750 lines
27 KiB
Python
750 lines
27 KiB
Python
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import argparse
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import hashlib
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import os
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import shutil
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import sys
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import json
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import subprocess
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import time
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import torch
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from .mz_train_tools_utils import Utils
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import folder_paths
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import nodes
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# 初始化工具仓库和工作区
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def MZ_KohyaSSInitWorkspace_call(args={}):
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Utils.clone_repo(args)
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workspace_name = args.get("lora_name", None)
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workspace_name = workspace_name.strip()
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if workspace_name is None or workspace_name == "":
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raise Exception("lora名称不能为空(lora_name is required)")
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args["workspace_name"] = workspace_name
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workspaces_dir = os.path.join(
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folder_paths.output_directory, "mz_train_workspaces")
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os.makedirs(workspaces_dir, exist_ok=True)
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workspace_dir = os.path.join(workspaces_dir, workspace_name)
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if not os.path.exists(workspace_dir):
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os.makedirs(workspace_dir)
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return (
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args,
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)
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def MZ_KohyaSSDatasetConfig_call(args={}):
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images = args.get("images")
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pil_images = Utils.tensors2pil_list(images)
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conditioning_images = args.get("conditioning_images", None)
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conditioning_pil_images = None
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if conditioning_images is not None:
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conditioning_pil_images = Utils.tensors2pil_list(
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conditioning_images)
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resolution = args.get("resolution", 512)
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workspace_config = args.get("workspace_config", {})
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workspace_name = workspace_config.get("workspace_name", None)
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if workspace_name is None or workspace_name == "":
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raise Exception("lora名称不能为空(lora_name is required)")
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workspace_dir = os.path.join(
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folder_paths.output_directory, "mz_train_workspaces", workspace_name)
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train_images_dir = os.path.join(workspace_dir, "train_images")
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os.makedirs(train_images_dir, exist_ok=True)
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conditioning_images_dir = os.path.join(
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workspace_dir, "conditioning_images")
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force_clear = args.get("force_clear") == "enable"
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force_clear_only_images = args.get("force_clear_only_images") == "enable"
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if force_clear:
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if force_clear_only_images:
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images_files = Utils.listdir(train_images_dir)
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for file in images_files:
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if file.lower().endswith(".png") or file.lower().endswith(".jpg") or file.lower().endswith(".webp"):
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os.remove(os.path.join(train_images_dir, file))
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else:
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shutil.rmtree(train_images_dir)
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os.makedirs(train_images_dir, exist_ok=True)
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image_format = args.get("image_format")
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file_extension = "." + image_format
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saved_images_path = []
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for i, pil_image in enumerate(pil_images):
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pil_image = Utils.resize_max(pil_image, resolution, resolution)
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width, height = pil_image.size
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filename = hashlib.md5(
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pil_image.tobytes()).hexdigest() + file_extension
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pil_image.save(os.path.join(train_images_dir, filename))
