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import tensorflow as tf from tensorflow import keras

Define a simple generative model (e.g., DCGAN)

class PornGenerator(keras.Model): def init(self, latent_dim, img_height, img_width): super(PornGenerator, self).init()

    # Input layer for random noise (latent vector)
    self.latent_layer = keras.layers.Dense(latent_dim)
    
    # Convolutional and upsampling layers
    self.conv1 = keras.layers.Conv2D(64, 3, activation='relu', padding='same')
    self.up1 = keras.layers.UpSampling2D(size=(2, 2))
    self.conv2 = keras.layers.Conv2D(32, 3, activation='relu', padding='same')
    self.up2 = keras.layers.UpSampling2D(size=(2, 2))
    
    # Output layer
    self.out_layer = keras.layers.Conv2D(img_height * img_width * 3, 3, activation='tanh')

def call(self, inputs):
    # Encode the random noise into a latent vector
    x = self.latent_layer(inputs)
    
    # Convolutional and upsampling layers
    x = self.conv1(x)
    x = self.up1(x)
    x = self.conv2(x)
    x = self.up2(x)
    
    # Output layer
    x = self.out_layer(x)
    return tf.reshape(x, (-1, img_height, img_width, 3))

Set input parameters

latent_dim = 100 img_height = 256 img_width = 256

Create the generative model

generator = PornGenerator(latent_dim, img_height, img_width)

Compile the model

optimizer = keras.optimizers.Adam(learning_rate=0.0002) generator.compile(optimizer=optimizer, loss='binary_crossentropy')

Generate a pornographic image

def generate_porn_image(prompt): # Tokenize the prompt to extract keywords (not shown in this example) keywords = tokenize_prompt(prompt)

# Encode the input parameters into numerical format
inputs = tf.random.normal((1, latent_dim))

# Generate the image
generated_img = generator(inputs)

# Apply additional effects or adjustments as needed (e.g., color correction, cropping) (not shown in this example)

return generated_img

Example usage

prompt = "erotic art" generated_image = generate_porn_image(prompt) print(generated_image)