Ọtụtụ n'ime anyị maara ndị na-emepụta ihe oyiyi AI dị ka Mgbasa kwụsiri ike. Ọ agbanweela ụlọ ọrụ ahụ ma tinye ya na ndụ anyị.
Agbanyeghị, ụdị Stable Diffusion dị ukwuu karịa ọgbọ onyonyo.
Enwere ọtụtụ ebe anyị nwere ike were ha n'ọrụ.
Ụdị mgbasa ozi kwụsiri ike bụ ụdị mgbakọ na mwepụ. Na, ha nwere ike inyere gị aka inyocha mgbanwe nke usoro mgbanwe ka oge na-aga.
Ha dabere na echiche usoro mgbasa ozi. N'ihi ya, ị nwere ike nyochaa ọtụtụ phenomena. Ọmụmaatụ; nnyefe okpomọkụ, mmeghachi omume kemịkalụ, na mgbasa ozi na ahịa ego.
Ụdị ndị a nwere ike ime mgbanwe nke ukwuu. Yabụ, ị nwere ike ịtụ anya ọnọdụ ọdịnihu nke usoro dabere na ọnọdụ ya ugbu a.
E wezụga nke ahụ, ị nwere ike ịhụ ụkpụrụ anụ ahụ ma ọ bụ nke ego na-achịkwa ya. Echiche a bara uru nke ukwuu n'ọtụtụ mpaghara. Ndị a gụnyere physics, kemistri, na ego.
Nke a bụ ihe mere anyị ji chọọ inyochakwu ya. Ma, anyị chọrọ inye gị nkuzi ka esi azụ ụdị Stable Diffusion ndị a.
Kedu ka Model Diffusion Stable siri bịa?
Nke a malitere na njedebe narị afọ nke 19.
Nnyocha mgbakọ na mwepụ nke usoro mgbasa ozi n'okwu bụ ebe ụdị Stable Diffusion malitere ha. Otu n'ime ụdị Stable Diffusion kacha ewu ewu bụ nha Fokker-Planck.
E gosipụtara ya na mbụ na 1906. Ụdị ndị a etolitela ma gbanwee site na oge. N'ihi ya, anyị na-eji ha eme ihe n'ụdị dị iche iche.
Gịnị bụ ezi uche n'azụ ya?
N'okwu dị mfe, dịka anyị kwuru, ha bụ ụdị mgbakọ na mwepụ. E wezụga nke ahụ, ha na-enyere anyị aka inyocha ka ihe onwunwe ma ọ bụ ọnụ ọgụgụ si agbasa ka oge na-aga na sistemụ.
Ha dabere na ụkpụrụ usoro mgbasa ozi. Yabụ, ha na-enyere anyị aka inyocha ka ọnụọgụgụ si agbasa n'ofe sistemụ. Mgbasa a bụ n'ihi mgbanwe dị iche iche na ntinye uche, nrụgide, ma ọ bụ parampat ndị ọzọ.
Ka anyị nye ihe atụ dị mfe. Were ya na ị nwere akpa nke mmiri juru na ya nke ị gbakwunyere agba. A na-ahụ mgbasa ozi ebe a mgbe agba na-amalite ịgbasa na emulsify na mmiri mmiri. Dabere na njirimara nke mmiri mmiri na esiji, enwere ike iji ụdị Stable Diffusion buru amụma ka agbachasị ahụ ga-esi gbasa ma gwakọta ka oge na-aga.
Na usoro mgbagwoju anya, dị ka ahịa ego ma ọ bụ mmeghachi omume kemịkalụ, ụdị ndị a nwere ike ịkọ otú ozi ma ọ bụ àgwà ga-esi gbasaa ma metụta usoro ahụ ka oge na-aga. E wezụga nke ahụ, nnukwu data nwere ike mara ya zụọ ụdị ndị a ime amụma ziri ezi. Ewubere ha site na iji usoro mgbakọ na mwepụ nke na-akọwa ngbanwe usoro ogologo oge.
Ịghọta na ịkọ mgbasawanye nke ụfọdụ àgwà na usoro site na oge bụ isi echiche na-adabere na ụdị ndị a. Ọ dị mkpa icheta na ndị ọkachamara na ngalaba pụrụ iche na-ejikarị ụdị ndị a.
Olee otú ịzụ Model?
