O le toʻatele oi tatou e masani ile faʻatupuina ata ole AI pei ole fa'asalalauga mautu. Ua uma ona suia le pisinisi ma ua tuʻufaʻatasia i o tatou olaga.
Ae ui i lea, o faʻataʻitaʻiga Stable Diffusion e sili atu nai lo le faʻatupuina o ata.
E tele naua vaega e mafai ona tatou fa'afaigaluegaina ai.
Stable Diffusion faʻataʻitaʻiga o faʻataʻitaʻiga o le matematika. Ma, e mafai ona latou fesoasoani ia te oe e suʻesuʻe le malosi o suiga o faiga ile taimi.
O lo'o fa'avae i luga o fa'amatalaga fa'agasologa o fa'asalalauga. O le mea lea, e mafai ona e suʻesuʻeina le tele o faʻailoga. Faataitaiga; fe'avea'i o le vevela, fa'a'avega fa'ama'i, ma fa'asalalauga fa'amatalaga i maketi tau tupe.
O nei faʻataʻitaʻiga e matua faʻafetaui. O lea la, e mafai ona e vaʻavaʻai i le lumanaʻi tulaga o se faiga e faʻatatau i lona tulaga o iai nei.
E le gata i lea, e mafai ona e vaʻai i mataupu faʻavae faaletino poʻo tupe o loʻo pulea ai. O lenei manatu ua aoga tele i le tele o vaega. E aofia ai le fisiki, kemisi, ma tupe.
O le mea lea matou te fia su'esu'e atili ai. Ma, matou te fia tuʻuina atu ia te oe se aʻoaʻoga i le auala e toleni ai nei faʻataʻitaʻiga Stable Diffusion.
Na fa'apefea ona fa'atupuina fa'ata'ita'iga Stable Diffusion?
E iai a'a lea i tua i le fa'ai'uga o le 19 seneturi.
O le su'esu'ega fa'a-matematika o fa'agasologa o fa'asalalauga i mataupu o lo'o amata ai fa'ata'ita'iga Stable Diffusion. O se tasi o faʻataʻitaʻiga Stable Diffusion sili ona lauiloa o le Fokker-Planck equation.
Na muamua tuʻuina atu i le 1906. O nei faʻataʻitaʻiga ua faʻaleleia ma suia i le taimi. O le mea lea, matou te faʻaaogaina nei i le tele o pisinisi.
O le a le Fa'atatau i tua?
I faaupuga faigofie, e pei ona matou fai atu, o latou o faʻataʻitaʻiga o le matematika. E le gata i lea, latou te fesoasoani ia i matou e suʻesuʻe pe faʻafefea ona sosolo se meatotino poʻo se aofaʻi i le taimi i se faiga.
O lo'o fa'avae i luga o ta'iala o faiga fa'asalalau. O lea la, latou te fesoasoani ia i matou e suʻesuʻe pe faʻafefea ona sosolo se aofaʻi i se faiga. O lenei fa'asalalauina o se fa'ai'uga o suiga i le fa'atonuga, mamafa, po'o isi ta'iala.
Sei o tatou tuuina atu se faataitaiga faigofie. Va'ai faalemafaufau o lo'o iai sau koneteina e tumu i vai na e fa'aopoopoina ai se vali. O lo'o va'aia le fa'asalalauga iinei pe a amata ona ta'ape le vali ma fa'amulumulu i totonu o le vai. Fa'atatau i uiga o le vai ma le vali, e mafai ona fa'aogaina fa'ata'ita'iga Stable Diffusion e va'ai ai pe fa'afefea ona taape le vali ma fa'afefiloi i le taimi.
I faiga e sili atu ona lavelave, e pei o maketi tau tupe poʻo faʻalavelave faʻamaʻi, e mafai e nei faʻataʻitaʻiga ona vaʻai pe faʻafefea ona salalau faʻamatalaga poʻo uiga ma aʻafia ai le faiga ile taimi. E le gata i lea, o faʻamatalaga tetele e mafai ona faʻaaogaina a'oa'o nei fa'ata'ita'iga e fai ai valo'aga sa'o. O lo'o fausia e fa'aaoga ai fua fa'atatau o le matematika e fa'amatala ai le fa'agasologa umi o le faiga.
