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