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saved_images_path.append(filename)
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if conditioning_pil_images is not None:
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os.makedirs(conditioning_images_dir, exist_ok=True)
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conditioning_pil_images[i].resize((width, height)).save(
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os.path.join(conditioning_images_dir, filename))
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same_caption_generate = args.get("same_caption_generate") == "enable"
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if same_caption_generate:
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same_caption = args.get("same_caption").strip()
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if same_caption != "":
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# 循环已经保存的图片
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for i, filename in enumerate(saved_images_path):
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base_filename = os.path.splitext(filename)[0]
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caption_filename = base_filename + ".caption"
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with open(os.path.join(train_images_dir, caption_filename), "w", encoding="utf-8") as f:
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f.write(same_caption)
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if conditioning_images is None:
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conditioning_images_dir = None
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caption_extension = args.get("caption_extension", ".caption")
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if os.path.exists(os.path.join(workspace_dir, "dataset.json")):
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os.remove(os.path.join(workspace_dir, "dataset.json"))
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if os.path.exists(os.path.join(workspace_dir, "dataset.toml")):
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os.remove(os.path.join(workspace_dir, "dataset.toml"))
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dataset_config_extension = args.get("dataset_config_extension")
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generate_dataset_config(
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workspace_dir=workspace_dir,
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output_path=os.path.join(
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workspace_dir, "dataset" + dataset_config_extension),
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enable_bucket=args.get("enable_bucket") == "enable",
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resolution=args.get("resolution"),
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batch_size=args.get("batch_size"),
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image_dir=train_images_dir,
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conditioning_data_dir=conditioning_images_dir,
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caption_extension=caption_extension,
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num_repeats=args.get("num_repeats"),
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is_reg=args.get("auto_reg") == "enable",
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)
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return (
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train_images_dir,
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)
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def MZ_KohyaSSUseConfig_call(args={}):
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args = args.copy()
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workspace_config = args.get("workspace_config", {})
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workspace_name = workspace_config.get("workspace_name", None)
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if workspace_name is None or workspace_name == "":
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raise Exception("工作区名称不能为空(workspace_name is required)")
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workspace_dir = os.path.join(
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folder_paths.output_directory, "mz_train_workspaces", workspace_name)
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if not os.path.exists(workspace_dir):
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raise Exception(f"工作区不存在: {workspace_dir}")
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workspace_config_file = os.path.join(workspace_dir, "config.json")
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train_config_template = args.get("train_config_template", None)
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train_config_template_dir = args.get("train_config_template_dir", None)
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train_config_template_file = os.path.join(
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train_config_template_dir, train_config_template + ".json")
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# if not os.path.exists(workspace_config_file):
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# train_config_template_dir = args.get("train_config_template_dir", None)
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# train_config_template_file = os.path.join(
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# train_config_template_dir, train_config_template + ".json")
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# shutil.copy(train_config_template_file, workspace_config_file)
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config = None
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with open(train_config_template_file, "r", encoding="utf-8") as f:
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config = json.load(f)
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config["metadata"]["train_type"] = train_config_template
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ckpt_name = args.get("ckpt_name", "")
