【情人节特辑】：虚拟女友教你如何正确“回答”

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2025-07-20

该项目旨在通过技术手段将“直男话术”转化为高情商表达，以增进情侣感情。其核心是让虚拟女友纠正不当话语，具体步骤如下：首先，输入直男语句（如“多喝热水”）转换为对应编号的高情商表达；接着用Pixel2Pixel模型将卡通照片真人化；再将真人化照片输入PaddleBoBo生成女友动画；最后让虚拟女友纠正话语。项目需32GB以上显卡环境，依赖相关模型和工具实现。

【情人节特辑】：虚拟女友教你如何正确“回答” - 游乐网

虚拟女友纠正话语器

情侣之间相处少不了摩擦，但是据发现很多不必要的吵架，往往是词不达意造成的。比如关心她的身体健康，要注意身体，往往就只说了句“多喝热水”。如果换成另外一种表达，会让对方更容易接受，也更容易接收你给的爱意。因此“会说话”就变得十分重要了。这个项目就给大家一个初步的示范，怎么样的高情商的回答会让这段感情升温。

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主要内容借鉴了我之前的项目：打造一个专属自己的卡通真人化主播

例如输入这张照片以及直男话术，你觉得会呈现出什么效果的视频呢?（doge）

【情人节特辑】：虚拟女友教你如何正确“回答” - 游乐网

直男语句：多喝热水。

效果展示

整体实现：

1.输入直男话语切换成高情商语句

2.利用Pixel2Pixel模型实现卡通照片真人化

3.把真人化输出的照片输入进PaddleBoBo生成女友动画

4.让虚拟女友纠正你的话语

PS：执行此项目请使用32GB显卡以上环境（看PaddleBoBo作者项目有提到，用16GB会爆内存导致跑不通，且本次项目也是在32GB显卡环境上制作的）

第一步、输入直男话语切换成高情商语句

请记住生成的编号等等用得着

In [ ]

huashu_dict={'多喝热水':'a',            '你怎么又生气了':'b',            '你又怎么了':'c',            '你要这样想我也没办法':'d',            '随便你！你定吧':'e',            '哦':'f'}#请输入上面指定语句（粗糙版，请大家多多包涵）a = input('请输入直男语句:'+'\n')if a in huashu_dict:    print('已生成合适的话术'+'\n'+'请记住生成编号'+':'+huashu_dict.get(a))else:    print('不好意思，这句话我还没学会呢。')

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请输入直男语句:已生成合适的话术请记住生成编号:a

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第二步、利用Pixel2Pixel模型实现卡通照片真人化

主要是修改 image_name='01503.webp'，改成自己心仪的动漫照片（最好使用逆向思维：卡通照片真人化项目里面数据集的照片文件，其他动漫照片生成效果不好看，我不负责的哈）

In [ ]

