ModuFlow/mask-ddpm
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MingzheYang
2026-01-24 01:26:23 +08:00
parent 0105a0b206
commit 94a5795f29
1 changed files with 3 additions and 3 deletions
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6
example/export_samples.py
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@@ -193,14 +193,14 @@ def main():
t_batch = torch.full((args.batch_size,), t, device=device, dtype=torch.long) t_batch = torch.full((args.batch_size,), t, device=device, dtype=torch.long)
eps_pred, logits = model(x_cont, x_disc, t_batch, cond) eps_pred, logits = model(x_cont, x_disc, t_batch, cond)
a_t = alphas[t]
a_bar_t = alphas_cumprod[t]
if cont_target == "x0": if cont_target == "x0":
x0_pred = eps_pred x0_pred = eps_pred
if cont_clamp_x0 > 0: if cont_clamp_x0 > 0:
x0_pred = torch.clamp(x0_pred, -cont_clamp_x0, cont_clamp_x0) x0_pred = torch.clamp(x0_pred, -cont_clamp_x0, cont_clamp_x0)
eps_pred = (x_cont - torch.sqrt(a_bar_t) * x0_pred) / torch.sqrt(1.0 - a_bar_t) eps_pred = (x_cont - torch.sqrt(a_bar_t) * x0_pred) / torch.sqrt(1.0 - a_bar_t)
a_t = alphas[t]
a_bar_t = alphas_cumprod[t]
coef1 = 1.0 / torch.sqrt(a_t) coef1 = 1.0 / torch.sqrt(a_t)
coef2 = (1 - a_t) / torch.sqrt(1 - a_bar_t) coef2 = (1 - a_t) / torch.sqrt(1 - a_bar_t)
mean_x = coef1 * (x_cont - coef2 * eps_pred) mean_x = coef1 * (x_cont - coef2 * eps_pred)