.rsplit(".", 2)
+            network_part = network_name + '.' + network_weight
+        else:
+            key_network_without_network_parts, _, network_part = key_network.partition(".")
+
+        if key_network_without_network_parts == "bundle_emb":
+            emb_name, vec_name = network_part.split(".", 1)
+            emb_dict = bundle_embeddings.get(emb_name, {})
+            if vec_name.split('.')[0] == 'string_to_param':
+                _, k2 = vec_name.split('.', 1)
+                emb_dict['string_to_param'] = {k2: weight}
+            else:
+                emb_dict[vec_name] = weight
+            bundle_embeddings[emb_name] = emb_dict
+
+        if diffusers_weight_map:
+            key = diffusers_weight_map.get(key_network_without_network_parts, key_network_without_network_parts)
+        else:
+            key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
+
+        sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+        if sd_module is None:
+            m = re_x_proj.match(key)
+            if m:
+                sd_module = shared.sd_model.network_layer_mapping.get(m.group(1), None)
+
+        # SDXL loras seem to already have correct compvis keys, so only need to replace "lora_unet" with "diffusion_model"
+        if sd_module is None and "lora_unet" in key_network_without_network_parts:
+            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
+            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+        elif sd_module is None and "lora_te1_text_model" in key_network_without_network_parts:
+            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
+            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+            # some SD1 Loras also have correct compvis keys
+            if sd_module is None:
+                key = key_network_without_network_parts.replace("lora_te1_text_model", "transformer_text_model")
+                sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+        # kohya_ss OFT module
+        elif sd_module is None and "oft_unet" in key_network_without_network_parts:
+            key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
+            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+        # KohakuBlueLeaf OFT module
+        if sd_module is None and "oft_diag" in key:
+            key = key_network_without_network_parts.replace("lora_unet", "diffusion_model")
+            key = key_network_without_network_parts.replace("lora_te1_text_model", "0_transformer_text_model")
+            sd_module = shared.sd_model.network_layer_mapping.get(key, None)
+
+        if sd_module is None:
+            keys_failed_to_match[key_network] = key
+            continue
+
+        if key not in matched_networks:
+            matched_networks[key] = network.NetworkWeights(network_key=key_network, sd_key=key, w={}, sd_module=sd_module)
+
+        matched_networks[key].w[network_part] = weight
+
+    for key, weights in matched_networks.items():
+        net_module = None
+        for nettype in module_types:
+            net_module = nettype.create_module(net, weights)
+            if net_module is not None:
+                break
+
+        if net_module is None:
+            raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}")
+
+        net.modules[key] = net_module
+
+    embeddings = {}
+    for emb_name, data in bundle_embeddings.items():
+        embedding = textual_inversion.create_embedding_from_data(data, emb_name, filename=network_on_disk.filename + "/" + emb_name)
+        embedding.loaded = None
+        embedding.shorthash = BundledTIHash(name)
+        embeddings[emb_name] = embedding
+
+    net.bundle_embeddings = embeddings
+
+    if keys_failed_to_match:
+        logging.debug(f"Network {network_on_disk.filename} didn't match keys: {keys_failed_to_match}")
+
+    return net
+
+
+def purge_networks_from_memory():
+    while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0:
+        name = next(iter(networks_in_memory))
+        networks_in_memory.pop(name, None)
+
+    devices.torch_gc()
+
+
+def load_networks(names, te_multipliers=None, unet_multipliers=None, dyn_dims=None):
+    emb_db = sd_hijack.model_hijack.embedding_db
+    already_loaded = {}
+
+    for net in loaded_networks:
+        if net.name in names:
+            already_loaded[net.name] = net
+        for emb_name, embedding in net.bundle_embeddings.items():
+            if embedding.loaded:
+                emb_db.register_embedding_by_name(None, shared.sd_model, emb_name)
+
+    loaded_networks.clear()
+
+    unavailable_networks = []
+    for name in names:
+        if name.lower() in forbidden_network_aliases and available_networks.get(name) is None:
+            unavailable_networks.append(name)
+        elif available_network_aliases.get(name) is None:
+            unavailable_networks.append(name)
+
+    if unavailable_networks:
+        update_available_networks_by_names(unavailable_networks)
+
+    networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
+    if any(x is None for x in networks_on_disk):
+        list_available_networks()
+
+        networks_on_disk = [available_networks.get(name, None) if name.lower() in forbidden_network_aliases else available_network_aliases.get(name, None) for name in names]
+
+    failed_to_load_networks = []
+
