update
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@@ -193,14 +193,14 @@ def main():
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t_batch = torch.full((args.batch_size,), t, device=device, dtype=torch.long)
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eps_pred, logits = model(x_cont, x_disc, t_batch, cond)
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a_t = alphas[t]
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a_bar_t = alphas_cumprod[t]
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if cont_target == "x0":
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x0_pred = eps_pred
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if cont_clamp_x0 > 0:
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x0_pred = torch.clamp(x0_pred, -cont_clamp_x0, cont_clamp_x0)
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eps_pred = (x_cont - torch.sqrt(a_bar_t) * x0_pred) / torch.sqrt(1.0 - a_bar_t)
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a_t = alphas[t]
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a_bar_t = alphas_cumprod[t]
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coef1 = 1.0 / torch.sqrt(a_t)
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coef2 = (1 - a_t) / torch.sqrt(1 - a_bar_t)
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mean_x = coef1 * (x_cont - coef2 * eps_pred)
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