import argparse import hashlib import os import shutil import sys import json import subprocess import time import torch from .mz_train_tools_utils import Utils import folder_paths import nodes # 初始化工具仓库和工作区 def MZ_KohyaSSInitWorkspace_call(args={}): Utils.clone_repo(args) workspace_name = args.get("lora_name", None) workspace_name = workspace_name.strip() if workspace_name is None or workspace_name == "": raise Exception("lora名称不能为空(lora_name is required)") args["workspace_name"] = workspace_name workspaces_dir = os.path.join( folder_paths.output_directory, "mz_train_workspaces") os.makedirs(workspaces_dir, exist_ok=True) workspace_dir = os.path.join(workspaces_dir, workspace_name) if not os.path.exists(workspace_dir): os.makedirs(workspace_dir) return ( args, ) def MZ_KohyaSSDatasetConfig_call(args={}): images = args.get("images") pil_images = Utils.tensors2pil_list(images) conditioning_images = args.get("conditioning_images", None) conditioning_pil_images = None if conditioning_images is not None: conditioning_pil_images = Utils.tensors2pil_list( conditioning_images) resolution = args.get("resolution", 512) workspace_config = args.get("workspace_config", {}) workspace_name = workspace_config.get("workspace_name", None) if workspace_name is None or workspace_name == "": raise Exception("lora名称不能为空(lora_name is required)") workspace_dir = os.path.join( folder_paths.output_directory, "mz_train_workspaces", workspace_name) train_images_dir = os.path.join(workspace_dir, "train_images") os.makedirs(train_images_dir, exist_ok=True) conditioning_images_dir = os.path.join( workspace_dir, "conditioning_images") force_clear = args.get("force_clear") == "enable" force_clear_only_images = args.get("force_clear_only_images") == "enable" if force_clear: if force_clear_only_images: images_files = Utils.listdir(train_images_dir) for file in images_files: if file.lower().endswith(".png") or file.lower().endswith(".jpg") or file.lower().endswith(".webp"): os.remove(os.path.join(train_images_dir, file)) else: shutil.rmtree(train_images_dir) os.makedirs(train_images_dir, exist_ok=True) image_format = args.get("image_format") file_extension = "." + image_format saved_images_path = [] for i, pil_image in enumerate(pil_images): pil_image = Utils.resize_max(pil_image, resolution, resolution) width, height = pil_image.size filename = hashlib.md5( pil_image.tobytes()).hexdigest() + file_extension pil_image.save(os.path.join(train_images_dir, filename)) saved_images_path.append(filename) if conditioning_pil_images is not None: os.makedirs(conditioning_images_dir, exist_ok=True) conditioning_pil_images[i].resize((width, height)).save( os.path.join(conditioning_images_dir, filename)) same_caption_generate = args.get("same_caption_generate") == "enable" if same_caption_generate: same_caption = args.get("same_caption").strip() if same_caption != "": # 循环已经保存的图片 for i, filename in enumerate(saved_images_path): base_filename = os.path.splitext(filename)[0] caption_filename = base_filename + ".caption" with open(os.path.join(train_images_dir, caption_filename), "w", encoding="utf-8") as f: f.write(same_caption) if conditioning_images is None: conditioning_images_dir = None caption_extension = args.get("caption_extension", ".caption") if os.path.exists(os.path.join(workspace_dir, "dataset.json")): os.remove(os.path.join(workspace_dir, "dataset.json")) if os.path.exists(os.path.join(workspace_dir, "dataset.toml")): os.remove(os.path.join(workspace_dir, "dataset.toml")) dataset_config_extension = args.get("dataset_config_extension") generate_dataset_config( workspace_dir=workspace_dir, output_path=os.path.join( workspace_dir, "dataset" + dataset_config_extension), enable_bucket=args.get("enable_bucket") == "enable", resolution=args.get("resolution"), batch_size=args.get("batch_size"), image_dir=train_images_dir, conditioning_data_dir=conditioning_images_dir, caption_extension=caption_extension, num_repeats=args.get("num_repeats"), is_reg=args.get("auto_reg") == "enable", ) return ( train_images_dir, ) def MZ_KohyaSSUseConfig_call(args={}): args = args.copy() workspace_config = args.get("workspace_config", {}) workspace_name = workspace_config.get("workspace_name", None) if workspace_name is None or workspace_name == "": raise Exception("工作区名称不能为空(workspace_name is required)") 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}") workspace_config_file = os.path.join(workspace_dir, "config.json") train_config_template = args.get("train_config_template", None) train_config_template_dir = args.get("train_config_template_dir", None) train_config_template_file = os.path.join( train_config_template_dir, train_config_template + ".json") # if not os.path.exists(workspace_config_file): # train_config_template_dir = args.get("train_config_template_dir", None) # train_config_template_file = os.path.join( # train_config_template_dir, train_config_template + ".json") # shutil.copy(train_config_template_file, workspace_config_file) 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: ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name) 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")) advanced_config = args.get("save_advanced_config", {}).copy() if len(advanced_config) == 0: advanced_config = args.get("advanced_config", {}).copy() 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}") with open(workspace_config_file, "w", encoding="utf-8") as f: json.dump(config, f, indent=4, ensure_ascii=False) # raise Exception(f"MZ_KohyaSSUseConfig_call: {args}") return ( args, ) def MZ_KohyaSSAdvConfig_call(args={}): return ( args, ) def config2args(train_parser: argparse.ArgumentParser, config): try: config_args_list = [] for key, value in config.items(): if value is None: