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horoiwa/inv_diff.py

Created July 11, 2023 09:15
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inv_diff.py
eps_t = self(x_t, t, states)
mu = (1.0 / tf.sqrt(1.0 - beta_t)) * (x_t - (beta_t / tf.sqrt(1.0 - alphas_cumprod_t)) * eps_t)
sigma = tf.sqrt(tf.reshape(tf.gather(self.variance, indices=t), (-1, 1)))
noise = tf.random.normal(shape=x_t.shape, mean=0., stddev=1.)
x_t_minus_1 = mu + sigma * noise
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