O le malamalama ma le vavalo o le faʻalauteleina o uiga faʻapitoa i se faiga i le taimi o le manatu autu lea e faʻavaeina ai nei faʻataʻitaʻiga. E taua le manatua o tagata tomai faapitoa i matata fa'apitoa e masani ona fa'aogaina nei fa'ata'ita'iga.
Fa'afefea ona Toleni Fa'ata'ita'iga?
Fa'apotopoto ma saunia au fa'amaumauga:
E tatau ona e aoina muamua ma saunia au faʻamatalaga ae e te leʻi amata aʻoaʻoina lau faʻataʻitaʻiga. O au fa'amaumauga atonu e mana'omia ona fa'amama ma fa'atulaga. E le gata i lea, o numera o loʻo misi e ono manaʻomia foi ona faʻaumatia.
Filifili se ata fa'atusa
Stable Diffusion faʻataʻitaʻiga e sau i ituaiga eseese. E tele lava e fa'avae i luga o le Fokker-Planck equation, le Schrödinger equation, ma le Master equation. O le faʻataʻitaʻiga e sili ona fetaui ma lou tulaga faʻapitoa e tatau ona filifilia. O le mea lea, o nei faʻataʻitaʻiga taʻitasi e iai le lelei ma le le lelei.
Fa'atuina lau galuega leiloa
E taua tele talu ai e a'afia ai le lelei o lau fa'ata'ita'iga e fetaui ma fa'amaumauga. Mo fa'ata'ita'iga Stable Diffusion, o le mean squared error ma le Kullback-Leibler divergence o galuega fa'aletonu masani.
Aoao lau faʻataʻitaʻiga
I le fa'aogaina o le stochastic gradient descent po'o se faiga fa'apena fa'atusa, e mafai ona e amata a'oa'oina lau fa'ata'ita'iga pe a uma ona fa'auiga lau galuega gau.
Su'esu'e le tulaga lautele o lau fa'ata'ita'iga
E tatau ona e siakiina faʻamatalaga fou pe a maeʻa aʻoaʻoga e ala i le faʻatusatusaina i se seti suʻega o faʻamaumauga.
Fa'asu'e fua fa'atusa a lau fa'ata'ita'iga
Ina ia faʻaleleia le faʻatinoga o lau faʻataʻitaʻiga, faʻataʻitaʻi i tau eseese o hyperparameters e pei o le aʻoaʻoina o le fua, tele o vaega, ma le aofaʻi o laupepa natia i totonu o le fesoʻotaʻiga.
Toe fai taga muamua
Atonu e te mana'omia le toe faia o nei faiga e sili atu ma le fa'atasi ina ia maua ai fa'ai'uga sili. O le a faʻalagolago i le faigata o le faʻafitauli ma le caliber o faʻamaumauga.
A'oa'oga Fa'ailoga
Polokalame polokalame pei o le Python, MATLAB, C++, ma le R e mafai ona faʻaaogaina uma e fai ai faʻataʻitaʻiga Stable Diffusion. O le gagana e fa'aaogaina o le a fa'alagolago i le fa'aoga patino. E le gata i lea, e mafai ona faalagolago i meafaigaluega ma faletusi ua avanoa mo lena gagana.
Python o le filifiliga sili lea i lenei tulaga. E iai faletusi malolosi e pei o NumPy ma SciPy mo fa'asologa numera. E le gata i lea, e lagolagoina TensorFlow ma PyTorch mo le fatuina ma le aʻoaʻoina o fesoʻotaʻiga neural. O le mea lea, e avea ma filifiliga sili mo le tusiaina o faʻataʻitaʻiga Stable Diffusion.
faataitaiga:
Se'i o tatou fa'aogaina le fa'avasegaina o le fa'avasegaina, o se fua fa'a-matematika e fa'amatala ai pe fa'afefea ona suia se tulaga lelei po'o se aofa'i, e pei o le vevela po'o le fa'asalaina o se mea, i le taimi i se faiga. O le fa'atusa e masani lava e pei o lenei:
∂u/∂t = α ∇²u
O le diffusion coefficient () o se fua lea o le faigofie o se meatotino poʻo se aofaʻi e sosolo i totonu o se faiga.