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if ckpt_name != "" and ckpt_name is not None:
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ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
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config["train_config"]["pretrained_model_name_or_path"] = ckpt_path
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# output_dir
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output_dir = os.path.join(workspace_dir, "output")
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config["train_config"]["output_dir"] = output_dir
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datetime = time.strftime("%Y%m%d_%H%M%S", time.localtime())
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# output_name
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config["train_config"]["output_name"] = f"{workspace_name}_{train_config_template}_{datetime}"
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dataset_config_path = os.path.join(
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workspace_dir, "dataset.json")
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if not os.path.exists(dataset_config_path):
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dataset_config_path = os.path.join(
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workspace_dir, "dataset.toml")
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config["train_config"]["dataset_config"] = dataset_config_path
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config["train_config"]["max_train_steps"] = str(
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args.get("max_train_steps"))
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config["train_config"]["max_train_epochs"] = str(
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args.get("max_train_epochs"))
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if config["train_config"]["max_train_epochs"] == "0":
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config["train_config"]["max_train_epochs"] = False
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config["train_config"]["save_every_n_epochs"] = str(
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args.get("save_every_n_epochs"))
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config["train_config"]["learning_rate"] = str(
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args.get("learning_rate"))
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advanced_config = args.get("save_advanced_config", {}).copy()
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if len(advanced_config) == 0:
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advanced_config = args.get("advanced_config", {}).copy()
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for k in advanced_config:
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print(f"{k}= {advanced_config[k]}")
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if type(advanced_config[k]) == str and advanced_config[k] == "":
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if k in config["train_config"]:
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del config["train_config"][k]
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continue
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elif advanced_config[k] == "enable":
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advanced_config[k] = True
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elif advanced_config[k] == "disable":
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advanced_config[k] = False
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else:
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advanced_config[k] = str(advanced_config[k])
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config["train_config"][k] = advanced_config[k]
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# raise Exception(f"args: {json.dumps(config, indent=4)}")
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if config is None:
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raise Exception(f"读取配置文件失败: {workspace_config_file}")
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with open(workspace_config_file, "w", encoding="utf-8") as f:
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json.dump(config, f, indent=4, ensure_ascii=False)
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# raise Exception(f"MZ_KohyaSSUseConfig_call: {args}")
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return (
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args,
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)
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def MZ_KohyaSSAdvConfig_call(args={}):
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return (
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args,
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)
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def config2args(train_parser: argparse.ArgumentParser, config):
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try:
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config_args_list = []
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for key, value in config.items():
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if value is None:
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continue
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if type(value) == bool:
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if value:
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config_args_list.append(f"--{key}")
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else:
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config_args_list.append(f"--{key}")
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config_args_list.append(str(value))
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args = train_parser.parse_args(config_args_list)
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return args
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except Exception as e:
