| import tensorflow as tf #We need tensorflow 2.x | |
| import numpy as np | |
| #The hashlength in bits | |
| hashLength = 256 | |
| def buildModel(): | |
| #we can set the seed to simulate the fact that this network is known and doesn't change between runs | |
| #tf.random.set_seed(42) | |
| model = tf.keras.Sequential() |
| import os | |
| import pickle | |
| import warnings | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from tensorflow.keras.callbacks import EarlyStopping | |
| from tensorflow.keras.layers import Dense | |
| from tensorflow.keras.layers import Dropout |
NOTE: Pytorch3d no longer has to be compiled i have stripped out the function we use to make this a lot easier and also so we do not have to use WSL2 with linux and can now run directly on your windows system.
Comments section is not checked often for issues please join the disco diffusion discord for assistance
You may now use the official disco diffusion notebook with this tutorial as it has been uodated to reflect the changes here for better cross platform support
| import torch | |
| import numpy as np | |
| import k_diffusion as K | |
| from PIL import Image | |
| from torch import autocast | |
| from einops import rearrange, repeat | |
| def pil_img_to_torch(pil_img, half=False): | |
| image = np.array(pil_img).astype(np.float32) / 255.0 |