Chịkọta ma dozie data gị:
Ị ga-ebu ụzọ kpọkọta ma kwadebe data gị tupu ịmalite ịzụ ihe nlereanya gị. Ọ nwere ike ịdị mkpa ka ihicha ma hazie data gị. Ọzọkwa, ọnụọgụ ndị na-efu nwekwara ike ịdị mkpa ka ekpochapụ.
Họrọ ụkpụrụ ụkpụrụ ụlọ
Ụdị Diffusion Stable na-abịa n'ụdị dị iche iche. Ọ dabere na nha Fokker-Planck, nha nha Schrödinger, na nhata Master. A ga-ahọrọrịrị ụdị nke dabara na ọnọdụ gị. Ya mere, onye ọ bụla n'ime ụdị ndị a nwere uru na ọghọm.
Ịmepụta ọrụ ọnwụ gị
Ọ dị mkpa ebe ọ na-emetụta otú ihe nlereanya gị nwere ike isi kwekọọ na data ahụ. Maka ụdị Stable Diffusion, njehie pụtara squared na iche Kullback-Leibler bụ ọrụ mfu ugboro ugboro.
Zụlite ihe nlereanya gị
N'iji mgbada gradient stochastic ma ọ bụ usoro nkwalite yiri ya, ị nwere ike ịmalite ịzụ ihe nlereanya gị mgbe ịkọwapụta ọrụ mfu gị.
Nyochaa n'ozuzu nke ihe nlereanya gị
Ị kwesịrị ịlele data ọhụrụ mgbe ọzụzụ gasịrị site n'iji ya tụnyere ihe nlele data.
Megharịa hyperparameters ụdị gị
Iji kwalite arụmọrụ nke ihe nlereanya gị, nwalee ụkpụrụ dị iche iche nke hyperparameters dị ka ọnụego mmụta, nha batch na ọnụọgụ nke ezoro ezo na netwọk.
Tinyegharịa omume ndị gara aga
Ị nwere ike ịmegharị usoro ndị a ihe karịrị otu ugboro ka ị nweta nsonaazụ kacha mma. Ọ ga-adabere na ike nke nsogbu na caliber nke data.
Nkuzi itinye akwụkwọ
Asụsụ mmemme dị ka Python, MATLAB, C++, na R ka enwere ike iji mepụta ụdị Stable Diffusion. Asụsụ a na-eji ga-adabere na ngwa ahụ. Ọzọkwa, ọ nwere ike dabere na ngwa ọrụ na ọba akwụkwọ emere maka asụsụ ahụ.
Python bụ nhọrọ kacha mma na nke a. Ọ nwere ọba akwụkwọ siri ike dị ka NumPy na SciPy maka ngụkọ ọnụọgụgụ. Ọzọkwa, ọ na-akwado TensorFlow na PyTorch maka ịmepụta na ọzụzụ netwọk neural. N'ihi ya, ọ na-aghọ nnukwu nhọrọ maka ide ụdị Stable Diffusion.
Ihe Nlereanya:
Ka anyị jiri nha nhata mgbasa ozi, usoro mgbakọ na mwepụ nke na-akọwa otú àgwà ma ọ bụ ọnụọgụgụ, dị ka okpomọkụ ma ọ bụ ntinye nke ihe si agbanwe ka oge na-aga na usoro. Nha nhata n'ozuzu dị ka nke a:
∂u/∂t = α∇²u
Ọnụọgụ mgbasa () bụ nleba anya ka ihe onwunwe ma ọ bụ ọnụọgụgụ si agbasa ngwa ngwa site na sistemụ.
Laplacian nke u (2u) bụ nkọwa nke otu ihe onwunwe ma ọ bụ ọnụọgụ na-agbanwe n'ihe gbasara oghere. Ebe ị bụ ihe onwunwe ma ọ bụ ọnụ ọgụgụ ka a na-agbasa (dịka ọmụmaatụ, okpomọkụ ma ọ bụ itinye uche), t bụ oge nke oge, bụ ọnụọgụ mgbasa ozi, ọ bụkwa mgbasa ozi na-adịgide adịgide ().
Anyị nwere ike mejuputa ya site na iji usoro Euler na 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
Koodu a na-eji usoro Euler mejuputa nha nhata mgbasa. Ọ na-akọwa ọnọdụ mmalite dị ka otu ụdị mmalite ọnọdụ nke ọtụtụ ndị nwere udi (100) nọchiri anya ya. A na-eji 0.01 mee ihe dị ka usoro oge.
