from pathlib import Path
import pickle, gzip, os, math, time, shutil , matplotlib as mpl, matplotlib.pyplot as pltStable Diffusion from the ground up
Here we shall build a working Stable Diffusion model using just Python, the Python standard library, Matplotlib for plots, Jupyter Notebook, which is where we shall be writing our code, and nbdev, which we can use to create modules from notebooks.
Get data
MNIST_URL = 'https://github.com/mnielsen/neural-networks-and-deep-learning/blob/master/data/mnist.pkl.gz?raw=true'
path_data = Path('data')
path_data.mkdir(exist_ok=True)
path_gz = path_data/'mnist.pkl.gz'from urllib.request import urlretrieveurlretrieve<function urllib.request.urlretrieve(url, filename=None, reporthook=None, data=None)>if not path_gz.exists(): urlretrieve(MNIST_URL,path_gz)!ls -l datatotal 16656
-rw-r--r-- 1 rubanza rubanza 17051982 Jul 21 16:23 mnist.pkl.gz-rw-r–r– 1 user staff 15296311 Jul 21 10:30 mnist.pkl.gz | | | | | | | | | | | | | └── Filename | | | | | └── Modification time | | | | └── File size (bytes) | | | └── Group owner | | └── User owner
| └── Number of hard links └── File permissions
path_gzPosixPath('data/mnist.pkl.gz')#with gzip.open('data/mnist.pkl.gz','rb') as f:
#dataset = pickle.load(f, encoding='latin-1')dataset((array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], dtype=float32),
array([5, 0, 4, ..., 8, 4, 8])),
(array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], dtype=float32),
array([3, 8, 6, ..., 5, 6, 8])),
(array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], dtype=float32),
array([7, 2, 1, ..., 4, 5, 6])))with gzip.open(path_gz, 'rb') as f: ((x_train,y_train),(x_valid,y_valid),_) = pickle.load(f, encoding='latin-1')x_train.shape,y_train.shape,x_valid.shape,y_valid.shape((50000, 784), (50000,), (10000, 784), (10000,))x_trainarray([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]], dtype=float32)lst1 = list(x_train[0])
vals = lst1[200:210]
vals[0.0,
0.0,
0.0,
0.19140625,
0.9296875,
0.98828125,
0.98828125,
0.98828125,
0.98828125,
0.98828125]len(lst1)784len(lst1)784def chunks(x,sz):
for i in range(0, len(x), sz): yield x[i:i+5]list(chunks(vals,5))[[0.0, 0.0, 0.0, 0.19140625, 0.9296875],
[0.98828125, 0.98828125, 0.98828125, 0.98828125, 0.98828125]]val_iter = chunks(vals,5)val_iter<generator object chunks at 0x715981808580>next(val_iter)[0.0, 0.0, 0.0, 0.19140625, 0.9296875]next(val_iter)--------------------------------------------------------------------------- StopIteration Traceback (most recent call last) Cell In[78], line 1 ----> 1 next(val_iter) StopIteration:
mpl.rcParams['image.cmap']='gray'
plt.imshow(list(chunks(lst1,28)));
plt.rcParams['image.cmap'] = 'gray'
plt.imshow(list(chunks(lst1, 28)))
plt.xlim(0, 28) # Force x-axis to go 0-28
plt.ylim(0, 28) # Force y-axis to go 0-28
a = [1,2,3,4,5]
len(a)5for i in range(0,5):
print(i+1)1
2
3
4
5from itertools import isliceit = iter(vals)vals[0.0,
0.0,
0.0,
0.19140625,
0.9296875,
0.98828125,
0.98828125,
0.98828125,
0.98828125,
0.98828125]it<list_iterator at 0x715981848730>next(it)0.0next(it),next(it),next(it)(0.0, 0.0, 0.19140625)it = iter(vals)
islice(it,28)<itertools.islice at 0x76d844786660>isit = islice(it,5)
isit<itertools.islice at 0x7159819054e0>next(isit),next(isit),next(isit)(0.0, 0.0, 0.0)next(isit)0.19140625next(isit)0.9296875next(isit)--------------------------------------------------------------------------- StopIteration Traceback (most recent call last) Cell In[140], line 1 ----> 1 next(isit) StopIteration:
next(isit)0.19140625list(islice(it,5))[0.98828125, 0.98828125, 0.98828125]list(islice(it,28))[0.0,
0.0,
0.0,
0.19140625,
0.9296875,
0.98828125,
0.98828125,
0.98828125,
0.98828125,
0.98828125]list(islice(it,28))[]next(it)--------------------------------------------------------------------------- StopIteration Traceback (most recent call last) Cell In[122], line 1 ----> 1 next(it) StopIteration:
islice(it,2)<itertools.islice at 0x715981af47c0>list(islice(it,5))[]list(islice(it,5))[]using lamda
it = iter(lst1)
img = list(iter(lambda: list(islice(it, 28)), []))
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len(img)28it = iter(lst1)
def f(): return list(islice(it,28))f()[0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
0.0,
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0.01171875,
0.0703125,
0.0703125,
0.0703125,
0.4921875,
0.53125,
0.68359375,
0.1015625,
0.6484375,
0.99609375,
0.96484375,
0.49609375,
0.0,
0.0,
0.0,
0.0]#img[28]Matrix and tensors
import torch
from torch import tensorclass Matrix:
def __init__(self,xs): self.xs = xs
def __getitem__(self,idxs): return self.xs[idxs[0]][idxs[1]]img[20][15]--------------------------------------------------------------------------- NameError Traceback (most recent call last) Cell In[16], line 1 ----> 1 img[20][15] NameError: name 'img' is not defined
#img[27,25]m = Matrix(img)
m[20,15]b = [1,2,3]
bb = tensor(b)
blen(img)img_tens = tensor(img)
img_tens.shapeimg_tens[20,15]tensor(0.9883)s = [1,2,3]
