bit diffusion

نشر البت - Pytorch

تنفيذ Bit Diffusion، محاولة مجموعة هينتون لنشر تقليل الضوضاء المنفصل، في Pytorch

يبدو أنهم أخطأوا علامة النص، لكن اتجاه البحث لا يزال يبدو واعدًا. أعتقد أن المستودع النظيف سيقدم لمجتمع البحث الكثير من الفوائد لأولئك الذين يتفرعون من هنا.

ثَبَّتَ

$ pip install bit-diffusion

الاستخدام

 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 ()

سيتم حفظ النتائج بشكل دوري في مجلد ./results

إذا كنت ترغب في تجربة فئة Unet و BitDiffusion خارج 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) 

الاستشهادات

 @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 }
}