+    for i, (network_on_disk, name) in enumerate(zip(networks_on_disk, names)):
+        net = already_loaded.get(name, None)
+
+        if network_on_disk is not None:
+            if net is None:
+                net = networks_in_memory.get(name)
+
+            if net is None or os.path.getmtime(network_on_disk.filename) > net.mtime:
+                try:
+                    net = load_network(name, network_on_disk)
+
+                    networks_in_memory.pop(name, None)
+                    networks_in_memory[name] = net
+                except Exception as e:
+                    errors.display(e, f"loading network {network_on_disk.filename}")
+                    continue
+
+            net.mentioned_name = name
+
+            network_on_disk.read_hash()
+
+        if net is None:
+            failed_to_load_networks.append(name)
+            logging.info(f"Couldn't find network with name {name}")
+            continue
+
+        net.te_multiplier = te_multipliers[i] if te_multipliers else 1.0
+        net.unet_multiplier = unet_multipliers[i] if unet_multipliers else 1.0
+        net.dyn_dim = dyn_dims[i] if dyn_dims else 1.0
+        loaded_networks.append(net)
+
+        for emb_name, embedding in net.bundle_embeddings.items():
+            if embedding.loaded is None and emb_name in emb_db.word_embeddings:
+                logger.warning(
+                    f'Skip bundle embedding: "{emb_name}"'
+                    ' as it was already loaded from embeddings folder'
+                )
+                continue
+
+            embedding.loaded = False
+            if emb_db.expected_shape == -1 or emb_db.expected_shape == embedding.shape:
+                embedding.loaded = True
+                emb_db.register_embedding(embedding, shared.sd_model)
+            else:
+                emb_db.skipped_embeddings[name] = embedding
+
+    if failed_to_load_networks:
+        lora_not_found_message = f'Lora not found: {", ".join(failed_to_load_networks)}'
+        sd_hijack.model_hijack.comments.append(lora_not_found_message)
+        if shared.opts.lora_not_found_warning_console:
+            print(f'\n{lora_not_found_message}\n')
+        if shared.opts.lora_not_found_gradio_warning:
+            gr.Warning(lora_not_found_message)
+
+    purge_networks_from_memory()
+
+
+def allowed_layer_without_weight(layer):
+    if isinstance(layer, torch.nn.LayerNorm) and not layer.elementwise_affine:
+        return True
+
+    return False
+
+
+def store_weights_backup(weight):
+    if weight is None:
+        return None
+
+    return weight.to(devices.cpu, copy=True)
+
+
+def restore_weights_backup(obj, field, weight):
+    if weight is None:
+        setattr(obj, field, None)
+        return
+
+    getattr(obj, field).copy_(weight)
+
+
+def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
+    weights_backup = getattr(self, "network_weights_backup", None)
+    bias_backup = getattr(self, "network_bias_backup", None)
+
+    if weights_backup is None and bias_backup is None:
+        return
+
+    if weights_backup is not None:
+        if isinstance(self, torch.nn.MultiheadAttention):
+            restore_weights_backup(self, 'in_proj_weight', weights_backup[0])
+            restore_weights_backup(self.out_proj, 'weight', weights_backup[1])
+        else:
+            restore_weights_backup(self, 'weight', weights_backup)
+
+    if isinstance(self, torch.nn.MultiheadAttention):
+        restore_weights_backup(self.out_proj, 'bias', bias_backup)
+    else:
+        restore_weights_backup(self, 'bias', bias_backup)
+
+
+def network_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]):
+    """
+    Applies the currently selected set of networks to the weights of torch layer self.
+    If weights already have this particular set of networks applied, does nothing.
+    If not, restores original weights from backup and alters weights according to networks.
+    """
+
+    network_layer_name = getattr(self, 'network_layer_name', None)
+    if network_layer_name is None:
+        return
+
+    current_names = getattr(self, "network_current_names", ())
+    wanted_names = tuple((x.name, x.te_multiplier, x.unet_multiplier, x.dyn_dim) for x in loaded_networks)
+
+    weights_backup = getattr(self, "network_weights_backup", None)
+    if weights_backup is None and wanted_names != ():
+        if current_names != () and not allowed_layer_without_weight(self):
+            raise RuntimeError(f"{network_layer_name} - no backup weights found and current weights are not unchanged")
+
+        if isinstance(self, torch.nn.MultiheadAttention):
+            weights_backup = (store_weights_backup(self.in_proj_weight), store_weights_backup(self.out_proj.weight))
+        else:
+            weights_backup = store_weights_backup(self.weight)
+
+        self.network_weights_backup = weights_backup
+
+    bias_backup = getattr(self, "network_bias_backup", None)
+    if bias_backup is None and wanted_names != ():
+        if isinstance(self, torch.nn.MultiheadAttention) and self.out_proj.bias is not None:
+            bias_backup = store_weights_backup(self.out_proj.bias)
+        elif getattr(self, 'bias', None) is not None:
+            bias_backup = store_weights_backup(self.bias)
+        else:
+            bias_backup = None
+
+        # Unlike weight which always has value, some modules don't have bias.
