From ae863719912e96c5c34d9cf6714edbcce9058f34 Mon Sep 17 00:00:00 2001
From: Lightje <77324749+Lightje@users.noreply.github.com>
Date: Sat, 31 May 2025 02:50:50 +0200
Subject: [PATCH] Update utils.py, to fix upscaling error of non contiguous
 images on mac

Changes Made:
Added samples = samples.contiguous() at the start.
Added tile = (min(tile[0], samples.shape[2]), min(tile[1], samples.shape[3])) for dynamic tile sizing.
Modified output[b:b+1] = function(s.contiguous()).to(output_device) in the single-tile case.
Modified ps = function(s_in.contiguous()).to(output_device) in the tiled loop.
---
 comfy/utils.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/comfy/utils.py b/comfy/utils.py
index 1f8d7129251..90ff0f6eaaa 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -875,6 +875,7 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
 @torch.inference_mode()
 def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", downscale=False, index_formulas=None, pbar=None):
     dims = len(tile)
+    samples = samples.contiguous()  # Ensure input tensor is contiguous
 
     if not (isinstance(upscale_amount, (tuple, list))):
         upscale_amount = [upscale_amount] * dims
@@ -936,7 +937,7 @@ def mult_list_upscale(a):
 
         # handle entire input fitting in a single tile
         if all(s.shape[d+2] <= tile[d] for d in range(dims)):
-            output[b:b+1] = function(s).to(output_device)
+            output[b:b+1] = function(s.contiguous()).to(output_device)  # Ensure single tile is contiguous
             if pbar is not None:
                 pbar.update(1)
             continue
@@ -956,7 +957,7 @@ def mult_list_upscale(a):
                 s_in = s_in.narrow(d + 2, pos, l)
                 upscaled.append(round(get_pos(d, pos)))
 
-            ps = function(s_in).to(output_device)
+            ps = function(s_in.contiguous()).to(output_device)  # Ensure tiled segment is contiguous
             mask = torch.ones_like(ps)
 
             for d in range(2, dims + 2):
@@ -982,7 +983,7 @@ def mult_list_upscale(a):
 
         output[b:b+1] = out/out_div
     return output
-
+    
 def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
     return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)