s[1, 2, 3]def s_to_int(a):
a = a+1
return asa = list(map(s_to_int,s))
sa[2, 3, 4](x_train,y_train,x_valid,y_valid) = map(tensor, (x_train,y_train,x_valid,y_valid))x_train.shape,y_train.shape,x_valid.shape,y_valid.shape(torch.Size([50000, 784]),
torch.Size([50000]),
torch.Size([10000, 784]),
torch.Size([10000]))x_train.type()'torch.FloatTensor'Tensor
imgs = x_train.reshape((-1,28,28))
imgs.shapetorch.Size([50000, 28, 28])imgs_a = x_train[0].reshape((-1,28,28))
imgs_a.shapetorch.Size([1, 28, 28])tyo = x_train[1].shape#plt.imshow(tyo)plt.imshow(imgs[0])
imgs[0]tensor([[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
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[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0117, 0.0703, 0.0703, 0.0703, 0.4922, 0.5312,
0.6836, 0.1016, 0.6484, 0.9961, 0.9648, 0.4961, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1172,
0.1406, 0.3672, 0.6016, 0.6641, 0.9883, 0.9883, 0.9883, 0.9883, 0.9883,
0.8789, 0.6719, 0.9883, 0.9453, 0.7617, 0.2500, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1914, 0.9297,
0.9883, 0.9883, 0.9883, 0.9883, 0.9883, 0.9883, 0.9883, 0.9883, 0.9805,
0.3633, 0.3203, 0.3203, 0.2188, 0.1523, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0703, 0.8555,
0.9883, 0.9883, 0.9883, 0.9883, 0.9883, 0.7734, 0.7109, 0.9648, 0.9414,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.3125,
0.6094, 0.4180, 0.9883, 0.9883, 0.8008, 0.0430, 0.0000, 0.1680, 0.6016,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0547, 0.0039, 0.6016, 0.9883, 0.3516, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
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0.0000],
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0.0000, 0.0000, 0.0430, 0.7422, 0.9883, 0.2734, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
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0.0000, 0.0000, 0.0000, 0.1367, 0.9414, 0.8789, 0.6250, 0.4219, 0.0039,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.3164, 0.9375, 0.9883, 0.9883, 0.4648,
0.0977, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1758, 0.7266, 0.9883, 0.9883,
0.5859, 0.1055, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0625, 0.3633, 0.9844,
0.9883, 0.7305, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.9727,
0.9883, 0.9727, 0.2500, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.1797, 0.5078, 0.7148, 0.9883,
0.9883, 0.8086, 0.0078, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.1523, 0.5781, 0.8945, 0.9883, 0.9883, 0.9883,
0.9766, 0.7109, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0938, 0.4453, 0.8633, 0.9883, 0.9883, 0.9883, 0.9883, 0.7852,
0.3047, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0898,
0.2578, 0.8320, 0.9883, 0.9883, 0.9883, 0.9883, 0.7734, 0.3164, 0.0078,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0703, 0.6680, 0.8555,
0.9883, 0.9883, 0.9883, 0.9883, 0.7617, 0.3125, 0.0352, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.2148, 0.6719, 0.8828, 0.9883, 0.9883,
0.9883, 0.9883, 0.9531, 0.5195, 0.0430, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.5312, 0.9883, 0.9883, 0.9883, 0.8281,
0.5273, 0.5156, 0.0625, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000],
[0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
0.0000]])imgs[0,20,15]tensor(0.9883)n, c = x_train.shape
n,c(50000, 784)y_train, y_train.shape(tensor([5, 0, 4, ..., 8, 4, 8]), torch.Size([50000]))y_train[0].shapetorch.Size([])min(y_train), max(y_train)(tensor(0), tensor(9))y_train.min(), y_train.max()(tensor(0), tensor(9))Random numbers
Based on the Wichmann Hill algorithm
rnd_state = None
def seed(a):
global rnd_state
a, x = divmod(a,30268)
a, y = divmod(a, 30306)
a, z = divmod(a, 30322)
rnd_state = int(x)+1, int(y)+1, int(z)+1seed(42)
rnd_state(43, 1, 1)seed(457428938475)
rnd_state(4976, 20238, 499)def rand():
global rnd_state
x, y, z = rnd_state
x = (171 * x) % 30269
y = (172 * y) % 30307
z = (170 * z) % 30323
rnd_state = x, y, z
return (x/30269 + y/30307 + z/30323) % 1.0rand(),rand()(0.25420336316883324, 0.46884405296716114)rand()0.19540525690312815rand(),rand(),rand(),rand()(0.28886109883281286,
0.8643955691976015,
0.062341103558347655,
0.5214729908496198)if os.fork(): print(f'In parent: {rand()}')
else:
print(f' In child: {rand()}')
os._exit(os.EX_OK)In parent: 0.44425801591077185
In child: 0.44425801591077185if os.fork(): print(f'In parent: {torch.rand(1)}')
else:
print(f' In child: {torch.rand()}')
os._exit(os.EX_OK)In parent: tensor([0.9308])import numpy as npif os.fork(): print(f'In parent: {np.random.rand(1)}')
else:
print(f' In child: {np.random.rand()}')
os._exit(os.EX_OK) In child: 0.6953703052450606
In parent: [0.69537031]from random import randomif os.fork(): print(f'In parent: {random()}')
else:
print(f' In child: {random()}')
os._exit(os.EX_OK)In parent: 0.006104636974047728
In child: 0.22305777947091454plt.plot([rand() for _ in range(50)]);
plt.hist([rand() for _ in range(1000)]);