ʻO ka hapa nui o mākou e kamaʻāina me nā mea hana kiʻi AI like Hoʻolaha Paʻa. Ua hoʻololi mua ia i ka ʻoihana a ua hoʻokomo ʻia i loko o ko mākou ola.
Eia naʻe, ʻoi aku ka nui o nā hiʻohiʻona Stable Diffusion ma mua o ka hana kiʻi.
Nui nā wahi e hiki ai iā mākou ke hoʻohana iā lākou.
ʻO nā kumu hoʻohālike Stable Diffusion he mau hiʻohiʻona makemakika. A, hiki iā lākou ke kōkua iā ʻoe e noiʻi i ka dinamika o nā ʻōnaehana hoʻololi i ka manawa.
Hoʻokumu ʻia lākou ma nā manaʻo kaʻina hana diffusion. No laila, hiki iā ʻoe ke noʻonoʻo i kahi ākea o nā phenomena. ʻo kahi laʻana; ka hoʻoili ʻana i ka wela, ka hoʻoheheʻe kemika, a me ka hoʻolaha ʻana i ka ʻike ma nā mākeke kālā.
Hiki ke hoʻololi ʻia kēia mau hiʻohiʻona. No laila, hiki iā ʻoe ke manaʻo i ke kūlana e hiki mai ana o kahi ʻōnaehana e pili ana i kona kūlana o kēia manawa.
Ma waho aʻe, hiki iā ʻoe ke ʻike i ke kumu o ke kino a i ʻole nā kumukūʻai kālā e hoʻokele ai. Ua kōkua nui kēia manaʻo ma nā wahi he nui. Hoʻopili kēia i ka physics, chemistry, a me ke kālā.
ʻO kēia ke kumu makemake mākou e noiʻi hou aku. A, makemake mākou e hāʻawi iā ʻoe i kahi aʻo e pili ana i ke aʻo ʻana i kēia mau hiʻohiʻona Stable Diffusion.
Pehea i hiki mai ai nā kumu hoʻohālike Stable Diffusion?
He kumu kēia i ka hopena o ke kenekulia 19.
ʻO ka noiʻi makemakika o nā kaʻina diffusion i nā mea kahi i hoʻomaka ai nā kumu hoʻohālike Stable Diffusion. ʻO kekahi o nā hiʻohiʻona Stable Diffusion kaulana loa ʻo ia ka hoʻohālikelike Fokker-Planck.
Ua hōʻike mua ʻia i ka makahiki 1906. Ua ulu a hoʻololi ʻia kēia mau hiʻohiʻona i ka manawa. No laila, hoʻohana mākou iā lākou i nā ʻano ʻoihana like ʻole.
He aha ka Logic ma hope o ia?
Ma nā ʻōlelo maʻalahi, e like me kā mākou i ʻōlelo ai, he mau hiʻohiʻona makemakika lākou. Ma waho aʻe, kōkua lākou iā mākou e noiʻi i ka laha ʻana o kahi waiwai a i ʻole ka nui i ka manawa i loko o kahi ʻōnaehana.
Hoʻokumu ʻia lākou ma nā loina kaʻina hana diffusion. No laila, kōkua lākou iā mākou e noiʻi pehea e laha ai ka nui ma waena o kahi ʻōnaehana. ʻO kēia hoʻolaha ʻana ma muli o nā ʻano like ʻole o ka noʻonoʻo, kaomi, a i ʻole nā ʻāpana ʻē aʻe.
E hāʻawi mākou i kahi laʻana maʻalahi. E noʻonoʻo ʻoe he pahu piha i ka wai āu i hoʻohui ai i kahi mea pena. ʻIke ʻia ka diffusion ma ʻaneʻi i ka wā e hoʻomaka ai ka hoʻoheheʻe ʻana a emulsify i loko o ka wai. Ma muli o nā hiʻohiʻona o ka wai a me ka wai, hiki ke hoʻohana ʻia nā kumu hoʻohālike Stable Diffusion e wānana i ke ʻano o ka laha a hui ʻana o ka wai i ka manawa.
Ma nā ʻōnaehana paʻakikī, e like me nā mākeke kālā a i ʻole ka hopena kemika, hiki i kēia mau hiʻohiʻona ke wānana i ka laha ʻana o ka ʻike a i ʻole nā hiʻohiʻona a hopena i ka ʻōnaehana i ka manawa. Ma waho aʻe, hiki ke hoʻohana ʻia ka ʻikepili nui e aʻo i kēia mau hiʻohiʻona e hana wanana pololei. Hoʻokumu ʻia lākou me ka hoʻohana ʻana i nā papa helu makemakika e wehewehe ana i ka hoʻomohala lōʻihi o ka ʻōnaehana.
ʻO ka hoʻomaopopo ʻana a me ka wānana ʻana i ka hoʻolaha ʻana o kekahi mau ʻano i loko o kahi ʻōnaehana ma o ka manawa ka manaʻo nui i lalo o kēia mau hiʻohiʻona. He mea nui e hoʻomanaʻo i ka hoʻohana maʻamau o ka poʻe akamai i nā kula kūikawā i kēia mau hiʻohiʻona.
