update ks
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@@ -293,12 +293,17 @@ def main():
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if q_weight > 0:
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q_points = config.get("quantile_points", [0.05, 0.25, 0.5, 0.75, 0.95])
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q_tensor = torch.tensor(q_points, device=device, dtype=x_cont.dtype)
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# Use normalized space for stable quantiles.
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# Use normalized space for stable quantiles on x0.
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x_real = x_cont
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a_bar_t = alphas_cumprod[t].view(-1, 1, 1)
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if cont_target == "x0":
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x_gen = eps_pred
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elif cont_target == "v":
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v_pred = eps_pred
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x_gen = torch.sqrt(a_bar_t) * x_cont_t - torch.sqrt(1.0 - a_bar_t) * v_pred
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else:
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x_gen = x_cont - noise
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# eps prediction
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x_gen = (x_cont_t - torch.sqrt(1.0 - a_bar_t) * eps_pred) / torch.sqrt(a_bar_t)
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x_real = x_real.view(-1, x_real.size(-1))
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x_gen = x_gen.view(-1, x_gen.size(-1))
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q_real = torch.quantile(x_real, q_tensor, dim=0)
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