1000 itier nke oge nzọpụ ụkwụ akaghị.
Ọ na-eji ọrụ np.diff, nke na-ekpebi ọdịiche dị n'etiti ihe ndị agbata obi. N'ihi ya, ọ na-agbakọ mpụta oghere nke akụrụngwa ma ọ bụ ọnụọgụ a na-agbasa. Na, du na-anọchi anya ya, na nke ọ bụla iteration.
Mgbe ahụ, anyị na-amụba mbipute oghere site na ọnụọgụ mgbasa ozi alfa yana usoro oge iji melite uru nke gị.
Ihe Nlereanya Mgbagwoju Anya Karia
Kedu ihe ụdị mgbasa ozi kwụsiri ike nke na-atụ naanị mgbasa ọkụ kwụsiri ike ga-adị ka? Kedu ka koodu ahụ si arụ ọrụ?
Ịdozi usoro nha anya dị iche iche (PDEs) nke na-akọwa otú okpomọkụ si agbasa n'ofe sistemu ka oge na-aga dị mkpa. Yabụ, anyị nwere ike zụọ ụdị Stable Diffusion nke na-emegharị mgbasawanye nke okpomọkụ.
Nke a bụ ihe atụ nke otu nha nha ọkụ, PDE nke na-akọwa Stable Diffusion nke okpomọkụ na mkpanaka otu akụkụ, nwere ike isi dozie ya site na iji usoro ọdịiche dị oke:
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()
Kedu ka imepụta ihe onyonyo si na ederede si arụ ọrụ?
Ebe ọ bụ na ọ na-ewu ewu na ịntanetị, anyị nwere ike ịlele ka mmepụta ihe oyiyi si arụ ọrụ nke ọma.
Usoro nhazi asụsụ eke (NLP) na neural netwọk. Na, a na-ejikarị ha weta ihe atụ Stable Diffusion maka ntụgharị ederede gaa na onyonyo. Enyere nkọwa sara mbara nke otu esi eme ya n'okpuru:
1- Tokenize okwu ndị dị na data ederede, ma kpochapụ okwu nkwụsị na akara edemede. Tụgharịa mkpụrụokwu ka ọ bụrụ ụkpụrụ ọnụọgụgụ. Ọ bụ akụkụ nke nhazi (mgbakwunye okwu).
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- Mụta ka esi ejikọta ederede na onyonyo site na iji netwọk neural nke na-ejikọta koodu nzuzo na ihe ngbanwe. Netwọk ihe ndozi na-enweta koodu nzuzo dị ka ntinye. Mgbe ahụ, ọ na-emepụta foto ejikọtara mgbe netwọk nzuzo tụgharịrị data ederede ka ọ bụrụ kọmpat nnọchiteanya (koodu latent).
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- Site n'inye ya nnukwu mkpokọta onyonyo yana nkọwa ederede na-eso ha. Mgbe ahụ, ị nwere ike ịzụ netwọk encoder-decoder.
# 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- Mgbe emechara netwọk ahụ, ịnwere ike iji ya mepụta foto sitere na ntinye ederede ọhụrụ. Ma, ọ bụ site n'inye ederede n'ime netwọk nzuzo. Mgbe ahụ, ị nwere ike ịmepụta koodu latent, wee zụọ koodu latent n'ime netwọk decoder iji mepụta foto ejikọta ya.
# Encode the text input
latent_code = encoder.predict(text)
# Generate an image from the latent code
image = decoder.predict(latent_code)
5-Nhọrọ nke kwesịrị ekwesị dataset na ọnwụ ọrụ bụ otu n'ime ndị kasị oké mkpa nzọụkwụ. Nhazi data dị iche iche ma nwee ọtụtụ foto na nkọwa ederede. Anyị chọrọ ijide n'aka na ihe oyiyi ndị a bụ eziokwu. Ọzọkwa, anyị kwesịrị ijide n'aka na nkọwa ederede ga-ekwe omume ka anyị nwee ike chepụta ọrụ ọnwụ.
# 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)
N'ikpeazụ, ị nwere ike ịnwale usoro nhazi na usoro ndị ọzọ. Yabụ, na ị nwere ike ibuli arụmọrụ ihe nlereanya ahụ, dịka usoro nlebara anya, GAN, ma ọ bụ VAEs.
Nkume a-aza