Pehea e aʻo ai i nā mea hoʻohālike?
E hōʻiliʻili a hoʻomākaukau i kāu ʻikepili:
Pono ʻoe e hōʻiliʻili a hoʻomākaukau i kāu ʻikepili ma mua o kou hoʻomaka ʻana e aʻo i kāu kumu hoʻohālike. Pono paha kāu ʻikepili e hoʻomaʻemaʻe a hoʻopili ʻia. Eia kekahi, pono e hoʻopau ʻia nā helu i nalowale.
E koho i kahi hoʻolālā hoʻohālike
Hiki mai nā ʻano hoʻohālike Stable Diffusion i nā ʻano like ʻole. Hoʻokumu ʻia ka nui ma ka hoʻohālikelike Fokker-Planck, ka hoohalike Schrödinger, a me ka hoohalike Master. Pono e koho ʻia ke kumu hoʻohālike i kūpono i kou kūlana. No laila, loaʻa i kēlā me kēia o kēia mau hiʻohiʻona nā pono a me nā hemahema.
Ke hoʻokumu nei i kāu hana poho
He mea nui ia no ka mea e pili ana i ka maikaʻi o kāu kumu hoʻohālike e hoʻohālikelike ai i ka ʻikepili. No nā hiʻohiʻona Stable Diffusion, ʻo ka mean squared error a me ka Kullback-Leibler divergence he mau hana poho pinepine.
E aʻo i kāu kumu hoʻohālike
Ke hoʻohana nei i ka stochastic gradient descent a i ʻole kahi ala hoʻohālikelike like, hiki iā ʻoe ke hoʻomaka i ke aʻo ʻana i kāu kumu hoʻohālike ma hope o ka wehewehe ʻana i kāu hana poho.
E noʻonoʻo i ka laulā o kāu kumu hoʻohālike
Pono ʻoe e nānā i ka ʻikepili hou ma hope o ka hoʻomaʻamaʻa ʻana ma ka hoʻohālikelike ʻana me kahi pūʻulu hoʻāʻo o ka ʻikepili.
Hoʻoponopono i nā hyperparameter o kāu kumu hoʻohālike
No ka hoʻomaikaʻi ʻana i ka hana o kāu kumu hoʻohālike, e hoʻāʻo me nā waiwai like ʻole o nā hyperparameters e like me ka helu aʻo, ka nui o ka pūʻulu, a me ka helu o nā papa huna i ka pūnaewele.
E hana hou i na hana mua
Pono paha ʻoe e hana hou i kēia mau kaʻina hana ma mua o hoʻokahi manawa e loaʻa ai nā hopena maikaʻi loa. E pili ana ia i ka paʻakikī o ka pilikia a me ka caliber o ka ʻikepili.
Aʻoaʻo Coding
Nā'ōlelo hoʻolālā e like me Python, MATLAB, C++, a me R hiki ke hoʻohana ʻia e hana i nā hiʻohiʻona Stable Diffusion. ʻO ka ʻōlelo i hoʻohana ʻia e hilinaʻi ʻia i ka noi kikoʻī. Eia kekahi, hiki ke hilinaʻi i nā mea hana a me nā hale waihona puke i loaʻa no kēlā ʻōlelo.
ʻO Python ka koho maikaʻi loa i kēia hihia. Loaʻa iā ia nā hale waihona puke e like me NumPy a me SciPy no ka helu helu. Eia kekahi, kākoʻo ia iā TensorFlow a ʻO PyTorch no ka hana ʻana a me ke aʻo ʻana i nā ʻupena neural. No laila, lilo ia i koho maikaʻi loa no ke kākau ʻana i nā hiʻohiʻona Stable Diffusion.
la'ana:
E hoʻohana kākou i ka diffusion equation, he ʻano makemakika e wehewehe ana i ka hoʻololi ʻana o ka maikaʻi a i ʻole ka nui, e like me ka wela a i ʻole ka neʻe ʻana o kekahi mea i ka manawa i loko o kahi ʻōnaehana. Penei ka hoohalike ana:
∂u/∂t = α ∇²u
ʻO ka diffusion coefficient () kahi ana o ka maʻalahi o ka laha ʻana o kahi waiwai a i ʻole ka nui ma o kahi ʻōnaehana.
ʻO ka Laplacian o u (2u) ka wehewehe ʻana i ka loli ʻana o ka waiwai a i ʻole ka nui e pili ana i ka lewa. Inā ʻo u ka waiwai a i ʻole ka nui e hoʻopuehu ʻia nei (e laʻana, ka wela a i ʻole ka hoʻopaʻa ʻana), ʻo ia ka hele ʻana o ka manawa, ʻo ia ka diffusion coefficient, a ʻo ia ka diffusion mau ().