+        # Only report if bias is not None and current bias are not unchanged.
+        if bias_backup is not None and current_names != ():
+            raise RuntimeError("no backup bias found and current bias are not unchanged")
+
+        self.network_bias_backup = bias_backup
+
+    if current_names != wanted_names:
+        network_restore_weights_from_backup(self)
+
+        for net in loaded_networks:
+            module = net.modules.get(network_layer_name, None)
+            if module is not None and hasattr(self, 'weight') and not isinstance(module, modules.models.sd3.mmdit.QkvLinear):
+                try:
+                    with torch.no_grad():
+                        if getattr(self, 'fp16_weight', None) is None:
+                            weight = self.weight
+                            bias = self.bias
+                        else:
+                            weight = self.fp16_weight.clone().to(self.weight.device)
+                            bias = getattr(self, 'fp16_bias', None)
+                            if bias is not None:
+                                bias = bias.clone().to(self.bias.device)
+                        updown, ex_bias = module.calc_updown(weight)
+
+                        if len(weight.shape) == 4 and weight.shape[1] == 9:
+                            # inpainting model. zero pad updown to make channel[1]  4 to 9
+                            updown = torch.nn.functional.pad(updown, (0, 0, 0, 0, 0, 5))
+
+                        self.weight.copy_((weight.to(dtype=updown.dtype) + updown).to(dtype=self.weight.dtype))
+                        if ex_bias is not None and hasattr(self, 'bias'):
+                            if self.bias is None:
+                                self.bias = torch.nn.Parameter(ex_bias).to(self.weight.dtype)
+                            else:
+                                self.bias.copy_((bias + ex_bias).to(dtype=self.bias.dtype))
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue
+
+            module_q = net.modules.get(network_layer_name + "_q_proj", None)
+            module_k = net.modules.get(network_layer_name + "_k_proj", None)
+            module_v = net.modules.get(network_layer_name + "_v_proj", None)
+            module_out = net.modules.get(network_layer_name + "_out_proj", None)
+
+            if isinstance(self, torch.nn.MultiheadAttention) and module_q and module_k and module_v and module_out:
+                try:
+                    with torch.no_grad():
+                        # Send "real" orig_weight into MHA's lora module
+                        qw, kw, vw = self.in_proj_weight.chunk(3, 0)
+                        updown_q, _ = module_q.calc_updown(qw)
+                        updown_k, _ = module_k.calc_updown(kw)
+                        updown_v, _ = module_v.calc_updown(vw)
+                        del qw, kw, vw
+                        updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+                        updown_out, ex_bias = module_out.calc_updown(self.out_proj.weight)
+
+                        self.in_proj_weight += updown_qkv
+                        self.out_proj.weight += updown_out
+                    if ex_bias is not None:
+                        if self.out_proj.bias is None:
+                            self.out_proj.bias = torch.nn.Parameter(ex_bias)
+                        else:
+                            self.out_proj.bias += ex_bias
+
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue
+
+            if isinstance(self, modules.models.sd3.mmdit.QkvLinear) and module_q and module_k and module_v:
+                try:
+                    with torch.no_grad():
+                        # Send "real" orig_weight into MHA's lora module
+                        qw, kw, vw = self.weight.chunk(3, 0)
+                        updown_q, _ = module_q.calc_updown(qw)
+                        updown_k, _ = module_k.calc_updown(kw)
+                        updown_v, _ = module_v.calc_updown(vw)
+                        del qw, kw, vw
+                        updown_qkv = torch.vstack([updown_q, updown_k, updown_v])
+                        self.weight += updown_qkv
+
+                except RuntimeError as e:
+                    logging.debug(f"Network {net.name} layer {network_layer_name}: {e}")
+                    extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+                continue
+
+            if module is None:
+                continue
+
+            logging.debug(f"Network {net.name} layer {network_layer_name}: couldn't find supported operation")
+            extra_network_lora.errors[net.name] = extra_network_lora.errors.get(net.name, 0) + 1
+
+        self.network_current_names = wanted_names
+
+
+def network_forward(org_module, input, original_forward):
+    """
+    Old way of applying Lora by executing operations during layer's forward.
+    Stacking many loras this way results in big performance degradation.
