Uninzi lwethu luqhelene neejeneretha zemifanekiso ye-AI njenge Ukusasazwa okuZinzile. Sele itshintshile ishishini kwaye ifakwe kubomi bethu.
Nangona kunjalo, iimodeli zeStable Diffusion zininzi kakhulu kunokuvelisa umfanekiso.
Zininzi iindawo esinokubaqesha kuzo.
Iimodeli eziZinzileyo zeDiffusion ziimodeli zemathematika. Kwaye, banokukunceda ukuba uphande amandla okutshintsha iinkqubo ngokuhamba kwexesha.
Zisekelwe kwiingcamango zenkqubo yokusabalalisa. Ke, unokujonga uluhlu olubanzi lweziganeko. Umzekelo; ukuhanjiswa kobushushu, ukusabela kweekhemikhali, kunye nokusasazwa kolwazi kwiimarike zemali.
Ezi modeli ziguquguquka kakhulu. Ke, unokuqikelela imeko yexesha elizayo yenkqubo esekwe kwimeko yayo yangoku.
Ngaphandle koko, unokubona imigaqo esisiseko yomzimba okanye yezemali eyilawulayo. Le ngcamango ibe luncedo kakhulu kwiindawo ezininzi. Ezi ziquka i-physics, ikhemistri, kunye nezezimali.
Yiyo loo nto sifuna ukuyiphanda ngakumbi. Kwaye, sifuna ukukunika isifundo malunga nendlela yokuqeqesha ezi modeli zeDiffusion eZinzile.
Zenzeka Njani Iimodeli Zokusasaza Ezizinzileyo?
Oku kuneengcambu emva ekupheleni kwenkulungwane ye-19.
Uphando lwemathematika lweenkqubo zokusasazwa kwimiba kulapho iimodeli zeStable Diffusion zaqala khona. Enye yeemodeli ezidumileyo zeStable Diffusion yiFokker-Planck equation.
Yaboniswa okokuqala ngo-1906. Le mifuziselo iye yavela kwaye yatshintshwa ngokuhamba kwexesha. Ke, ngoku sizisebenzisa kumashishini ahlukeneyo.
Yintoni Ingqiqo Ngasemva Kwayo?
Ngamagama alula, njengoko sitshilo, ziyimodeli yemathematika. Ngaphandle koko, zisinceda ukuba siphande ukuba ipropathi okanye ubungakanani busasazeka njani ngokuhamba kwexesha kwinkqubo.
Zisekelwe kwimigaqo yenkqubo yokusabalalisa. Ke, ziyasinceda ukuba siphande ukuba isixa sisasazeka njani na kwinkqubo. Oku kusasazeka kungumphumo wokuhluka koxinzelelo, uxinzelelo, okanye ezinye iiparamitha.
Makhe sinike umzekelo olula. Khawucinge ukuba unesikhongozeli esizele lulwelo apho wongeze idayi. Ukusasazwa kubonakala apha xa idayi iqala ukusasazeka kwaye i-emulsify kulwelo. Ngokusekwe kwiimpawu zolwelo kunye nedayi, iimodeli zeStable Diffusion zinokusetyenziselwa ukuqikelela ukuba idayi iya kusasazwa njani kwaye ixube ngokuhamba kwexesha.
Kwiinkqubo ezintsonkothileyo, ezifana neemarike zezimali okanye ukuphendulwa kweekhemikhali, le mizekelo inokuqikelela ukuba ulwazi okanye iimpawu ziya kusasazwa njani kwaye zibe nefuthe kwinkqubo ngokuhamba kwexesha. Ngaphandle koko, idatha enkulu inokuqhelana nayo qeqesha le mizekelo ukwenza uqikelelo oluchanekileyo. Zakhiwe kusetyenziswa iifomula zemathematika ezichaza inkqubo yendaleko yexesha elide.
Ukuqonda kunye nokuqikelela ukusasazwa kweempawu ezithile kwinkqubo ngokuhamba kwexesha yeyona ngcamango iphambili ephantsi kwale mizekelo. Kubalulekile ukukhumbula ukuba iingcali kwiinkalo ezizodwa zidla ngokuqesha le modeli.
Uziqeqesha njani iiModeli?