Hiki iā mākou ke hoʻokō me ka hoʻohana ʻana i ke ʻano Euler ma 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
Hoʻohana kēia code i ka ʻenehana Euler no ka hoʻokō ʻana i ka hoohalike diffusion. Hōʻike ia i ke kūlana hoʻomaka ma ke ʻano he kūlana mua like ʻole i hōʻike ʻia e kahi ʻano o nā mea me ke ʻano o (100). Hoʻohana ʻia ʻo 0.01 e like me ke kaʻina manawa.
Ua pau ka 1000 iterations o ka loop-stepping loop.
Hoʻohana ia i ka hana np.diff, kahi e hoʻoholo ai i ka ʻokoʻa ma waena o nā mea pili. No laila, helu ia i ka derivative spatial o ka waiwai a i ʻole ka nui e laha ʻia. A, ua hōʻike ʻia e du, i kēlā me kēia hoʻololi.
A laila hoʻonui mākou i ka derivative spatial me ka diffusion coefficient alpha a me ka manawa manawa e hoʻonui ai i ka waiwai o u.
He Laʻana Paʻakikī
He aha ke ʻano o ka hoʻopulapula paʻa e ana wale ana i ka hoʻopuehu wela paʻa? Pehea ka hana o ia code?
E hoʻoholo i kahi pūʻulu o nā haʻihaʻi ʻokoʻa hapa (PDE) e wehewehe ana i ka laha ʻana o ka wela ma kahi ʻōnaehana i ka manawa. No laila, hiki iā mākou ke hoʻomaʻamaʻa i kahi kumu hoʻohālike Stable Diffusion e hoʻopili i ka hoʻopuehu mau o ka wela.
Eia kekahi hiʻohiʻona o ka hoʻoponopono ʻana i ka hoʻohālikelike wela, he PDE e wehewehe ana i ka Stable Diffusion o ka wela i loko o kahi koʻokoʻo ʻāpana hoʻokahi, me ka hoʻohana ʻana i ke ʻano ʻokoʻa palena.
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()
Pehea ka hana ʻana o nā kiʻi mai ke kikokikona?
No ka mea kaulana loa ia ma ka pūnaewele, hiki iā mākou ke nānā i ke ʻano o ka hana ʻana o nā kiʻi.
Nā ʻano hana ʻōlelo kūlohelohe (NLP) a nā hanana laulā. A, hoʻohana pinepine ʻia lākou e hāʻawi i kahi kumu hoʻohālike Stable Diffusion no ka hoʻololi ʻana i ke kikokikona i ke kiʻi. ʻO kahi wehewehe ākea o ka hana ʻana i hāʻawi ʻia ma lalo nei:
1- Hoʻokaʻawale i nā huaʻōlelo ma ka ʻikepili kikokikona, a kāpae i nā huaʻōlelo hoʻomaha a me nā kaha kiko. E hoʻololi i nā huaʻōlelo i nā waiwai helu. He ʻāpana ia o ka preprocessing (huaʻōlelo embeddings).
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- E aʻo pehea e hoʻopili ai i ka kikokikona a me nā kiʻi me ka hoʻohana ʻana i kahi neural network e hoʻohui i kahi encoder a me kahi decoder. Loaʻa i ka pūnaewele decoder ke code huna ma ke ʻano he hoʻokomo. A laila, hana ia i ke kiʻi pili ma hope o ka hoʻololi ʻana o ka pūnaewele encoder i ka ʻikepili kikokikona i kahi hōʻike paʻa (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- Ma ka hāʻawi ʻana iā ia me kahi hōʻiliʻili nui o nā kiʻi a me nā wehewehe kikokikona e hele pū me lākou. A laila, hiki iā ʻoe ke hoʻomaʻamaʻa i ka pūnaewele 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- Ma hope o ka hoʻomaʻamaʻa ʻana o ka pūnaewele, hiki iā ʻoe ke hoʻohana iā ia e hana i nā kiʻi mai nā hoʻokomo kikokikona hou. A, ʻo ia ma ka hānai ʻana i ka kikokikona i loko o ka pūnaewele encoder. A laila, hiki iā ʻoe ke hana i kahi code latent, a laila hānai i ke code latent i loko o ka pūnaewele decoder e hana i ke kiʻi pili.
# Encode the text input
latent_code = encoder.predict(text)
# Generate an image from the latent code
image = decoder.predict(latent_code)
5-ʻO ke koho ʻana i ka ʻikepili kūpono a me nā hana poho kekahi o nā hana koʻikoʻi. He ʻokoʻa ka waihona a loaʻa i nā kiʻi a me nā wehewehe kikokikona. Makemake mākou e hōʻoia i ka ʻoiaʻiʻo o nā kiʻi. Eia kekahi, pono mākou e hōʻoia i hiki ke hiki i nā wehewehe kikokikona i hiki iā mākou ke hoʻolālā i ka hana poho.
# 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)
ʻO ka hope, hiki iā ʻoe ke hoʻāʻo me nā hale hana a me nā ʻano hana ʻē aʻe. No laila, hiki iā ʻoe ke hoʻonui i ka hana o ke kumu hoʻohālike, e like me nā mīkini hoʻolohe, GAN, a i ʻole VAE.
Waiho i ka Reply