Take some code from chainner to implement ESRGAN and other upscale models.

comfy_extras/chainner_models/architecture/timm/helpers.py

@@ -0,0 +1,31 @@
+""" Layer/Module Helpers
+Hacked together by / Copyright 2020 Ross Wightman
+"""
+import collections.abc
+from itertools import repeat
+
+
+# From PyTorch internals
+def _ntuple(n):
+    def parse(x):
+        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
+            return x
+        return tuple(repeat(x, n))
+
+    return parse
+
+
+to_1tuple = _ntuple(1)
+to_2tuple = _ntuple(2)
+to_3tuple = _ntuple(3)
+to_4tuple = _ntuple(4)
+to_ntuple = _ntuple
+
+
+def make_divisible(v, divisor=8, min_value=None, round_limit=0.9):
+    min_value = min_value or divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than 10%.
+    if new_v < round_limit * v:
+        new_v += divisor
+    return new_v