85 lines
3.4 KiB
Python
85 lines
3.4 KiB
Python
import torch
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import torch.nn as nn
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import matplotlib.pyplot as plt
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class DDPM(nn.Module):
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'''
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Denoising Diffussion Probabilistic Model
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Inputs:
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Args:
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batch_size (int): batch_size, for generate time_seq, etc.
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iteration (int): max time_seq
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beta_min, beta_max (float): for beta scheduling
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device (nn.Device)
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'''
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def __init__(self, batch_size, iteration, beta_min, beta_max, device):
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super(DDPM, self).__init__()
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self.batch_size = batch_size
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self.iteration = iteration
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self.device = device
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self.beta = torch.linspace(beta_min, beta_max, steps=iteration).to(self.device) # (iteration)
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self.alpha = (1 - self.beta).to(self.device) # (iteration)
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self.overline_alpha = torch.cumprod(self.alpha, dim=0)
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def get_time_seq(self, length):
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'''
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Get random time sequence for each picture in the batch
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Inputs:
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length (int): size of sequence
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Outputs:
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time_seq: rand int from 0 to ITERATION
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'''
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return torch.randint(0, self.iteration, (length,) ).to(self.device)
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def get_x_t(self, x_0, time_seq):
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'''
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Input pictures then return noised pictures
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Inputs:
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x_0: pictures (b, c, w, h)
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time_seq: times apply on each pictures (b, )
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Outputs:
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x_t: noised pictures (b, c, w, h)
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'''
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b, c, w, h = x_0.shape
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mu = torch.sqrt(self.overline_alpha[time_seq])[:, None, None, None].repeat(1, c, w, h) # (b, c, w, h)
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mu = mu * x_0 # (b, c, w, h)
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sigma = torch.sqrt(1-self.overline_alpha[time_seq])[:, None, None, None].repeat(1, c, w, h) # (b, c, w, h)
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epsilon = torch.randn_like(x_0).to(self.device) # (b, c, w, h)
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return mu + sigma * epsilon, epsilon # (b, c, w, h)
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def sample(self, model, n):
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'''
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Inputs:
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model (nn.Module): Unet instance
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n (int): want to sample n pictures
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Outputs:
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x_0 (nn.Tensor): (n, c, h, w)
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'''
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c, h, w = 1, 28, 28
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model.eval()
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with torch.no_grad():
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x_t = torch.randn((n, c, h, w)).to(self.device) # (n, c, h, w)
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for i in reversed(range(self.iteration)):
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time_seq = (torch.ones(n) * i).long().to(self.device) # (n, )
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predict_noise = model(x_t, time_seq) # (n, c, h, w)
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first_term = 1/(torch.sqrt(self.alpha[time_seq])) # (n, )
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second_term = (1-self.alpha[time_seq]) / (torch.sqrt(1-self.overline_alpha[time_seq]))
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first_term = first_term[:, None, None, None].repeat(1, c, h, w)
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second_term = second_term[:, None, None, None].repeat(1, c, h, w)
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beta = self.beta[time_seq][:, None, None, None].repeat(1, c, h, w)
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z = torch.randn((n, c, h, w))
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x_t = first_term * (x_t-(second_term * predict_noise)) - z * beta
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x_t = ( x_t.clamp(-1, 1) + 1 ) / 2
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x = x * 255
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return x_t |