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/modules/sd_samplers.py

@@ -0,0 +1,132 @@
+from __future__ import annotations
+
+import functools
+import logging
+from modules import sd_samplers_kdiffusion, sd_samplers_timesteps, sd_samplers_lcm, shared, sd_samplers_common, sd_schedulers
+
+# imports for functions that previously were here and are used by other modules
+samples_to_image_grid = sd_samplers_common.samples_to_image_grid
+sample_to_image = sd_samplers_common.sample_to_image
+
+all_samplers = [
+    *sd_samplers_kdiffusion.samplers_data_k_diffusion,
+    *sd_samplers_timesteps.samplers_data_timesteps,
+    *sd_samplers_lcm.samplers_data_lcm,
+]
+all_samplers_map = {x.name: x for x in all_samplers}
+
+samplers: list[sd_samplers_common.SamplerData] = []
+samplers_for_img2img: list[sd_samplers_common.SamplerData] = []
+samplers_map = {}
+samplers_hidden = {}
+
+
+def find_sampler_config(name):
+    if name is not None:
+        config = all_samplers_map.get(name, None)
+    else:
+        config = all_samplers[0]
+
+    return config
+
+
+def create_sampler(name, model):
+    config = find_sampler_config(name)
+
+    assert config is not None, f'bad sampler name: {name}'
+
+    if model.is_sdxl and config.options.get("no_sdxl", False):
+        raise Exception(f"Sampler {config.name} is not supported for SDXL")
+
+    sampler = config.constructor(model)
+    sampler.config = config
+
+    return sampler
+
+
+def set_samplers():
+    global samplers, samplers_for_img2img, samplers_hidden
+
+    samplers_hidden = set(shared.opts.hide_samplers)
+    samplers = all_samplers
+    samplers_for_img2img = all_samplers
+
+    samplers_map.clear()
+    for sampler in all_samplers:
+        samplers_map[sampler.name.lower()] = sampler.name
+        for alias in sampler.aliases:
+            samplers_map[alias.lower()] = sampler.name
+
+
+def visible_sampler_names():
+    return [x.name for x in samplers if x.name not in samplers_hidden]
+
+
+def visible_samplers():
+    return [x for x in samplers if x.name not in samplers_hidden]
+
+
+def get_sampler_from_infotext(d: dict):
+    return get_sampler_and_scheduler(d.get("Sampler"), d.get("Schedule type"))[0]
+
+
+def get_scheduler_from_infotext(d: dict):
+    return get_sampler_and_scheduler(d.get("Sampler"), d.get("Schedule type"))[1]
+
+
+def get_hr_sampler_and_scheduler(d: dict):
+    hr_sampler = d.get("Hires sampler", "Use same sampler")
+    sampler = d.get("Sampler") if hr_sampler == "Use same sampler" else hr_sampler
+
+    hr_scheduler = d.get("Hires schedule type", "Use same scheduler")
+    scheduler = d.get("Schedule type") if hr_scheduler == "Use same scheduler" else hr_scheduler
+
+    sampler, scheduler = get_sampler_and_scheduler(sampler, scheduler)
+
+    sampler = sampler if sampler != d.get("Sampler") else "Use same sampler"
+    scheduler = scheduler if scheduler != d.get("Schedule type") else "Use same scheduler"
+
+    return sampler, scheduler
+
+
+def get_hr_sampler_from_infotext(d: dict):
+    return get_hr_sampler_and_scheduler(d)[0]
+
+
+def get_hr_scheduler_from_infotext(d: dict):
+    return get_hr_sampler_and_scheduler(d)[1]
+
+
+@functools.cache
+def get_sampler_and_scheduler(sampler_name, scheduler_name, *, convert_automatic=True):
+    default_sampler = samplers[0]
+    found_scheduler = sd_schedulers.schedulers_map.get(scheduler_name, sd_schedulers.schedulers[0])
+
+    name = sampler_name or default_sampler.name
+
+    for scheduler in sd_schedulers.schedulers:
+        name_options = [scheduler.label, scheduler.name, *(scheduler.aliases or [])]
+
+        for name_option in name_options:
+            if name.endswith(" " + name_option):
+                found_scheduler = scheduler
+                name = name[0:-(len(name_option) + 1)]
+                break
+
+    sampler = all_samplers_map.get(name, default_sampler)
+
+    # revert back to Automatic if it's the default scheduler for the selected sampler
+    if convert_automatic and sampler.options.get('scheduler', None) == found_scheduler.name:
+        found_scheduler = sd_schedulers.schedulers[0]
+
+    return sampler.name, found_scheduler.label
+
+
+def fix_p_invalid_sampler_and_scheduler(p):
+    i_sampler_name, i_scheduler = p.sampler_name, p.scheduler
+    p.sampler_name, p.scheduler = get_sampler_and_scheduler(p.sampler_name, p.scheduler, convert_automatic=False)
+    if p.sampler_name != i_sampler_name or i_scheduler != p.scheduler:
+        logging.warning(f'Sampler Scheduler autocorrection: "{i_sampler_name}" -> "{p.sampler_name}", "{i_scheduler}" -> "{p.scheduler}"')
+
+
+set_samplers()