+    """
+
+    if len(loaded_networks) == 0:
+        return original_forward(org_module, input)
+
+    input = devices.cond_cast_unet(input)
+
+    network_restore_weights_from_backup(org_module)
+    network_reset_cached_weight(org_module)
+
+    y = original_forward(org_module, input)
+
+    network_layer_name = getattr(org_module, 'network_layer_name', None)
+    for lora in loaded_networks:
+        module = lora.modules.get(network_layer_name, None)
+        if module is None:
+            continue
+
+        y = module.forward(input, y)
+
+    return y
+
+
+def network_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):
+    self.network_current_names = ()
+    self.network_weights_backup = None
+    self.network_bias_backup = None
+
+
+def network_Linear_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, originals.Linear_forward)
+
+    network_apply_weights(self)
+
+    return originals.Linear_forward(self, input)
+
+
+def network_Linear_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return originals.Linear_load_state_dict(self, *args, **kwargs)
+
+
+def network_Conv2d_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, originals.Conv2d_forward)
+
+    network_apply_weights(self)
+
+    return originals.Conv2d_forward(self, input)
+
+
+def network_Conv2d_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return originals.Conv2d_load_state_dict(self, *args, **kwargs)
+
+
+def network_GroupNorm_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, originals.GroupNorm_forward)
+
+    network_apply_weights(self)
+
+    return originals.GroupNorm_forward(self, input)
+
+
+def network_GroupNorm_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return originals.GroupNorm_load_state_dict(self, *args, **kwargs)
+
+
+def network_LayerNorm_forward(self, input):
+    if shared.opts.lora_functional:
+        return network_forward(self, input, originals.LayerNorm_forward)
+
+    network_apply_weights(self)
+
+    return originals.LayerNorm_forward(self, input)
+
+
+def network_LayerNorm_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return originals.LayerNorm_load_state_dict(self, *args, **kwargs)
+
+
+def network_MultiheadAttention_forward(self, *args, **kwargs):
+    network_apply_weights(self)
+
+    return originals.MultiheadAttention_forward(self, *args, **kwargs)
+
+
+def network_MultiheadAttention_load_state_dict(self, *args, **kwargs):
+    network_reset_cached_weight(self)
+
+    return originals.MultiheadAttention_load_state_dict(self, *args, **kwargs)
+
+
+def process_network_files(names: list[str] | None = None):
+    candidates = list(shared.walk_files(shared.cmd_opts.lora_dir, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
+    candidates += list(shared.walk_files(shared.cmd_opts.lyco_dir_backcompat, allowed_extensions=[".pt", ".ckpt", ".safetensors"]))
+    for filename in candidates:
+        if os.path.isdir(filename):
+            continue
+        name = os.path.splitext(os.path.basename(filename))[0]
+        # if names is provided, only load networks with names in the list
+        if names and name not in names:
+            continue
+        try:
+            entry = network.NetworkOnDisk(name, filename)
+        except OSError:  # should catch FileNotFoundError and PermissionError etc.
+            errors.report(f"Failed to load network {name} from {filename}", exc_info=True)
+            continue
+
+        available_networks[name] = entry
+
+        if entry.alias in available_network_aliases:
+            forbidden_network_aliases[entry.alias.lower()] = 1
+
+        available_network_aliases[name] = entry
+        available_network_aliases[entry.alias] = entry
+
+
+def update_available_networks_by_names(names: list[str]):
+    process_network_files(names)
+
+
+def list_available_networks():
+    available_networks.clear()
+    available_network_aliases.clear()
+    forbidden_network_aliases.clear()
+    available_network_hash_lookup.clear()
+    forbidden_network_aliases.update({"none": 1, "Addams": 1})
+
+    os.makedirs(shared.cmd_opts.lora_dir, exist_ok=True)
+
+    process_network_files()
+
+
+re_network_name = re.compile(r"(.*)\s*\([0-9a-fA-F]+\)")
+
+
+def infotext_pasted(infotext, params):
+    if "AddNet Module 1" in [x[1] for x in scripts.scripts_txt2img.infotext_fields]:
+        return  # if the other extension is active, it will handle those fields, no need to do anything
+
+    added = []
+
+    for k in params:
+        if not k.startswith("AddNet Model "):
+            continue
+
+        num = k[13:]
+
+        if params.get("AddNet Module " + num) != "LoRA":
+            continue