O le Laplacian of u (2u) o se faʻamatalaga pe faʻafefea ona suia le meatotino poʻo le aofaʻi e faʻatatau i avanoa. O le u o le mea totino po'o le aofa'i o lo'o fa'asalalauina (mo se fa'ata'ita'iga, vevela po'o le fa'atonuga), t o le fa'asologa o le taimi, o le fa'asalalauga fa'atasi, ma o le fa'asalalauga tumau ().
E mafai ona matou faʻaaogaina e faʻaaoga ai le auala Euler i le Python.
import numpy as np
# Define the diffusion coefficient
alpha = 0.1
# Define the initial condition (e.g. initial temperature or concentration)
u = np.ones(100)
# Time step
dt = 0.01
# Time-stepping loop
for t in range(1000):
# Compute the spatial derivative
du = np.diff(u)
# Update the value of u
u[1:] = u[1:] + alpha * du * dt
O lenei tulafono e faʻaaogaina le auala Euler e faʻatino ai le faʻasologa faʻasalalau. O loʻo faʻamatalaina le tulaga amata o se tulaga muamua tutusa o loʻo faʻatusalia e se faʻasologa o mea e foliga mai o le (100). 0.01 o loʻo faʻaaogaina e fai ma laasaga o le taimi.
1000 fa'asologa o le fa'asologa o le taimi-laasaga ua mae'a.
E fa'aaogaina le galuega np.diff, lea e iloa ai le eseesega i le va o elemene tuaoi. O le mea lea, e fa'atatauina le fa'a-spatial derivative o le meatotino po'o le aofa'i o lo'o fa'asalalauina. Ma, o loʻo faʻatusalia e le du, i faʻasalalauga taʻitasi.
Ona tatou fa'ateleina lea o le fa'aagaga fa'afuainumera e ala i le diffusion coefficient alpha ma le taimi e fa'afou ai le tau o le u.
Ose Fa'ata'ita'iga Lavelave
O le a le fa'atusa o se fa'ata'ita'iga fa'asalalau mautu e na'o le fuaina o le fa'avevelaina mautu? E fa'afefea ona fa'aoga lena code?
Foia se seti o vaega fa'avasega fa'atusa (PDEs) e fa'amatala ai le fa'afefeteina o le vevela i luga o se faiga i le taimi e mana'omia. O lea la, e mafai ona matou aʻoaʻoina se faʻataʻitaʻiga Stable Diffusion e faʻatusaina le faʻasalalau tumau o le vevela.
O se faʻataʻitaʻiga lea o le faʻaogaina o le vevela, o se PDE o loʻo faʻamatalaina le Stable Diffusion o le vevela i totonu o le tasi-dimensional rod, e mafai ona foia e ala i le faʻaogaina o le eseesega iʻuga:
import numpy as np
import matplotlib.pyplot as plt
# Define the initial conditions
L = 1 # length of the rod
Nx = 10 # number of spatial grid points
dx = L / (Nx - 1) # spatial grid spacing
dt = 0.01 # time step
T = 1 # total time
# Set up the spatial grid
x = np.linspace(0, L, Nx)
# Set up the initial temperature field
T0 = np.zeros(Nx)
T0[0] = 100 # left boundary condition
T0[-1] = 0 # right boundary condition
# Set up the time loop
Tn = T0
for n in range(int(T / dt)):
Tnp1 = np.zeros(Nx)
Tnp1[0] = 100 # left boundary condition
Tnp1[-1] = 0 # right boundary condition
for i in range(1, Nx - 1):
Tnp1[i] = Tn[i] + dt * (Tn[i+1] - 2*Tn[i] + Tn[i-1]) / dx**2
Tn = Tnp1
# Plot the final temperature field
plt.plot(x, Tn)
plt.xlabel('x')
plt.ylabel('T(x)')
plt.show()
Fa'afefea ona Fa'atupuina Ata mai Fa'amatalaga?
