import os import random import torch import logging import sys import json import importlib import argparse import toml def config2args(train_parser: argparse.ArgumentParser, config): config_args_list = [] for key, value in config.items(): 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 from PIL import Image import numpy as np import tempfile import safetensors.torch import hook_kohya_ss_utils other_config = {} original_save_model = None train_config_json = "{}" sample_images_pipe_class = None def utils_sample_images(*args, **kwargs): return sample_images(None, *args, **kwargs) def sample_images(self, *args, **kwargs): # accelerator, args, epoch, global_step, device, vae, tokenizer, text_encoder, unet accelerator = args[0] cmd_args = args[1] epoch = args[2] global_step = args[3] device = args[4] vae = args[5] tokenizer = args[6] text_encoder = args[7] unet = args[8] # print(f"sample_images: args = {args}") # print(f"sample_images: kwargs = {kwargs}") controlnet = kwargs.get("controlnet", None) if epoch is not None and cmd_args.save_every_n_epochs is not None and epoch % cmd_args.save_every_n_epochs == 0: prompt_dict_list = other_config.get("prompt_dict_list", []) if len(prompt_dict_list) == 0: seed = other_config.get("seed", 0) prompt_dict = { "controlnet_image": other_config.get("controlnet_image", None), "prompt": other_config.get("sample_prompt", ""), "seed": seed, "negative_prompt": "", "enum": 0, "sample_sampler": "euler_a", "sample_steps": 20, "scale": 5.0, } prompt_dict_list.append(prompt_dict) else: for i, prompt_dict in enumerate(prompt_dict_list): if prompt_dict.get("controlnet_image", None) is None: prompt_dict["controlnet_image"] = None if prompt_dict.get("seed", None) is None: prompt_dict["seed"] = 0 if prompt_dict.get("negative_prompt", None) is None: prompt_dict["negative_prompt"] = "" if prompt_dict.get("enum", None) is None: prompt_dict["enum"] = i hook_kohya_ss_utils.generate_image( pipe_class=sample_images_pipe_class, cmd_args=cmd_args, accelerator=accelerator, epoch=epoch, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, vae=vae, prompt_dict_list=prompt_dict_list, controlnet=controlnet, ) LOG({ "type": "sample_images", "global_step": global_step, "total_steps": cmd_args.max_train_steps, # "latent": noise_pred_latent_path, }) def run_lora_sd1_5(): hook_kohya_ss_utils.hook_kohya_ss() # 覆盖sample_images生成函数,包括进度条和生成图片功能 import train_network train_network.NetworkTrainer.sample_images = sample_images # 配置对应的pipeline import library.train_util global sample_images_pipe_class sample_images_pipe_class = library.train_util.StableDiffusionLongPromptWeightingPipeline trainer = train_network.NetworkTrainer() train_config = json.loads(train_config_json) train_args = config2args(train_network.setup_parser(), train_config) LOG({ "type": "start_train", }) trainer.train(train_args) def run_lora_sdxl(): hook_kohya_ss_utils.hook_kohya_ss() # 覆盖sample_images生成函数,包括进度条和生成图片功能 import sdxl_train_network sdxl_train_network.SdxlNetworkTrainer.sample_images = sample_images # 配置对应的pipeline import library.sdxl_train_util global sample_images_pipe_class sample_images_pipe_class = library.sdxl_train_util.SdxlStableDiffusionLongPromptWeightingPipeline trainer = sdxl_train_network.SdxlNetworkTrainer() train_config = json.loads(train_config_json) train_args = config2args(sdxl_train_network.setup_parser(), train_config) LOG({ "type": "start_train", }) trainer.train(train_args) from types import SimpleNamespace class SimpleNamespaceCNWarrper(SimpleNamespace): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.__dict__.update(kwargs) # or self.__dict__ = kwargs self.__dict__["mid_block_type"] = "UNetMidBlock2DCrossAttn" self.__dict__["_diffusers_version"] = "0.6.0" self.__iter__ = lambda: iter(kwargs.keys()) # is not iterable def __iter__(self): return iter(self.__dict__.keys()) # object has no attribute 'num_attention_heads' def __getattr__(self, name): return self.__dict__.get(name, None) def run_controlnet_sd1_5(): import types types.SimpleNamespace = SimpleNamespaceCNWarrper hook_kohya_ss_utils.hook_kohya_ss() # 覆盖sample_images生成函数,包括进度条和生成图片功能 import train_controlnet # 配置对应的pipeline import library.train_util library.train_util.sample_images = utils_sample_images global sample_images_pipe_class sample_images_pipe_class = library.train_util.StableDiffusionLongPromptWeightingPipeline train_config = json.loads(train_config_json) train_args = config2args(train_controlnet.setup_parser(), train_config) LOG({ "type": "start_train", }) train_controlnet.train(train_args) func_map = { "run_lora_sd1_5": run_lora_sd1_5, "run_lora_sdxl": run_lora_sdxl, "run_controlnet_sd1_5": run_controlnet_sd1_5, } import requests def LOG(log): # 发送http resp = requests.request("post", f"http://127.0.0.1:{master_port}/log", data=json.dumps(log), headers={ "Content-Type": "application/json"}) if resp.status_code != 200: raise Exception(f"LOG failed: {resp.text}") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--sys_path", type=str, default="") parser.add_argument("--train_config_json", type=str, default="") parser.add_argument("--train_func", type=str, default="") parser.add_argument("--master_port", type=int, default=0) parser.add_argument("--other_config_json", type=str, default="{}") args = parser.parse_args() other_config_json = args.other_config_json other_config = json.loads(other_config_json) master_port = args.master_port print(f"master_port = {master_port}") sys_path = args.sys_path if sys_path != "": sys.path.append(sys_path) train_config_json = args.train_config_json train_func = args.train_func if train_func == "": raise Exception("train_func is empty") func_map[train_func]()