+
+        name = params.get("AddNet Model " + num)
+        if name is None:
+            continue
+
+        m = re_network_name.match(name)
+        if m:
+            name = m.group(1)
+
+        multiplier = params.get("AddNet Weight A " + num, "1.0")
+
+        added.append(f"<lora:{name}:{multiplier}>")
+
+    if added:
+        params["Prompt"] += "\n" + "".join(added)
+
+
+originals: lora_patches.LoraPatches = None
+
+extra_network_lora = None
+
+available_networks = {}
+available_network_aliases = {}
+loaded_networks = []
+loaded_bundle_embeddings = {}
+networks_in_memory = {}
+available_network_hash_lookup = {}
+forbidden_network_aliases = {}
+
+list_available_networks()

stable-diffusion-webui/extensions-builtin/Lora/preload.py

@@ -0,0 +1,8 @@
+import os
+from modules import paths
+from modules.paths_internal import normalized_filepath
+
+
+def preload(parser):
+    parser.add_argument("--lora-dir", type=normalized_filepath, help="Path to directory with Lora networks.", default=os.path.join(paths.models_path, 'Lora'))
+    parser.add_argument("--lyco-dir-backcompat", type=normalized_filepath, help="Path to directory with LyCORIS networks (for backawards compatibility; can also use --lyco-dir).", default=os.path.join(paths.models_path, 'LyCORIS'))

stable-diffusion-webui/extensions-builtin/Lora/scripts/lora_script.py

@@ -0,0 +1,102 @@
+import re
+
+import gradio as gr
+from fastapi import FastAPI
+
+import network
+import networks
+import lora  # noqa:F401
+import lora_patches
+import extra_networks_lora
+import ui_extra_networks_lora
+from modules import script_callbacks, ui_extra_networks, extra_networks, shared
+
+
+def unload():
+    networks.originals.undo()
+
+
+def before_ui():
+    ui_extra_networks.register_page(ui_extra_networks_lora.ExtraNetworksPageLora())
+
+    networks.extra_network_lora = extra_networks_lora.ExtraNetworkLora()
+    extra_networks.register_extra_network(networks.extra_network_lora)
+    extra_networks.register_extra_network_alias(networks.extra_network_lora, "lyco")
+
+
+networks.originals = lora_patches.LoraPatches()
+
+script_callbacks.on_model_loaded(networks.assign_network_names_to_compvis_modules)
+script_callbacks.on_script_unloaded(unload)
+script_callbacks.on_before_ui(before_ui)
+script_callbacks.on_infotext_pasted(networks.infotext_pasted)
+
+
+shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
+    "sd_lora": shared.OptionInfo("None", "Add network to prompt", gr.Dropdown, lambda: {"choices": ["None", *networks.available_networks]}, refresh=networks.list_available_networks),
+    "lora_preferred_name": shared.OptionInfo("Alias from file", "When adding to prompt, refer to Lora by", gr.Radio, {"choices": ["Alias from file", "Filename"]}),
+    "lora_add_hashes_to_infotext": shared.OptionInfo(True, "Add Lora hashes to infotext"),
+    "lora_bundled_ti_to_infotext": shared.OptionInfo(True, "Add Lora name as TI hashes for bundled Textual Inversion").info('"Add Textual Inversion hashes to infotext" needs to be enabled'),
+    "lora_show_all": shared.OptionInfo(False, "Always show all networks on the Lora page").info("otherwise, those detected as for incompatible version of Stable Diffusion will be hidden"),
+    "lora_hide_unknown_for_versions": shared.OptionInfo([], "Hide networks of unknown versions for model versions", gr.CheckboxGroup, {"choices": ["SD1", "SD2", "SDXL"]}),
+    "lora_in_memory_limit": shared.OptionInfo(0, "Number of Lora networks to keep cached in memory", gr.Number, {"precision": 0}),
+    "lora_not_found_warning_console": shared.OptionInfo(False, "Lora not found warning in console"),
+    "lora_not_found_gradio_warning": shared.OptionInfo(False, "Lora not found warning popup in webui"),
+}))
+
+
+shared.options_templates.update(shared.options_section(('compatibility', "Compatibility"), {
+    "lora_functional": shared.OptionInfo(False, "Lora/Networks: use old method that takes longer when you have multiple Loras active and produces same results as kohya-ss/sd-webui-additional-networks extension"),
+}))
+
+
+def create_lora_json(obj: network.NetworkOnDisk):
+    return {
+        "name": obj.name,
+        "alias": obj.alias,
+        "path": obj.filename,
+        "metadata": obj.metadata,
+    }
+
+
+def api_networks(_: gr.Blocks, app: FastAPI):
+    @app.get("/sdapi/v1/loras")
+    async def get_loras():
+        return [create_lora_json(obj) for obj in networks.available_networks.values()]
+
+    @app.post("/sdapi/v1/refresh-loras")
+    async def refresh_loras():
+        return networks.list_available_networks()
+
+
+script_callbacks.on_app_started(api_networks)
+
