colomi
0eb61f3e60
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。
80 lines
2.6 KiB
Python
Executable File
80 lines
2.6 KiB
Python
Executable File
import os
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from modules import modelloader, errors
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from modules.shared import cmd_opts, opts
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from modules.upscaler import Upscaler, UpscalerData
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from modules.upscaler_utils import upscale_with_model
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class UpscalerDAT(Upscaler):
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def __init__(self, user_path):
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self.name = "DAT"
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self.user_path = user_path
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self.scalers = []
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super().__init__()
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for file in self.find_models(ext_filter=[".pt", ".pth"]):
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name = modelloader.friendly_name(file)
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scaler_data = UpscalerData(name, file, upscaler=self, scale=None)
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self.scalers.append(scaler_data)
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for model in get_dat_models(self):
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if model.name in opts.dat_enabled_models:
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self.scalers.append(model)
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def do_upscale(self, img, path):
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try:
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info = self.load_model(path)
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except Exception:
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errors.report(f"Unable to load DAT model {path}", exc_info=True)
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return img
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model_descriptor = modelloader.load_spandrel_model(
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info.local_data_path,
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device=self.device,
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prefer_half=(not cmd_opts.no_half and not cmd_opts.upcast_sampling),
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expected_architecture="DAT",
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)
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return upscale_with_model(
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model_descriptor,
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img,
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tile_size=opts.DAT_tile,
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tile_overlap=opts.DAT_tile_overlap,
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)
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def load_model(self, path):
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for scaler in self.scalers:
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if scaler.data_path == path:
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if scaler.local_data_path.startswith("http"):
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scaler.local_data_path = modelloader.load_file_from_url(
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scaler.data_path,
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model_dir=self.model_download_path,
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)
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if not os.path.exists(scaler.local_data_path):
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raise FileNotFoundError(f"DAT data missing: {scaler.local_data_path}")
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return scaler
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raise ValueError(f"Unable to find model info: {path}")
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def get_dat_models(scaler):
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return [
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UpscalerData(
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name="DAT x2",
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path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x2.pth",
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scale=2,
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upscaler=scaler,
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),
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UpscalerData(
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name="DAT x3",
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path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x3.pth",
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scale=3,
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upscaler=scaler,
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),
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UpscalerData(
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name="DAT x4",
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path="https://github.com/n0kovo/dat_upscaler_models/raw/main/DAT/DAT_x4.pth",
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scale=4,
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upscaler=scaler,
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),
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]
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