diff --git a/codes/models/diffusion/gaussian_diffusion.py b/codes/models/diffusion/gaussian_diffusion.py
index 1cf59dd9..385c790f 100644
--- a/codes/models/diffusion/gaussian_diffusion.py
+++ b/codes/models/diffusion/gaussian_diffusion.py
@@ -25,7 +25,9 @@ def causal_timestep_adjustment(t, S, num_timesteps, causal_slope=1, add_jitter=T
     sequence is all [num_timesteps].
 
     As a result of the last property, longer sequences will have larger "gaps" between them in continuous space. This must
-    be considered at inference time.
+    be considered at inference time. Specifically, you should allot ((num_timesteps+causal_slope*(seq_len-1))/num_timesteps)
+    times more timesteps in inference for the same quality.
+
     :param t: Batched timestep integers
     :param S: Sequence length.
     :param num_timesteps: Number of total timesteps.
@@ -37,7 +39,7 @@ def causal_timestep_adjustment(t, S, num_timesteps, causal_slope=1, add_jitter=T
     S_sloped = causal_slope * (S-1)
     # This algorithm for adding causality does so by simply adding S_sloped additional timesteps. To make this
     # actually work, we map the existing t from the timescale specified to the model to the causal timescale:
-    adj_t = t * (num_timesteps + S_sloped) // num_timesteps
+    adj_t = torch.div(t * (num_timesteps + S_sloped), num_timesteps, rounding_mode='floor')
     adj_t = adj_t - S_sloped
     if add_jitter:
         t_gap = (num_timesteps + S_sloped) / num_timesteps
@@ -1129,12 +1131,11 @@ def graph_causal_timestep_adjustment():
 def graph_causal_timestep_adjustment_by_timestep():
     import matplotlib.pyplot as plt
     S = 400
-    slope=10
+    slope=8
     num_timesteps=4000
     t_res = []
     for t in range(num_timesteps, -1, -num_timesteps//50):
         T = causal_timestep_adjustment(torch.tensor([t]), S, num_timesteps, causal_slope=slope, add_jitter=False)[0]
-
         t_res.append(T)
         plt.plot(T.numpy())
         plt.ylim(0,num_timesteps)