+re_lora = re.compile("<lora:([^:]+):")
+
+
+def infotext_pasted(infotext, d):
+    hashes = d.get("Lora hashes")
+    if not hashes:
+        return
+
+    hashes = [x.strip().split(':', 1) for x in hashes.split(",")]
+    hashes = {x[0].strip().replace(",", ""): x[1].strip() for x in hashes}
+
+    def network_replacement(m):
+        alias = m.group(1)
+        shorthash = hashes.get(alias)
+        if shorthash is None:
+            return m.group(0)
+
+        network_on_disk = networks.available_network_hash_lookup.get(shorthash)
+        if network_on_disk is None:
+            return m.group(0)
+
+        return f'<lora:{network_on_disk.get_alias()}:'
+
+    d["Prompt"] = re.sub(re_lora, network_replacement, d["Prompt"])
+
+
+script_callbacks.on_infotext_pasted(infotext_pasted)
+
+shared.opts.onchange("lora_in_memory_limit", networks.purge_networks_from_memory)

stable-diffusion-webui/extensions-builtin/Lora/ui_edit_user_metadata.py

@@ -0,0 +1,226 @@
+import datetime
+import html
+import random
+
+import gradio as gr
+import re
+
+from modules import ui_extra_networks_user_metadata
+
+
+def is_non_comma_tagset(tags):
+    average_tag_length = sum(len(x) for x in tags.keys()) / len(tags)
+
+    return average_tag_length >= 16
+
+
+re_word = re.compile(r"[-_\w']+")
+re_comma = re.compile(r" *, *")
+
+
+def build_tags(metadata):
+    tags = {}
+
+    ss_tag_frequency = metadata.get("ss_tag_frequency", {})
+    if ss_tag_frequency is not None and hasattr(ss_tag_frequency, 'items'):
+        for _, tags_dict in ss_tag_frequency.items():
+            for tag, tag_count in tags_dict.items():
+                tag = tag.strip()
+                tags[tag] = tags.get(tag, 0) + int(tag_count)
+
+    if tags and is_non_comma_tagset(tags):
+        new_tags = {}
+
+        for text, text_count in tags.items():
+            for word in re.findall(re_word, text):
+                if len(word) < 3:
+                    continue
+
+                new_tags[word] = new_tags.get(word, 0) + text_count
+
+        tags = new_tags
+
+    ordered_tags = sorted(tags.keys(), key=tags.get, reverse=True)
+
+    return [(tag, tags[tag]) for tag in ordered_tags]
+
+
+class LoraUserMetadataEditor(ui_extra_networks_user_metadata.UserMetadataEditor):
+    def __init__(self, ui, tabname, page):
+        super().__init__(ui, tabname, page)
+
+        self.select_sd_version = None
+
+        self.taginfo = None
+        self.edit_activation_text = None
+        self.slider_preferred_weight = None
+        self.edit_notes = None
+
+    def save_lora_user_metadata(self, name, desc, sd_version, activation_text, preferred_weight, negative_text, notes):
+        user_metadata = self.get_user_metadata(name)
+        user_metadata["description"] = desc
+        user_metadata["sd version"] = sd_version
+        user_metadata["activation text"] = activation_text
+        user_metadata["preferred weight"] = preferred_weight
+        user_metadata["negative text"] = negative_text
+        user_metadata["notes"] = notes
+
+        self.write_user_metadata(name, user_metadata)
+
+    def get_metadata_table(self, name):
+        table = super().get_metadata_table(name)
+        item = self.page.items.get(name, {})
+        metadata = item.get("metadata") or {}
+
+        keys = {
+            'ss_output_name': "Output name:",
+            'ss_sd_model_name': "Model:",
+            'ss_clip_skip': "Clip skip:",
+            'ss_network_module': "Kohya module:",
+        }
+
+        for key, label in keys.items():
+            value = metadata.get(key, None)
+            if value is not None and str(value) != "None":
+                table.append((label, html.escape(value)))
+
+        ss_training_started_at = metadata.get('ss_training_started_at')
+        if ss_training_started_at:
+            table.append(("Date trained:", datetime.datetime.utcfromtimestamp(float(ss_training_started_at)).strftime('%Y-%m-%d %H:%M')))
+
+        ss_bucket_info = metadata.get("ss_bucket_info")
+        if ss_bucket_info and "buckets" in ss_bucket_info:
+            resolutions = {}
+            for _, bucket in ss_bucket_info["buckets"].items():
+                resolution = bucket["resolution"]
+                resolution = f'{resolution[1]}x{resolution[0]}'
+
+                resolutions[resolution] = resolutions.get(resolution, 0) + int(bucket["count"])
+
+            resolutions_list = sorted(resolutions.keys(), key=resolutions.get, reverse=True)
+            resolutions_text = html.escape(", ".join(resolutions_list[0:4]))
+            if len(resolutions) > 4:
+                resolutions_text += ", ..."