Qokelela kwaye ulungiselele idatha yakho:
Kuya kufuneka uqale uqokelele kwaye ulungiselele idatha yakho ngaphambi kokuba uqalise ukuqeqesha imodeli yakho. Idatha yakho inokufuna ukucocwa kwaye ifomathwe. Kwakhona, amanani angekhoyo nawo anokufuna ukususwa.
Khetha imodeli yoyilo
Iimodeli zeDiffusion eziZinzile ziza ngeendlela ezahlukeneyo. Ikakhulu isekwe kwi-equation ye-Fokker-Planck, i-Schrödinger equation, kunye ne-Master equation. Imodeli ehambelana nemeko yakho ethile kufuneka ikhethwe. Ke, nganye kwezi modeli ineengenelo kunye nokungonakali.
Ukuseka umsebenzi wakho welahleko
Kubalulekile kuba ichaphazela indlela imodeli yakho inokuhambelana ngayo nedatha. Kwimifuziselo yeDiffusion eZinzile, impazamo ephakathi kwesikwere kunye ne-Kullback-Leibler divergence yimisebenzi yokulahleka rhoqo.
Qeqesha imodeli yakho
Ukusebenzisa i-stochastic gradient descent okanye indlela efanayo yokuphucula, unokuqalisa ukuqeqesha imodeli yakho emva kokuchaza umsebenzi wakho welahleko.
Vavanya ukuba imodeli yakho ilungelelaniswe ngokubanzi
Kuya kufuneka ujonge idatha entsha emva koqeqesho ngokuyithelekisa neseti yovavanyo lwedatha.
Lungelelanisa iihyperparameters zemodeli yakho
Ukuphucula ukusebenza kwemodeli yakho, linge ngamaxabiso ahlukeneyo eehyperparameters ezifana nezinga lokufunda, ubungakanani bebhetshi, kunye nenani leeleya ezifihliweyo kwinethiwekhi.
Phinda izenzo zangaphambili
Kusenokufuneka uphinde ezi nkqubo ngaphezu kwesinye ukuze ufumane ezona ziphumo zibalaseleyo. Kuya kuxhomekeka kubunzima bengxaki kunye nomgangatho wedatha.
IKhowudi yokufundisa
Iilwimi zoLwimi njengePython, MATLAB, C++, kunye no-R zonke zinokusetyenziselwa ukwenza imifuziselo yoDiffusion eZinzile. Ulwimi olusetyenzisiweyo luya kuxhomekeka kwisicelo esithile. Kwakhona, inokuxhomekeka kwizixhobo namathala eencwadi enziwe afumaneke kolo lwimi.
I-Python lolona khetho lungcono kule meko. Inamathala eencwadi awomeleleyo afana neNumPy kunye neSciPy yokubala amanani. Kwakhona, ixhasa iTensorFlow kunye I-PyTorch ekudaleni nasekuqeqesheni uthungelwano lwe-neural. Ke ngoko, iba lukhetho olukhulu lokubhala iimodeli zeDiffusion eZinzile.
Umzekelo:
Masisebenzise i-diffusion equation, ifomyula yemathematika echaza indlela umgangatho okanye ubungakanani, obufana nobushushu okanye ukuhlangana kwento, etshintsha ngayo ekuhambeni kwexesha kwisixokelelwano. Inxaki ngokubanzi ibonakala ngolu hlobo:
∂u/∂t = α ∇²u
I-diffusion coefficient () ngumlinganiselo wendlela ipropati okanye ubungakanani obusasazeka ngayo kwisixokelelwano.
ILaplacian ye-u (2u) yinkcazo yendlela ipropati okanye ubungakanani obutshintsha ngayo ngokumalunga nendawo. Apho u-uyipropati okanye ubungakanani obusasazwayo (umzekelo, ubushushu okanye ugxininiso), t kukuhamba kwexesha, ngumlinganiso wokusasazwa, kwaye lutshintsho oluqhubekayo ().
Sinokuyisebenzisa ngokusebenzisa indlela ye-Euler kwiPython.
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
Le khowudi isebenzisa ubuchule be-Euler ukuphumeza i-diffusion equation. Ichaza imo yokuqala njengemeko yokuqala efanayo emelwe luluhlu lwazo ezinemilo ye (100). I-0.01 isetyenziswe njengenyathelo lexesha.