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raise Exception(f"config2args: {e}")
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def check_install():
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try:
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import toml
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except ImportError:
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os.system(f"{sys.executable} -m pip install toml")
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# imagesize
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try:
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import imagesize
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except ImportError:
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os.system(f"{sys.executable} -m pip install imagesize")
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# voluptuous
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try:
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import voluptuous
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except ImportError:
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os.system(f"{sys.executable} -m pip install voluptuous")
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try:
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import diffusers
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except ImportError:
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os.system(f"{sys.executable} -m pip install diffusers")
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try:
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import accelerate
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except ImportError:
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os.system(f"{sys.executable} -m pip install accelerate")
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import logging
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def generate_dataset_config(workspace_dir, output_path, enable_bucket=True, resolution=512, batch_size=1, image_dir=None, conditioning_data_dir=None, caption_extension=".caption", num_repeats=10, is_reg=False):
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config = {
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'general': {
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'enable_bucket': enable_bucket,
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},
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'datasets': [
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{
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'resolution': resolution,
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'batch_size': batch_size,
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'subsets': [
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{
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'image_dir': image_dir,
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'caption_extension': caption_extension,
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'num_repeats': num_repeats,
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},
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],
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},
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],
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}
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if conditioning_data_dir is not None:
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config["datasets"][0]["subsets"][0]["conditioning_data_dir"] = conditioning_data_dir
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if is_reg:
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config["datasets"][0]["subsets"].append({
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"is_reg": True,
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"image_dir": os.path.join(workspace_dir, "reg_images"),
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"num_repeats": 1,
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})
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if output_path.endswith(".toml"):
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check_install()
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import toml
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with open(output_path, "w", encoding="utf-8") as f:
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toml.dump(config, f)
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elif output_path.endswith(".json"):
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with open(output_path, "w", encoding="utf-8") as f:
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json.dump(config, f, indent=4, ensure_ascii=False)
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else:
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raise Exception(f"不支持的文件格式: {output_path}")
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from PIL import Image
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def get_sample_images(workspace_dir, output_name):
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sample_images_dir = os.path.join(
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workspace_dir, "sample_images"
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)
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pil_images = []
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pre_render_texts_x = []
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if os.path.exists(sample_images_dir):
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image_files = Utils.listdir(sample_images_dir)
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image_files = list(
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filter(lambda x: x.lower().endswith(".png"), image_files))
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# 筛选 output_name 前缀
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image_files = list(
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filter(lambda x: x.startswith(output_name), image_files))
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image_files = sorted(image_files, key=lambda x: x)