+                resolutions_text = f"<span title='{html.escape(', '.join(resolutions_list))}'>{resolutions_text}</span>"
+
+            table.append(('Resolutions:' if len(resolutions_list) > 1 else 'Resolution:', resolutions_text))
+
+        image_count = 0
+        for _, params in metadata.get("ss_dataset_dirs", {}).items():
+            image_count += int(params.get("img_count", 0))
+
+        if image_count:
+            table.append(("Dataset size:", image_count))
+
+        return table
+
+    def put_values_into_components(self, name):
+        user_metadata = self.get_user_metadata(name)
+        values = super().put_values_into_components(name)
+
+        item = self.page.items.get(name, {})
+        metadata = item.get("metadata") or {}
+
+        tags = build_tags(metadata)
+        gradio_tags = [(tag, str(count)) for tag, count in tags[0:24]]
+
+        return [
+            *values[0:5],
+            item.get("sd_version", "Unknown"),
+            gr.HighlightedText.update(value=gradio_tags, visible=True if tags else False),
+            user_metadata.get('activation text', ''),
+            float(user_metadata.get('preferred weight', 0.0)),
+            user_metadata.get('negative text', ''),
+            gr.update(visible=True if tags else False),
+            gr.update(value=self.generate_random_prompt_from_tags(tags), visible=True if tags else False),
+        ]
+
+    def generate_random_prompt(self, name):
+        item = self.page.items.get(name, {})
+        metadata = item.get("metadata") or {}
+        tags = build_tags(metadata)
+
+        return self.generate_random_prompt_from_tags(tags)
+
+    def generate_random_prompt_from_tags(self, tags):
+        max_count = None
+        res = []
+        for tag, count in tags:
+            if not max_count:
+                max_count = count
+
+            v = random.random() * max_count
+            if count > v:
+                for x in "({[]})":
+                    tag = tag.replace(x, '\\' + x)
+                res.append(tag)
+
+        return ", ".join(sorted(res))
+
+    def create_extra_default_items_in_left_column(self):
+
+        # this would be a lot better as gr.Radio but I can't make it work
+        self.select_sd_version = gr.Dropdown(['SD1', 'SD2', 'SDXL', 'Unknown'], value='Unknown', label='Stable Diffusion version', interactive=True)
+
+    def create_editor(self):
+        self.create_default_editor_elems()
+
+        self.taginfo = gr.HighlightedText(label="Training dataset tags")
+        self.edit_activation_text = gr.Text(label='Activation text', info="Will be added to prompt along with Lora")
+        self.slider_preferred_weight = gr.Slider(label='Preferred weight', info="Set to 0 to disable", minimum=0.0, maximum=2.0, step=0.01)
+        self.edit_negative_text = gr.Text(label='Negative prompt', info="Will be added to negative prompts")
+        with gr.Row() as row_random_prompt:
+            with gr.Column(scale=8):
+                random_prompt = gr.Textbox(label='Random prompt', lines=4, max_lines=4, interactive=False)
+
+            with gr.Column(scale=1, min_width=120):
+                generate_random_prompt = gr.Button('Generate', size="lg", scale=1)
+
+        self.edit_notes = gr.TextArea(label='Notes', lines=4)
+
+        generate_random_prompt.click(fn=self.generate_random_prompt, inputs=[self.edit_name_input], outputs=[random_prompt], show_progress=False)
+
+        def select_tag(activation_text, evt: gr.SelectData):
+            tag = evt.value[0]
+
+            words = re.split(re_comma, activation_text)
+            if tag in words:
+                words = [x for x in words if x != tag and x.strip()]
+                return ", ".join(words)
+
+            return activation_text + ", " + tag if activation_text else tag
+
+        self.taginfo.select(fn=select_tag, inputs=[self.edit_activation_text], outputs=[self.edit_activation_text], show_progress=False)
+
+        self.create_default_buttons()
+
+        viewed_components = [
+            self.edit_name,
+            self.edit_description,
+            self.html_filedata,
+            self.html_preview,
+            self.edit_notes,
+            self.select_sd_version,
+            self.taginfo,
+            self.edit_activation_text,
+            self.slider_preferred_weight,
+            self.edit_negative_text,
+            row_random_prompt,
+            random_prompt,
+        ]
+
+        self.button_edit\
