Delete sampler.py

sampler.py

@@ -1,44 +0,0 @@
-import torch
-
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
-
-
-def extract(a, t, x_shape):
-    batch_size = t.shape[0]
-    out = a.gather(0, t.to(a.device))
-    return out.reshape(batch_size, *((1,) * (len(x_shape) - 1)))
-
-
-beta_start = 1e-4
-beta_end = 0.02
-num_timesteps = 1000
-
-betas = torch.linspace(
-    beta_start,
-    beta_end,
-    num_timesteps,
-    dtype=torch.float32,
-    device=device,
-)
-
-alphas = 1.0 - betas
-alphas_cumprod = torch.cumprod(alphas, dim=0)
-
-
-@torch.no_grad()
-def ddim_sample(model, x, t, t_prev, labels=None):
-    alpha_bar_t = extract(alphas_cumprod, t, x.shape)
-    alpha_bar_prev = extract(alphas_cumprod, t_prev, x.shape)
-
-    pred_noise = model(x, t, labels)
-
-    x0 = (
-        x - torch.sqrt(1 - alpha_bar_t) * pred_noise
-    ) / torch.sqrt(alpha_bar_t)
-
-    x_prev = (
-        torch.sqrt(alpha_bar_prev) * x0
-        + torch.sqrt(1 - alpha_bar_prev) * pred_noise
-    )
-
-    return x_prev