ModuFlow/mask-ddpm
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update ks

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MingzheYang
2026-01-25 17:55:28 +08:00
parent 5ef1e465f9
commit cc10125fbf
4 changed files with 18 additions and 7 deletions
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11
example/sample.py
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@@ -114,15 +114,18 @@ def main():
t_batch = torch.full((batch_size,), t, device=DEVICE, dtype=torch.long)
eps_pred, logits = model(x_cont, x_disc, t_batch, cond)
# Continuous reverse step (DDPM): x_{t-1} mean
a_t = alphas[t]
a_bar_t = alphas_cumprod[t]
if cont_target == "x0":
x0_pred = eps_pred
if cont_clamp_x0 > 0:
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)
# Continuous reverse step (DDPM): x_{t-1} mean
a_t = alphas[t]
a_bar_t = alphas_cumprod[t]
elif cont_target == "v":
v_pred = eps_pred
x0_pred = torch.sqrt(a_bar_t) * x_cont - torch.sqrt(1.0 - a_bar_t) * v_pred
eps_pred = torch.sqrt(1.0 - a_bar_t) * x_cont + torch.sqrt(a_bar_t) * v_pred
coef1 = 1.0 / torch.sqrt(a_t)
coef2 = (1 - a_t) / torch.sqrt(1 - a_bar_t)
mean = coef1 * (x_cont - coef2 * eps_pred)