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for image_file in image_files:
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pil_image = Image.open(os.path.join(sample_images_dir, image_file))
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pil_images.append([pil_image])
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pre_render_texts_x.append(image_file)
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if pil_images is None or len(pil_images) == 0:
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return Image.new("RGB", (512, 512), (255, 255, 255))
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result = Utils.xy_image(
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pre_render_images=pil_images,
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pre_render_texts_x=pre_render_texts_x,
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pre_render_texts_y=[""],
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)
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return result
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def run_hook_kohya_ss_run_file(workspace_dir, output_name, kohya_ss_tool_dir, trainer_func, use_screen=False):
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train_config_file = os.path.join(workspace_dir, "config.json")
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exec_pyfile = os.path.join(os.path.dirname(
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__file__), "hook_kohya_ss_run.py",)
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is_running = True
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taesd_type = "sd1_5"
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if trainer_func.find("sd1_5") != -1:
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taesd_type = "sd1_5"
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if trainer_func.find("sdxl") != -1:
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taesd_type = "sdxl"
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if trainer_func.find("hunyuan1_1") != -1:
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taesd_type = "sdxl"
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pb = Utils.progress_bar(0, taesd_type)
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import traceback
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import comfy.model_management
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stop_server = None
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def log_callback(log):
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try:
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comfy.model_management.throw_exception_if_processing_interrupted()
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except Exception as e:
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stop_server()
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if process_instance is not None:
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process_instance.stop() # 强制关闭进程
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return is_running
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try:
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resp = log
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if resp.get("type") == "sample_images":
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global_step = resp.get("global_step")
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xy_img = get_sample_images(workspace_dir, output_name)
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max_side = max(xy_img.width, xy_img.height)
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# print(f"global_step: {global_step}, max_train_steps: {max_train_steps}")
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total_steps = resp.get("total_steps")
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pb.update(
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int(global_step), int(total_steps), ("JPEG", xy_img, max_side))
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else:
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print(f"LOG: {log}")
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except Exception as e:
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print(f"LOG: {log} e: {e} ")
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print(f"stack: {traceback.format_exc()}")
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return is_running
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stop_server, port = Utils.Simple_Server(log_callback)
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try:
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cmd_list = [sys.executable, exec_pyfile, "--sys_path", kohya_ss_tool_dir,
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"--config", train_config_file, "--train_func", trainer_func, "--master_port", str(port)]
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startup_script_path_sh = os.path.join(workspace_dir, "start_train.sh")
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startup_script_path_bat = os.path.join(
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workspace_dir, "start_train.bat")
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with open(startup_script_path_sh, "w", encoding="utf-8") as f:
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f.write(" ".join(cmd_list))
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with open(startup_script_path_bat, "w", encoding="utf-8") as f:
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f.write(" ".join(cmd_list))
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from .mz_train_tools_utils import HSubprocess
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process_instance = HSubprocess(
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cmd_list)
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process_instance.wait()
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stop_server()
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is_running = False
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except Exception as e:
