diff --git a/library/custom_train_functions.py b/library/custom_train_functions.py
index 2d387d156..0c527c352 100644
--- a/library/custom_train_functions.py
+++ b/library/custom_train_functions.py
@@ -346,14 +346,14 @@ def get_weighted_text_embeddings(
 
 
 # https://wandb.ai/johnowhitaker/multires_noise/reports/Multi-Resolution-Noise-for-Diffusion-Model-Training--VmlldzozNjYyOTU2
-def pyramid_noise_like(noise, device, iterations=6, discount=0.3):
-    b, c, w, h = noise.shape
+def pyramid_noise_like(noise, device, iterations=6, discount=0.4):
+    b, c, w, h = noise.shape # EDIT: w and h get over-written, rename for a different variant!
     u = torch.nn.Upsample(size=(w, h), mode="bilinear").to(device)
     for i in range(iterations):
         r = random.random() * 2 + 2  # Rather than always going 2x,
-        w, h = max(1, int(w / (r**i))), max(1, int(h / (r**i)))
-        noise += u(torch.randn(b, c, w, h).to(device)) * discount**i
-        if w == 1 or h == 1:
+        wn, hn = max(1, int(w / (r**i))), max(1, int(h / (r**i)))
+        noise += u(torch.randn(b, c, wn, hn).to(device)) * discount**i
+        if wn == 1 or hn == 1:
             break  # Lowest resolution is 1x1
     return noise / noise.std()  # Scaled back to roughly unit variance