关于stable diffusion 问题

Question

关于stable diffusion 问题

我用图生图局部重绘，骨架图都弄好了，我看了教程，设置完全没问题，但是却总是生成不出模特，生成出来的都是原图，请问是怎么回事？

Answer 1

让阿豪来帮你解答，本回答参考chatgpt3.5编写提供，如需一对一服务私聊我哦

可能的原因有以下几点：

1. 数据集不够大，导致模型无法学习到足够的特征。
2. 训练次数不够，需要增加训练轮数。
3. 学习率过大或过小，需要调整学习率。
4. 模型结构不够合理，需要进行调整或更换模型。
5. 损失函数不合适，需要使用适当的损失函数。
6. 数据预处理不够完善，需要进一步对数据进行清洗和增强。 示例代码： 针对以上可能原因，可以进行相应的调整和优化，例如：
7. 增加数据集大小

from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')
model.fit_generator(training_set,
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)

1. 增加训练次数

model.fit(X_train, y_train, 
          batch_size=128, 
          epochs=50, 
          validation_data=(X_test, y_test))

1. 调整学习率

from keras.callbacks import LearningRateScheduler
def step_decay(epoch):
   initial_lrate = 0.1
   drop = 0.5
   epochs_drop = 10.0
   lrate = initial_lrate * math.pow(drop,  
           math.floor((1+epoch)/epochs_drop))
   return lrate
lrate = LearningRateScheduler(step_decay)
model.fit(X_train, y_train,
          validation_data=(X_test, y_test), 
          epochs=50, 
          batch_size=128, 
          callbacks=[lrate])

1. 更换模型结构

from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
model = Sequential()
model.add(Conv2D(32, (3,3), input_shape = (64, 64, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Conv2D(64, (3,3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Conv2D(128, (3,3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Flatten())
model.add(Dense(units = 128, activation = 'relu'))
model.add(Dense(units = 1, activation = 'sigmoid'))
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
model.fit(X_train, y_train, epochs = 25, batch_size = 32, validation_data = (X_test, y_test))

1. 更换损失函数

from keras.losses import mean_squared_error
model.compile(optimizer = 'adam', loss = mean_squared_error, metrics = ['accuracy'])
model.fit(X_train, y_train, epochs = 25, batch_size = 32, validation_data = (X_test, y_test))

1. 进一步处理数据

from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')
model.fit_generator(training_set,
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)