The World of Crypto

AI Pyt hon NFT code for your own bot. Created by @big_dwz Copy script below: {import random import string def generate_nft_metadata(): title = generate_random_string(10) description = generate_random_string(50) image_url = generate_random_string(20) + ".jpg" attributes = generate_random_attributes() metadata = { "title": title, "description": description, "image": image_url, "attributes": attributes } return metadata def generate_random_string(length): letters = string.ascii_letters return ''.join(random.choice(letters) for _ in range(length)) def generate_random_attributes(): attributes = {} for i in range(3): attribute_name = generate_random_string(5) attribute_value = generate_random_string(5) attributes[attribute_name] = attribute_value return attributes def gene

My custom AI images AI generated images are computer-generated images that are created using artificial intelligence algorithms. There are several different techniques for generating images using AI, including: Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator. The generator creates new images, while the discriminator attempts to distinguish the generated images from real images. Over time, the generator learns to produce images that are increasingly realistic, while the discriminator becomes better at identifying fake images. Variational Autoencoders (VAEs): VAEs consist of an encoder network that converts an input image into a set of latent variables, and a decoder network that generates an output image from the latent variables. The goal of the VAE is to learn a compact representation of the input image in the latent space, so that the decoder can generate new images that are similar to the input image. Autoregressive Mode