Created
August 2, 2022 19:51
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| def q_sample(x_start, t, list_bar_alphas, device): | |
| """ | |
| Diffuse the data (t == 0 means diffused for 1 step) | |
| """ | |
| alpha_bar_t = list_bar_alphas[t] | |
| mean = alpha_bar_t*x_start | |
| cov = torch.eye(x_start.shape[0]).to(device) | |
| cov = cov*(1-alpha_bar_t) | |
| return torch.distributions.MultivariateNormal(loc=mean,covariance_matrix=cov).sample().to(device) |
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