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import torch
def add_diffusion_noise(image_tensor, noise_step):
num_steps = 1000 # Number of diffusion steps
# decide beta in each step
betas = torch.linspace(-6,6,num_steps)
betas = torch.sigmoid(betas) * (0.5e-2 - 1e-5) + 1e-5
# decide alphas in each step
alphas = 1 - betas
alphas_prod = torch.cumprod(alphas, dim=0)
alphas_prod_p = torch.cat([torch.tensor([1]).float(), alphas_prod[:-1]],0) # p for previous
alphas_bar_sqrt = torch.sqrt(alphas_prod)
one_minus_alphas_bar_log = torch.log(1 - alphas_prod)
one_minus_alphas_bar_sqrt = torch.sqrt(1 - alphas_prod)
def q_x(x_0,t):
noise = torch.randn_like(x_0)
alphas_t = alphas_bar_sqrt[t]
alphas_1_m_t = one_minus_alphas_bar_sqrt[t]
return (alphas_t*x_0 + alphas_1_m_t*noise)
noise_delta = int(noise_step) # from 0-999
noisy_image = image_tensor.clone()
image_tensor_cd = q_x(noisy_image,noise_step)
return image_tensor_cd
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