import paddleimport paddle.nn as nnfrom paddle.io import Dataset, DataLoaderimport osimport cv2import numpy as npfrom tqdm import tqdmimport matplotlib.pyplot as pltimport PIL.Image as Image%matplotlib inlineclass UnetGenerator(nn.Layer):    def __init__(self, input_nc=3, output_nc=3, ngf=64):        super(UnetGenerator, self).__init__()        self.down1 = nn.Conv2D(input_nc, ngf, kernel_size=4, stride=2, padding=1)        self.down2 = Downsample(ngf, ngf*2)        self.down3 = Downsample(ngf*2, ngf*4)        self.down4 = Downsample(ngf*4, ngf*8)        self.down5 = Downsample(ngf*8, ngf*8)        self.down6 = Downsample(ngf*8, ngf*8)        self.down7 = Downsample(ngf*8, ngf*8)        self.center = Downsample(ngf*8, ngf*8)        self.up7 = Upsample(ngf*8, ngf*8, use_dropout=True)        self.up6 = Upsample(ngf*8*2, ngf*8, use_dropout=True)        self.up5 = Upsample(ngf*8*2, ngf*8, use_dropout=True)        self.up4 = Upsample(ngf*8*2, ngf*8)        self.up3 = Upsample(ngf*8*2, ngf*4)        self.up2 = Upsample(ngf*4*2, ngf*2)        self.up1 = Upsample(ngf*2*2, ngf)        self.output_block = nn.Sequential(            nn.ReLU(),            nn.Conv2DTranspose(ngf*2, output_nc, kernel_size=4, stride=2, padding=1),            nn.Tanh()        )    def forward(self, x):        d1 = self.down1(x)        d2 = self.down2(d1)        d3 = self.down3(d2)        d4 = self.down4(d3)        d5 = self.down5(d4)        d6 = self.down6(d5)        d7 = self.down7(d6)                c = self.center(d7)                x = self.up7(c, d7)        x = self.up6(x, d6)        x = self.up5(x, d5)        x = self.up4(x, d4)        x = self.up3(x, d3)        x = self.up2(x, d2)        x = self.up1(x, d1)        x = self.output_block(x)        return xclass Downsample(nn.Layer):    # LeakyReLU => conv => batch norm    def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1):        super(Downsample, self).__init__()        self.layers = nn.Sequential(            nn.LeakyReLU(0.2),            nn.Conv2D(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False),            nn.BatchNorm2D(out_dim)        )    def forward(self, x):        x = self.layers(x)        return xclass Upsample(nn.Layer):    # ReLU => deconv => batch norm => dropout    def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1, use_dropout=False):        super(Upsample, self).__init__()        sequence = [            nn.ReLU(),            nn.Conv2DTranspose(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False),            nn.BatchNorm2D(out_dim)        ]        if use_dropout:            sequence.append(nn.Dropout(p=0.5))        self.layers = nn.Sequential(*sequence)    def forward(self, x, skip):        x = self.layers(x)        x = paddle.concat([x, skip], axis=1)        return x#实例化生成器generator = UnetGenerator()#加载权重last_weights_path = 'data/data148534/epoch100.pdparams'print('加载权重:', last_weights_path)model_state_dict = paddle.load(last_weights_path)generator.load_dict(model_state_dict)generator.eval()#读取数据image_name='01503.webp'img_A2B = cv2.imread('work/'+image_name)img_A = img_A2B[:, 256:]                                  # 卡通图（即输入）img_B = img_A2B[:, :256]                                  # 真人图（即预测结果）g_input = img_A.astype('float32') / 127.5 - 1             # 归一化g_input = g_input[np.newaxis, ...].transpose(0, 3, 1, 2)  # NHWC -> NCHWg_input = paddle.to_tensor(g_input)                       # numpy -> tensorg_output = generator(g_input)g_output = g_output.detach().numpy()                      # tensor -> numpyg_output = g_output.transpose(0, 2, 3, 1)[0]              # NCHW -> NHWCg_output = g_output * 127.5 + 127.5                       # 反归一化g_output = g_output.astype(np.uint8)#只保存生成真人图像img = np.asarray(g_output).copy()img = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))# cv2 to Imageimg.save('work/'+'output_'+image_name)img_show = np.hstack([img_A, g_output])[:,:,::-1]plt.figure(figsize=(8, 8))plt.imshow(img_show)plt.show()

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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  from collections import MutableMapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  from collections import Iterable, Mapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  from collections import SizedW0728 22:24:44.614435   192 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1W0728 22:24:44.619457   192 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.

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加载权重: data/data148534/epoch100.pdparams

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/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  if isinstance(obj, collections.Iterator):/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working  return list(data) if isinstance(data, collections.MappingView) else data

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效果展示

【情人节特辑】：虚拟女友教你如何正确“回答” - 游乐网

变身！！！

【情人节特辑】：虚拟女友教你如何正确“回答” - 游乐网

第三步、把真人化输出的照片输入进paddlebobo生成虚拟女友动画

3.1解压压缩包

In [11]

!tar xzvf bobo.tar.gz PaddleBoBo data nltk_data work

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3.2安装PaddleGAN和PaddleSpeech依赖

In [ ]

#这一步执行时间会比较久!pip install ppgan paddlespeech

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3.3动漫真人化图像生成虚拟女友动画

这一步用到了default.yaml的配置文件，如果你只是尝试的话使用默认配置即可，如果你需要生成另一个人像，请修改default.yaml配置。主要是修改输入照片的位置：PaddleBoBo/default.yaml 里面的FOM_INPUT_IMAGE: '/home/aistudio/work/output_01503.webp'

In [ ]

%cd PaddleBoBo!python create_virtual_human.py --config default.yaml

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第四步、让虚拟女友纠正你的话语

--text 请输入之前生成的编号

In [ ]

!python general_demo.py --human ./file/input/test.mp4 --output ../output.mp4 --text a

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效果展示

来源:https://www.php.cn/faq/1409995.html

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