continue if type(value) == bool: if value: config_args_list.append(f"--{key}") else: config_args_list.append(f"--{key}") config_args_list.append(str(value)) args = train_parser.parse_args(config_args_list) return args except Exception as e: raise Exception(f"config2args: {e}") def check_install(): try: import toml except ImportError: os.system(f"{sys.executable} -m pip install toml") # imagesize try: import imagesize except ImportError: os.system(f"{sys.executable} -m pip install imagesize") # voluptuous try: import voluptuous except ImportError: os.system(f"{sys.executable} -m pip install voluptuous") try: import diffusers except ImportError: os.system(f"{sys.executable} -m pip install diffusers") try: import accelerate except ImportError: os.system(f"{sys.executable} -m pip install accelerate") import logging 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): config = { 'general': { 'enable_bucket': enable_bucket, }, 'datasets': [ { 'resolution': resolution, 'batch_size': batch_size, 'subsets': [ { 'image_dir': image_dir, 'caption_extension': caption_extension, 'num_repeats': num_repeats, }, ], }, ], } if conditioning_data_dir is not None: config["datasets"][0]["subsets"][0]["conditioning_data_dir"] = conditioning_data_dir if is_reg: config["datasets"][0]["subsets"].append({ "is_reg": True, "image_dir": os.path.join(workspace_dir, "reg_images"), "num_repeats": 1, }) if output_path.endswith(".toml"): check_install() import toml with open(output_path, "w", encoding="utf-8") as f: toml.dump(config, f) elif output_path.endswith(".json"): with open(output_path, "w", encoding="utf-8") as f: json.dump(config, f, indent=4, ensure_ascii=False) else: raise Exception(f"不支持的文件格式: {output_path}") from PIL import Image def get_sample_images(workspace_dir, output_name): sample_images_dir = os.path.join( workspace_dir, "sample_images" ) pil_images = [] pre_render_texts_x = [] if os.path.exists(sample_images_dir): image_files = Utils.listdir(sample_images_dir) image_files = list( filter(lambda x: x.lower().endswith(".png"), image_files)) # 筛选 output_name 前缀 image_files = list( filter(lambda x: x.startswith(output_name), image_files)) image_files = sorted(image_files, key=lambda x: x) for image_file in image_files: pil_image = Image.open(os.path.join(sample_images_dir, image_file)) pil_images.append([pil_image]) pre_render_texts_x.append(image_file) if pil_images is None or len(pil_images) == 0: return Image.new("RGB", (512, 512), (255, 255, 255)) result = Utils.xy_image( pre_render_images=pil_images, pre_render_texts_x=pre_render_texts_x, pre_render_texts_y=[""], ) return result def run_hook_kohya_ss_run_file(workspace_dir, output_name, kohya_ss_tool_dir, trainer_func, use_screen=False): train_config_file = os.path.join(workspace_dir, "config.json") exec_pyfile = os.path.join(os.path.dirname( __file__), "hook_kohya_ss_run.py",) is_running = True taesd_type = "sd1_5" if trainer_func.find("sd1_5") != -1: taesd_type = "sd1_5" if trainer_func.find("sdxl") != -1: taesd_type = "sdxl" if trainer_func.find("hunyuan1_1") != -1: taesd_type = "sdxl" pb = Utils.progress_bar(0, taesd_type) import traceback import comfy.model_management stop_server = None def log_callback(log): try: comfy.model_management.throw_exception_if_processing_interrupted() except Exception as e: stop_server() if process_instance is not None: process_instance.stop() # 强制关闭进程 return is_running try: resp = log if resp.get("type") == "sample_images": global_step = resp.get("global_step") xy_img = get_sample_images(workspace_dir, output_name) max_side = max(xy_img.width, xy_img.height) # print(f"global_step: {global_step}, max_train_steps: {max_train_steps}") total_steps = resp.get("total_steps") pb.update( int(global_step), int(total_steps), ("JPEG", xy_img, max_side)) else: print(f"LOG: {log}") except Exception as e: print(f"LOG: {log} e: {e} ") print(f"stack: {traceback.format_exc()}") return is_running stop_server, port = Utils.Simple_Server(log_callback) try: cmd_list = [sys.executable, exec_pyfile, "--sys_path", kohya_ss_tool_dir, "--config", train_config_file, "--train_func", trainer_func, "--master_port", str(port)] startup_script_path_sh = os.path.join(workspace_dir, "start_train.sh") startup_script_path_bat = os.path.join( workspace_dir, "start_train.bat") with open(startup_script_path_sh, "w", encoding="utf-8") as f: f.write(" ".join(cmd_list)) with open(startup_script_path_bat, "w", encoding="utf-8") as f: f.write(" ".join(cmd_list)) from .mz_train_tools_utils import HSubprocess process_instance = HSubprocess( cmd_list) process_instance.wait() stop_server() is_running = False except Exception as e: stop_server() is_running = False raise Exception(f"训练失败!!! 具体报错信息请查看控制台...") def get_models_type(model_path): from comfy.model_detection import model_config_from_unet, unet_prefix_from_state_dict, detect_unet_config from comfy.supported_models import SDXL, SD15 import safetensors.torch sd = safetensors.torch.load_file(model_path, device="cpu") unet_prefix = unet_prefix_from_state_dict(sd) model_type = model_config_from_unet(sd, unet_prefix) if isinstance(model_type, SD15): return "lora_sd1_5" if isinstance(model_type, SDXL): return "lora_sdxl" raise Exception(f"不支持的模型类型: {model_type}") def generate_kohya_ss_config(args): args = args.copy() workspace_config = args.get("workspace_config", {}).copy() advanced_config = args.get("advanced_config", {}).copy() train_config = args.get("train_config", {}).copy() workspace_name = workspace_config.get("workspace_name", None) if workspace_name is None or workspace_name == "": raise Exception("工作区名称不能为空(workspace_name is required)") 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}") 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", )