+            .click(fn=self.put_values_into_components, inputs=[self.edit_name_input], outputs=viewed_components)\
+            .then(fn=lambda: gr.update(visible=True), inputs=[], outputs=[self.box])
+
+        edited_components = [
+            self.edit_description,
+            self.select_sd_version,
+            self.edit_activation_text,
+            self.slider_preferred_weight,
+            self.edit_negative_text,
+            self.edit_notes,
+        ]
+
+
+        self.setup_save_handler(self.button_save, self.save_lora_user_metadata, edited_components)

stable-diffusion-webui/extensions-builtin/Lora/ui_extra_networks_lora.py

@@ -0,0 +1,90 @@
+import os
+
+import network
+import networks
+
+from modules import shared, ui_extra_networks
+from modules.ui_extra_networks import quote_js
+from ui_edit_user_metadata import LoraUserMetadataEditor
+
+
+class ExtraNetworksPageLora(ui_extra_networks.ExtraNetworksPage):
+    def __init__(self):
+        super().__init__('Lora')
+
+    def refresh(self):
+        networks.list_available_networks()
+
+    def create_item(self, name, index=None, enable_filter=True):
+        lora_on_disk = networks.available_networks.get(name)
+        if lora_on_disk is None:
+            return
+
+        path, ext = os.path.splitext(lora_on_disk.filename)
+
+        alias = lora_on_disk.get_alias()
+
+        search_terms = [self.search_terms_from_path(lora_on_disk.filename)]
+        if lora_on_disk.hash:
+            search_terms.append(lora_on_disk.hash)
+        item = {
+            "name": name,
+            "filename": lora_on_disk.filename,
+            "shorthash": lora_on_disk.shorthash,
+            "preview": self.find_preview(path) or self.find_embedded_preview(path, name, lora_on_disk.metadata),
+            "description": self.find_description(path),
+            "search_terms": search_terms,
+            "local_preview": f"{path}.{shared.opts.samples_format}",
+            "metadata": lora_on_disk.metadata,
+            "sort_keys": {'default': index, **self.get_sort_keys(lora_on_disk.filename)},
+            "sd_version": lora_on_disk.sd_version.name,
+        }
+
+        self.read_user_metadata(item)
+        activation_text = item["user_metadata"].get("activation text")
+        preferred_weight = item["user_metadata"].get("preferred weight", 0.0)
+        item["prompt"] = quote_js(f"<lora:{alias}:") + " + " + (str(preferred_weight) if preferred_weight else "opts.extra_networks_default_multiplier") + " + " + quote_js(">")
+
+        if activation_text:
+            item["prompt"] += " + " + quote_js(" " + activation_text)
+
+        negative_prompt = item["user_metadata"].get("negative text")
+        item["negative_prompt"] = quote_js("")
+        if negative_prompt:
+            item["negative_prompt"] = quote_js('(' + negative_prompt + ':1)')
+
+        sd_version = item["user_metadata"].get("sd version")
+        if sd_version in network.SdVersion.__members__:
+            item["sd_version"] = sd_version
+            sd_version = network.SdVersion[sd_version]
+        else:
+            sd_version = lora_on_disk.sd_version
+
+        if shared.opts.lora_show_all or not enable_filter or not shared.sd_model:
+            pass
+        elif sd_version == network.SdVersion.Unknown:
+            model_version = network.SdVersion.SDXL if shared.sd_model.is_sdxl else network.SdVersion.SD2 if shared.sd_model.is_sd2 else network.SdVersion.SD1
+            if model_version.name in shared.opts.lora_hide_unknown_for_versions:
+                return None
+        elif shared.sd_model.is_sdxl and sd_version != network.SdVersion.SDXL:
+            return None
+        elif shared.sd_model.is_sd2 and sd_version != network.SdVersion.SD2:
+            return None
+        elif shared.sd_model.is_sd1 and sd_version != network.SdVersion.SD1:
+            return None
+
+        return item
+
+    def list_items(self):
+        # instantiate a list to protect against concurrent modification
+        names = list(networks.available_networks)
+        for index, name in enumerate(names):
+            item = self.create_item(name, index)
+            if item is not None:
+                yield item
+
+    def allowed_directories_for_previews(self):
+        return [shared.cmd_opts.lora_dir, shared.cmd_opts.lyco_dir_backcompat]
+
+    def create_user_metadata_editor(self, ui, tabname):
+        return LoraUserMetadataEditor(ui, tabname, self)