Talu ai e sili ona lauiloa i luga o le initaneti, e mafai ona tatou siaki pe faʻafefea foi ona faʻaogaina ata.
Fa'asologa o gagana fa'alenatura (NLP) ma fesoʻotaiga i tua. Ma, e masani ona faʻaaogaina e tuʻuina atu ai se faʻataʻitaʻiga Stable Diffusion mo le liua o tusitusiga-i-ata. O se faʻamatalaga lautele o le auala e ausia ai o loʻo tuʻuina atu i lalo:
1- Fa'ailoga upu i fa'amaumauga o tusitusiga, ma aveese upu taofi ma faailoga. Su'e upu i ni numera. O se vaega o le fa'agaioiga (fa'apipi'i upu).
import nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')
# Pre-processing the text data
text = "a bird sitting on a flower. "
words = word_tokenize(text)
words = [word.lower() for word in words if word.isalpha()]
2- A'oa'o pe fa'afefea ona fa'afeso'ota'i tusitusiga ma ata e fa'aaoga ai se neural network e tu'ufa'atasia ai le encoder ma le decoder. E maua e le feso'ota'iga decoder le latent code e fai ma fa'aoga. Ona fa'atupuina lea o le ata fa'atasi pe a uma ona fa'aliliu e le feso'ota'iga encoder fa'amaumauga o tusitusiga i se fa'atusa fa'atusatusa (latent code).
import tensorflow as tf
# Define the encoder model
encoder = tf.keras.Sequential()
encoder.add(tf.keras.layers.Embedding(input_dim=vocab_size,
output_dim=latent_dim))
encoder.add(tf.keras.layers.GRU(latent_dim))
encoder.add(tf.keras.layers.Dense(latent_dim))
# Define the decoder model
decoder = tf.keras.Sequential()
decoder.add(tf.keras.layers.Dense(latent_dim,
input_shape=(latent_dim,)))
decoder.add(tf.keras.layers.GRU(latent_dim))
decoder.add(tf.keras.layers.Dense(vocab_size))
# Combine the encoder and decoder into an end-to-end model
model = tf.keras.Sequential([encoder, decoder])
3- E ala i le tuʻuina atu i ai o se aofaʻiga tele o ata ma faʻamatalaga o tusitusiga e o faatasi ma i latou. Ona, e mafai ona e aoaoina le encoder-decoder network.
# Compile the model
model.compile(optimizer='adam',
loss='categorical_crossentropy')
# Train the model on the dataset
model.fit(X_train, y_train, epochs=10, batch_size=32)
4- A mae'a ona a'oa'oina le feso'ota'iga, e mafai ona e fa'aogaina e maua mai ai ata mai tusitusiga fou. Ma, e ala i le fafagaina o tusitusiga i totonu o le encoder network. Ma, e mafai ona e faia se latent code, ona fafaga lea o le latent code i le decoder network e maua ai le ata o loʻo i ai.
# Encode the text input
latent_code = encoder.predict(text)
# Generate an image from the latent code
image = decoder.predict(latent_code)
5-O le filifilia o faʻamaumauga talafeagai ma galuega leiloa o se tasi lea o laasaga sili ona taua. O fa'amaumauga e 'ese'ese ma o lo'o iai le tele o ata ma fa'amatalaga tusitusia. Matou te mananaʻo ia mautinoa o ata e moni. E le gata i lea, e tatau ona tatou mautinoa o faʻamatalaga o tusitusiga e mafai ona mafai ona tatou mamanuina le galuega leiloa.
# Define the loss function
loss = tf.losses.mean_squared_error(y_true, y_pred)
# Compile the model
model.compile(optimizer='adam', loss=loss)
# use diverse dataset
from sklearn.utils import shuffle
X_train, y_train = shuffle(X_train, y_train)
Ma le mea mulimuli, e mafai ona e faʻataʻitaʻi i isi fausaga ma metotia. O lea, e mafai ona e siitia le faatinoga o le faataitaiga, e pei o faiga fa'alogo, GAN, po'o VAE.
Tuua se tali