确保伏羲0.12（文生图）注释和GUI显示均为中文，项目文件夹名称为中文，并提供使用说明。此外，我将完善风格迁移的确定及训练函数和代码。以下是完整的Python文件和相关说明。
项目结构
文本生成多模态项目/
├── config.yaml
├── data/
│ ├── dataset.csv
│ └── input.txt
├── models/
│ ├── model1.pth
│ ├── model2.pth
│ └── model3.pth
├── output/
│ ├── 图像/
│ ├── 视频/
│ └── 音频/
├── main.py
└── README.md
config.yaml
device: 'cuda'
data:dataset_path: 'data/dataset.csv'input_file: 'data/input.txt'output_dir: 'output'image_output_dir: 'output/图像'video_output_dir: 'output/视频'audio_output_dir: 'output/音频'
model:text_encoder_model_name: 'bert-base-uncased'audio_generator_model_name: 'tacotron2'path: 'models/model1.pth'path1: 'models/model1.pth'path2: 'models/model2.pth'path3: 'models/model3.pth'
training:learning_rate: 0.0002batch_size: 64epochs: 100log_dir: 'logs'
main.py
import os
import yaml
import torch
import torch.optim as optim
import torch.nn as nn
import torchvision.transforms as transforms
import pandas as pd
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, AutoModel
import random
import numpy as np
import logging
from tqdm import tqdm
from tensorboardX import SummaryWriter
import threading
import tkinter as tk
from tkinter import filedialog, messagebox
from PIL import Image, ImageTk
from cryptography.fernet import Fernet
import unittest
import matplotlib.pyplot as plt# 配置文件加载
def load_config(config_path):"""从配置文件中加载配置参数。:param config_path: 配置文件的路径:return: 配置参数字典"""try:with open(config_path, 'r', encoding='utf-8') as file:config = yaml.safe_load(file)return configexcept FileNotFoundError:logging.error(f"配置文件 {config_path} 未找到")raiseexcept yaml.YAMLError as e:logging.error(f"配置文件解析错误: {e}")raise# 数据加载
def load_text_data(file_path):"""从文本文件中加载数据。:param file_path: 文本文件的路径:return: 文本数据列表"""try:with open(file_path, 'r', encoding='utf-8') as file:text_data = file.readlines()return [line.strip() for line in text_data]except FileNotFoundError:logging.error(f"文本文件 {file_path} 未找到")raiseexcept IOError as e:logging.error(f"读取文本文件时发生错误: {e}")raise# 数据清洗
def clean_data(data):"""清洗数据，去除空值和重复值。:param data: DataFrame 数据:return: 清洗后的 DataFrame 数据"""return data.dropna().drop_duplicates()# 文本预处理
def preprocess_text(text, tokenizer):"""对文本进行预处理，转换为模型输入格式。:param text: 输入文本:param tokenizer: 分词器:return: 预处理后的文本张量"""return tokenizer(text, return_tensors='pt', padding=True, truncation=True)# 数据增强
def augment_data(image, mode, style_image=None):"""对图像进行数据增强。:param image: 输入图像:param mode: 增强模式（'train' 或 'test'）:param style_image: 风格图像:return: 增强后的图像"""if mode == 'train':transform = transforms.Compose([transforms.RandomHorizontalFlip(),transforms.RandomRotation(10),transforms.RandomResizedCrop(64, scale=(0.8, 1.0)),transforms.ColorJitter(brightness=0.2, contrast=0.2, saturation=0.2, hue=0.1),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])else:transform = transforms.Compose([transforms.Resize((64, 64)),transforms.ToTensor(),transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])image = transform(image)if style_image is not None:image = style_transfer(image, style_image)image = color_jitter(image)return image# 风格迁移
def style_transfer(image, style_image):"""风格迁移。:param image: 输入图像:param style_image: 风格图像:return: 迁移后的图像"""# 假设有一个预训练的风格迁移模型style_model = StyleTransferModel()return style_model(image, style_image)# 颜色抖动
def color_jitter(image):"""颜色抖动。:param image: 输入图像:return: 颜色抖动后的图像"""return transforms.functional.adjust_brightness(transforms.functional.adjust_contrast(transforms.functional.adjust_saturation(image, 1.2), 1.2), 1.2)# 文本编码器
class TextEncoder(nn.Module):"""文本编码器，使用预训练的BERT模型。"""def __init__(self, model_name):super(TextEncoder, self).__init__()self.tokenizer = AutoTokenizer.from_pretrained(model_name)self.model = AutoModel.from_pretrained(model_name)def forward(self, text):"""前向传播，将文本编码为特征向量。:param text: 输入文本:return: 编码后的特征向量"""inputs = self.tokenizer(text, return_tensors='pt', padding=True, truncation=True)outputs = self.model(**inputs)return outputs.last_hidden_state.mean(dim=1)# 图像生成器
class ImageGenerator(nn.Module):"""图像生成器，使用卷积转置层生成图像。"""def __init__(self, in_channels):super(ImageGenerator, self).__init__()self.decoder = nn.Sequential(nn.ConvTranspose2d(in_channels, 512, kernel_size=4, stride=1, padding=0),nn.BatchNorm2d(512),nn.ReLU(True),nn.ConvTranspose2d(512, 256, kernel_size=4, stride=2, padding=1),nn.BatchNorm2d(256),nn.ReLU(True),nn.ConvTranspose2d(256, 128, kernel_size=4, stride=2, padding=1),nn.BatchNorm2d(128),nn.ReLU(True),nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),nn.BatchNorm2d(64),nn.ReLU(True),nn.ConvTranspose2d(64, 3, kernel_size=4, stride=2, padding=1),nn.Tanh())def forward(self, x):"""前向传播，生成图像。:param x: 输入特征向量:return: 生成的图像"""x = x.view(-1, x.size(1), 1, 1)return self.decoder(x)# 视频生成器
class VideoGenerator(nn.Module):def __init__(self,<
