bit diffusion
0.1.4
Implementasi Difusi Bit, upaya kelompok Hinton dalam difusi denoising diskrit, di Pytorch
Sepertinya mereka meleset dari sasaran teks, namun arah penelitiannya masih tampak menjanjikan. Saya pikir repositori yang bersih akan memberikan banyak manfaat bagi komunitas riset bagi mereka yang memulai dari sini.
$ pip install bit-diffusion
from bit_diffusion import Unet , Trainer , BitDiffusion
model = Unet (
dim = 32 ,
channels = 3 ,
dim_mults = ( 1 , 2 , 4 , 8 ),
). cuda ()
bit_diffusion = BitDiffusion (
model ,
image_size = 128 ,
timesteps = 100 ,
time_difference = 0.1 , # they found in the paper that at lower number of timesteps, a time difference during sampling of greater than 0 helps FID. as timesteps increases, this time difference can be set to 0 as it does not help
use_ddim = True # use ddim
). cuda ()
trainer = Trainer (
bit_diffusion ,
'/path/to/your/data' , # path to your folder of images
results_folder = './results' , # where to save results
num_samples = 16 , # number of samples
train_batch_size = 4 , # training batch size
gradient_accumulate_every = 4 , # gradient accumulation
train_lr = 1e-4 , # learning rate
save_and_sample_every = 1000 , # how often to save and sample
train_num_steps = 700000 , # total training steps
ema_decay = 0.995 , # exponential moving average decay
)
trainer . train ()
Hasil akan disimpan secara berkala ke folder ./results
Jika Anda ingin bereksperimen dengan kelas Unet
dan BitDiffusion
di luar Trainer
import torch
from bit_diffusion import Unet , BitDiffusion
model = Unet (
dim = 64 ,
dim_mults = ( 1 , 2 , 4 , 8 )
)
bit_diffusion = BitDiffusion (
model ,
image_size = 128 ,
timesteps = 1000
)
training_images = torch . randn ( 8 , 3 , 128 , 128 ) # images are normalized from 0 to 1
loss = bit_diffusion ( training_images )
loss . backward ()
# after a lot of training
sampled_images = bit_diffusion . sample ( batch_size = 4 )
sampled_images . shape # (4, 3, 128, 128)
@article { Chen2022AnalogBG ,
title = { Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning } ,
author = { Ting Chen and Ruixiang Zhang and Geoffrey E. Hinton } ,
journal = { ArXiv } ,
year = { 2022 } ,
volume = { abs/2208.04202 }
}