Image augmentation in Keras mixed functional model

I've created a mixed model in Keras, creating weights for metadata and image data and then combining them for the classification. Here's the model:

Model: "model_1"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_5 (InputLayer)            [(None, 80, 120, 3)] 0                                            
__________________________________________________________________________________________________
xception (Functional)           (None, 3, 4, 2048)   20861480    input_5[0][0]                    
__________________________________________________________________________________________________
input_4 (InputLayer)            [(None, 10)]         0                                            
__________________________________________________________________________________________________
conv2d_9 (Conv2D)               (None, 3, 4, 8)      409608      xception[0][0]                   
__________________________________________________________________________________________________
dense_3 (Dense)                 (None, 4)            44          input_4[0][0]                    
__________________________________________________________________________________________________
global_average_pooling2d_1 (Glo (None, 8)            0           conv2d_9[0][0]                   
__________________________________________________________________________________________________
concatenate_1 (Concatenate)     (None, 12)           0           dense_3[0][0]                    
                                                                 global_average_pooling2d_1[0][0] 
__________________________________________________________________________________________________
dense_4 (Dense)                 (None, 4)            52          concatenate_1[0][0]              
__________________________________________________________________________________________________
dense_5 (Dense)                 (None, 1)            5           dense_4[0][0]                    
==================================================================================================
Total params: 21,271,189
Trainable params: 21,216,661
Non-trainable params: 54,528
__________________________________________________________________________________________________

I decided to augment the images due to imbalance. I used the following ImageDataGenerator:

aug = ImageDataGenerator(rescale=1/255.,
                        rotation_range=180,
                        height_shift_range=0.2,
                        width_shift_range=0.2,
                        brightness_range=[0.5,1.5],
                        channel_shift_range=100.0,
                        horizontal_flip=True,
                        vertical_flip=True,
                        shear_range=45.0)

I then compiled and attempted to train the model using ImageDataGenerator().flow():

epochs = 10
BATCH_SIZE = 20
flow = aug.flow(img_train, y_train, batch_size=BATCH_SIZE)

history = model.fit([meta_train, flow], y_train, epochs=epochs, batch_size=100, validation_data=([meta_test, img_test], y_test), class_weight=class_weight)

This gives me an error:

ValueError: Failed to find data adapter that can handle input: (<class 'list'> containing values of
types {"<class 'pandas.core.frame.DataFrame'>", "<class 'tensorflow.python.keras.preprocessing.image.NumpyArrayIterator'>"}),
<class 'numpy.ndarray'>

I've tried multiple versions of the code, but I'm just not familiar enough with the backend to correctly diagnose the problem. Can anyone help me with this?

---

Model code and MRE

Model code

LEARNING_RATE = 0.001

# Define inputs
meta_inputs = Input(shape=(10,))
img_inputs = Input(shape=(80,120,3,))

# Model 1
meta_layer1 = Dense(4, activation='relu')(meta_inputs)

# Model 2
xception_layer = Xception(include_top=False, input_shape=(80,120,3,))(img_inputs)
img_conv_layer1 = Conv2D(8, kernel_size=(5,5), padding='same', activation='relu')(xception_layer)
img_gap_layer = GlobalAveragePooling2D()(img_conv_layer1)
# img_sdense_layer = Dense(4, activation='relu')(img_gap_layer)

# Merge models
merged_layer = Concatenate()([meta_layer1, img_gap_layer])
merged_dense_layer = Dense(4, activation='relu')(merged_layer)
merged_output = Dense(1, activation='sigmoid')(merged_dense_layer)


# Define functional model
model = Model(inputs=[meta_inputs, img_inputs], outputs=merged_output)

# Compile model
auc = AUC(name = 'auc')
model.compile(Adam(learning_rate=LEARNING_RATE), loss='binary_crossentropy', metrics=[auc])
model.summary()

meta_train MRE

       age_approx  Unknown  female  male  head/neck  lower extremity  \
11655          45        0       0     1          0                0   
24502          60        0       0     1          0                1   
2524           50        0       1     0          0                1   
13894          60        0       1     0          0                0   
29325          45        0       1     0          0                1   

       oral/genital  palms/soles  torso  upper extremity  
11655             0            0      1                0  
24502             0            0      0                0  
2524              0            0      0                0  
13894             0            0      1                0  
29325             0            0      0                0 

img_train MRE

Array too large, see code here.

y_train.shape

(23188, 1)

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