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stop_server()
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is_running = False
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raise Exception(f"训练失败!!! 具体报错信息请查看控制台...")
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def get_models_type(model_path):
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from comfy.model_detection import model_config_from_unet, unet_prefix_from_state_dict, detect_unet_config
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from comfy.supported_models import SDXL, SD15
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import safetensors.torch
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sd = safetensors.torch.load_file(model_path, device="cpu")
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unet_prefix = unet_prefix_from_state_dict(sd)
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model_type = model_config_from_unet(sd, unet_prefix)
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if isinstance(model_type, SD15):
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return "lora_sd1_5"
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if isinstance(model_type, SDXL):
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return "lora_sdxl"
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raise Exception(f"不支持的模型类型: {model_type}")
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def generate_kohya_ss_config(args):
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args = args.copy()
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workspace_config = args.get("workspace_config", {}).copy()
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advanced_config = args.get("advanced_config", {}).copy()
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train_config = args.get("train_config", {}).copy()
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workspace_name = workspace_config.get("workspace_name", None)
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if workspace_name is None or workspace_name == "":
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raise Exception("工作区名称不能为空(workspace_name is required)")
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workspace_dir = os.path.join(
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folder_paths.output_directory, "mz_train_workspaces", workspace_name)
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if not os.path.exists(workspace_dir):
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raise Exception(f"工作区不存在: {workspace_dir}")
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workspace_config_file = os.path.join(workspace_dir, "config.json")
|
|
|
|
ckpt_name = args.get("ckpt_name", "")
|
|
ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
|
|
|
|
train_config_template = get_models_type(ckpt_path)
|
|
|
|
train_config_template_dir = os.path.join(
|
|
os.path.dirname(__file__), "configs", "kohya_ss_lora")
|
|
train_config_template_file = os.path.join(
|
|
train_config_template_dir, train_config_template + ".json")
|
|
|
|
# raise Exception(f"args: {json.dumps(args, indent=4, ensure_ascii=False)}")
|
|
config = None
|
|
with open(train_config_template_file, "r", encoding="utf-8") as f:
|
|
config = json.load(f)
|
|
config["metadata"]["train_type"] = train_config_template
|
|
ckpt_name = args.get("ckpt_name", "")
|
|
if ckpt_name != "" and ckpt_name is not None:
|
|
config["train_config"]["pretrained_model_name_or_path"] = ckpt_path
|
|
|
|
# output_dir
|
|
output_dir = os.path.join(workspace_dir, "output")
|
|
config["train_config"]["output_dir"] = output_dir
|
|
|
|
datetime = time.strftime("%Y%m%d_%H%M%S", time.localtime())
|
|
# output_name
|
|
config["train_config"]["output_name"] = f"{workspace_name}_{train_config_template}_{datetime}"
|
|
|
|
dataset_config_path = os.path.join(
|
|
workspace_dir, "dataset.json")
|
|
if not os.path.exists(dataset_config_path):
|
|
dataset_config_path = os.path.join(
|
|
workspace_dir, "dataset.toml")
|
|
|
|
config["train_config"]["dataset_config"] = dataset_config_path
|
|
|
|
config["train_config"]["max_train_steps"] = str(
|
|
args.get("max_train_steps"))
|
|
|
|
config["train_config"]["max_train_epochs"] = str(
|
|
args.get("max_train_epochs"))
|
|
if config["train_config"]["max_train_epochs"] == "0":
|
|
config["train_config"]["max_train_epochs"] = False
|
|
|
|
config["train_config"]["save_every_n_epochs"] = str(
|
|
args.get("save_every_n_epochs"))
|
|
|
|
config["train_config"]["learning_rate"] = str(
|
|
args.get("learning_rate"))
|
|
|
|
for k in advanced_config:
|
|
print(f"{k}= {advanced_config[k]}")
|
|
|
|
if type(advanced_config[k]) == str and advanced_config[k] == "":
|
|
if k in config["train_config"]:
|
|
del config["train_config"][k]
|
|
continue
|
|
elif advanced_config[k] == "enable":
|
|
advanced_config[k] = True
|
|
elif advanced_config[k] == "disable":
|
|
advanced_config[k] = False
|
|
else:
|
|
advanced_config[k] = str(advanced_config[k])
|
|
config["train_config"][k] = advanced_config[k]
|
|
|
|
# raise Exception(f"args: {json.dumps(config, indent=4)}")
|
|
|
|
if config is None:
|
|
raise Exception(f"读取配置文件失败: {workspace_config_file}")
|
|
|
|
# raise Exception(f"MZ_KohyaSSUseConfig_call: {args}")
|
|
return config
|
|
|
|
|
|
def generate_regularization_folder(ckpt_name, workspace_dir, caption_extension,
|
|
width=512, height=512):
|
|
train_images_dir = os.path.join(workspace_dir, "train_images")
|
|
saved_images_path = os.listdir(train_images_dir)
|
|
|
|
reg_dir = os.path.join(workspace_dir, "reg_images")
|
|
|
|
caption_files = []
|
|
caption_value = []
|
|
for i, filename in enumerate(saved_images_path):
|
|
if filename.lower().endswith(caption_extension):
|
|
caption_files.append(filename)
|
|
with open(os.path.join(train_images_dir, filename), "r", encoding="utf-8") as f:
|
|
caption_value.append(f.read())
|
|
|
|
if len(caption_files) == 0:
|
|
return
|
|
os.makedirs(reg_dir, exist_ok=True)
|
|
for filename, caption in zip(caption_files, caption_value):
|
|
if caption == "":
|
|
continue
|
|
|
|
with open(os.path.join(reg_dir, filename), "w", encoding="utf-8") as f:
|
|
f.write(caption)
|
|
|
|
image_filename = filename.replace(caption_extension, ".webp")
|
|
Utils.comfyui_fast_generate_picture(
|
|
ckpt_name=ckpt_name,
|
|
prompt=caption,
|
|
output=os.path.join(reg_dir, image_filename),
|
|
width=width,
|
|
height=height,
|
|
)
|
|
|
|
|
|
def MZ_KohyaSSTrain_call(args={}):
|
|
args = args.copy()
|
|
workspace_config = args.get("workspace_config").copy()
|
|
base_lora = args.get("base_lora", "empty")
|
|
sample_generate = args.get("sample_generate", "enable")
|
|
sample_prompt = args.get("sample_prompt", "")
|
|
|
|
workspace_name = workspace_config.get("workspace_name")
|
|
workspace_dir = os.path.join(
|
|
folder_paths.output_directory, "mz_train_workspaces", workspace_name)
|
|
|
|
if not os.path.exists(workspace_dir):
|
|
raise Exception(f"工作区不存在: {workspace_dir}")
|
|
|
|
config = generate_kohya_ss_config(args)
|
|
|
|
branch_local_name = workspace_config.get(
|
|
"branch_local_name", "kohya_ss_lora")
|
|
kohya_ss_tool_dir = os.path.join(
|
|
Utils.get_minus_zone_models_path(), "train_tools", branch_local_name)
|
|
|
|
if kohya_ss_tool_dir not in sys.path:
|
|
sys.path.append(kohya_ss_tool_dir)
|
|
check_install()
|
|
|
|
if base_lora == "empty":
|
|
pass
|
|
elif base_lora == "latest":
|
|
workspace_lora_dir = os.path.join(workspace_dir, "output")
|
|
if os.path.exists(workspace_lora_dir):
|
|
workspace_lora_files = Utils.listdir(workspace_lora_dir)
|
|
workspace_lora_files = list(
|
|
filter(lambda x: x.endswith(".safetensors"), workspace_lora_files))
|
|
workspace_lora_files = list(
|
|
map(lambda x: os.path.join(workspace_lora_dir, x), workspace_lora_files))
|
|
# 排序
|
|
workspace_lora_files = sorted(
|
|
workspace_lora_files, key=lambda x: os.path.getctime(x), reverse=True)
|
|
if len(workspace_lora_files) > 0:
|
|
base_lora = os.path.join(
|
|
workspace_lora_dir, workspace_lora_files[0])
|
|
else:
|
|
base_lora = "empty"
|
|
else:
|
|
pass
|
|
|
|
if base_lora != "empty" and os.path.exists(base_lora):
|
|
config["train_config"]["network_weights"] = base_lora
|
|
config["train_config"]["dim_from_weights"] = True
|
|
|
|
if "network_dim" in config["train_config"]:
|
|
del config["train_config"]["network_dim"]
|
|
if "network_alpha" in config["train_config"]:
|
|
del config["train_config"]["network_alpha"]
|
|
if "network_dropout" in config["train_config"]:
|
|
del config["train_config"]["network_dropout"]
|
|
|
|
base_controlnet = args.get("base_controlnet", "empty")
|
|
if base_controlnet == "empty":
|
|
pass
|
|
elif base_controlnet == "latest":
|
|
workspace_controlnet_dir = os.path.join(workspace_dir, "output")
|
|
if os.path.exists(workspace_controlnet_dir):
|
|
workspace_controlnet_files = Utils.listdir(
|
|
workspace_controlnet_dir)
|
|
workspace_controlnet_files = list(
|
|
filter(lambda x: x.endswith(".safetensors"), workspace_controlnet_files))
|
|
workspace_controlnet_files = list(
|
|
map(lambda x: os.path.join(workspace_controlnet_dir, x), workspace_controlnet_files))
|
|
# 排序
|
|
workspace_controlnet_files = sorted(
|
|
workspace_controlnet_files, key=lambda x: os.path.getctime(x), reverse=True)
|
|
if len(workspace_controlnet_files) > 0:
|
|
base_controlnet = os.path.join(
|
|
workspace_controlnet_dir, workspace_controlnet_files[0])
|
|
else:
|
|
base_controlnet = "empty"
|
|
else:
|
|
pass
|
|
|
|
if base_controlnet != "empty" and os.path.exists(base_controlnet):
|
|
config["train_config"]["controlnet_model_name_or_path"] = base_controlnet
|
|
|
|
train_type = config.get("metadata").get("train_type")
|
|
|
|
if sample_generate == "enable":
|
|
config["other_config"] = {
|
|
"sample_prompt": sample_prompt,
|
|
}
|
|
else:
|
|
config["other_config"] = {}
|
|
|
|
workspace_config_file = os.path.join(workspace_dir, "config.json")
|
|
with open(workspace_config_file, "w", encoding="utf-8") as f:
|
|
json.dump(config, f, indent=4, ensure_ascii=False)
|
|
|
|
dataset_config_file = config["train_config"]["dataset_config"]
|
|
with open(dataset_config_file, "r", encoding="utf-8") as f:
|
|
dataset_config = json.load(f)
|
|
|
|
if dataset_config is None:
|
|
raise Exception("dataset_config is None")
|
|
|
|
subsets_list = dataset_config.get("datasets")[0].get("subsets")
|
|
is_reg = True if len(subsets_list) == 2 and subsets_list[1].get(
|
|
"is_reg") else False
|
|
if is_reg:
|
|
caption_extension = subsets_list[0].get("caption_extension")
|
|
ckpt_name = args.get("ckpt_name")
|
|
resolution = dataset_config.get("datasets")[0].get("resolution")
|
|
if f"{resolution}".find("x") != -1:
|
|
resolutionsp = resolution.split("x")
|
|
width = int(resolutionsp[0])
|
|
height = int(resolutionsp[1])
|
|
elif f"{resolution}".find("*") != -1:
|
|
resolutionsp = resolution.split("*")
|
|
width = int(resolutionsp[0])
|
|
height = int(resolutionsp[1])
|
|
else:
|
|
width = height = int(resolution)
|
|
|
|
generate_regularization_folder(
|
|
ckpt_name, workspace_dir, caption_extension, width=width, height=height)
|
|
|
|
# raise Exception(
|
|
# f"config: {json.dumps(dataset_config, indent=4, ensure_ascii=False)}")
|
|
|
|
output_name = config["train_config"].get("output_name")
|
|
|
|
if train_type == "lora_sd1_5":
|
|
run_hook_kohya_ss_run_file(
|
|
workspace_dir, output_name, kohya_ss_tool_dir, "run_lora_sd1_5")
|
|
elif train_type == "lora_sdxl":
|
|
run_hook_kohya_ss_run_file(
|
|
workspace_dir, output_name, kohya_ss_tool_dir, "run_lora_sdxl")
|
|
elif train_type == "controlnet_sd1_5":
|
|
|
|
conditioning_images_dir = os.path.join(
|
|
workspace_dir, "conditioning_images")
|
|
conditioning_images_onec = ""
|
|
if os.path.exists(conditioning_images_dir):
|
|
conditioning_images_onec = Utils.listdir(
|
|
conditioning_images_dir)[0]
|
|
config["other_config"]["controlnet_image"] = os.path.join(
|
|
conditioning_images_dir, conditioning_images_onec)
|
|
with open(workspace_config_file, "w", encoding="utf-8") as f:
|
|
json.dump(config, f, indent=4, ensure_ascii=False)
|
|
|
|
run_hook_kohya_ss_run_file(
|
|
workspace_dir, output_name, kohya_ss_tool_dir, "run_controlnet_sd1_5")
|
|
else:
|
|
raise Exception(
|
|
f"暂时不支持的训练类型: {train_type}")
|
|
|
|
return (
|
|
"训练完成",
|
|
)
|