Uphindaphindo lwe-1000 lwelophu yokunyathela ixesha lugqityiwe.
Isebenzisa umsebenzi we-np.diff, omisela umahluko phakathi kwezinto ezingabamelwane. Kungoko, ibala i-derivative yendawo yepropati okanye ubungakanani obusasazwayo. Kwaye, imelwe ngu-du, kwi-iteration nganye.
Emva koko siphinda-phinda i-derivative yendawo nge-alpha yomlinganiso wokusasazwa kunye nenyathelo lexesha lokuhlaziya ixabiso lika-u.
Umzekelo Onzima ngakumbi
Inokuthini imodeli yosasazo ezinzileyo elinganisa kuphela usasazo lobushushu oluzinzileyo lujongeka njani? Isebenza njani loo khowudi?
Ukusonjululwa kweseti ye-partial differential equations (PDEs) echaza ukuba ubushushu bunwenwa njani kwisixokelelwano ngokuhamba kwexesha kuyimfuneko. Ke, sinokuqeqesha imodeli yoDiffusion eZinzileyo ephindaphinda ukusasazwa kobushushu.
Nanku umzekeliso wendlela i-equation yobushushu, i-PDE echaza i-Stable Diffusion yobushushu kwintonga ene-dimensional enye, inokusombulula kusetyenziswa indlela yomahluko ogqityiweyo:
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()
Sisebenza Njani Imveliso Yomfanekiso ukusuka kwiTeksti?
Kuba ithandwa kakhulu kwi-intanethi, sinokujonga ukuba ukuveliswa kwemifanekiso kusebenza njani.
Iindlela zokulungisa ulwimi lwendalo (NLP) kunye amanethiwekhi. Kwaye, zihlala zisetyenziselwa ukubonelela ngemodeli yeDiffusion eZinzileyo yokuguqula umbhalo ukuya kumfanekiso. Inkcazo ebanzi yendlela yokuyifeza inikwe ngezantsi:
1- Yenza umqondiso wamagama kwidatha yokubhaliweyo, kwaye ushenxise amagama amileyo kunye neziphumlisi. Guqula amagama abe ngamanani amanani. Yinxalenye yokusetyenzwa kwangaphambili (ufakelo lwamagama).
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- Funda indlela yokunxulumana okubhaliweyo kunye nemifanekiso usebenzisa inethiwekhi ye-neural edibanisa i-encoder kunye ne-decoder. Inethiwekhi yedekhowuda ifumana ikhowudi efihlakeleyo njengegalelo. Emva koko, yenza umfanekiso ohambelanayo emva kokuba inethiwekhi ye-encoder iguqule idatha yokubhaliweyo ibe ngumboniso odibeneyo (ikhowudi efihlakeleyo).
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- Ngokubonelela ngengqokelela enkulu yemifanekiso kunye neenkcazo zetekisi ezihamba nazo. Emva koko, ungaqeqesha inethiwekhi ye-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- Emva kokuba inethwekhi iqeqeshiwe, ungayisebenzisa ukuvelisa imifanekiso evela kumagama amatsha abhaliweyo. Kwaye, kungondla okubhaliweyo kwi-encoder network. Emva koko, unokuvelisa ikhowudi efihlakeleyo, kwaye emva koko wondle ikhowudi efihlakeleyo kwinethiwekhi ye-decoder ukuvelisa umfanekiso ohambelanayo.
# Encode the text input
latent_code = encoder.predict(text)
# Generate an image from the latent code
image = decoder.predict(latent_code)
I-5-Ukukhethwa kwedatha efanelekileyo kunye nemisebenzi yelahleko yenye yamanyathelo abaluleke kakhulu. I-dataset iyahluka kwaye iqulethe uluhlu olubanzi lwemifanekiso kunye neenkcazo zetekisi. Sifuna ukuqiniseka ukuba imifanekiso iyinyani. Kwakhona, kufuneka siqiniseke ukuba iinkcazo zetekisi zinokwenzeka ukuze sikwazi ukuyila umsebenzi welahleko.
# 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)
Okokugqibela, ungazama nezinye iindlela zokwakha kunye neendlela. Ke, ukuba unokuphakamisa ukusebenza kwemodeli, njenge iindlela zokuqwalaselwa, GANs, okanye VAEs.
Shiya iMpendulo