diff --git a/modules/processing.py b/modules/processing.py
index e115aadd1..557355725 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -501,26 +501,26 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
             if k == 'sd_vae':
                 sd_vae.reload_vae_weights()
 
-        if opts.token_merging:
-
-            if p.hr_second_pass_steps < 1 and not opts.token_merging_hr_only:
-                tomesd.apply_patch(
-                    p.sd_model,
-                    ratio=opts.token_merging_ratio,
-                    max_downsample=opts.token_merging_maximum_down_sampling,
-                    sx=opts.token_merging_stride_x,
-                    sy=opts.token_merging_stride_y,
-                    use_rand=opts.token_merging_random,
-                    merge_attn=opts.token_merging_merge_attention,
-                    merge_crossattn=opts.token_merging_merge_cross_attention,
-                    merge_mlp=opts.token_merging_merge_mlp
-                )
+        if opts.token_merging and not opts.token_merging_hr_only:
+            print("applying token merging to all passes")
+            tomesd.apply_patch(
+                p.sd_model,
+                ratio=opts.token_merging_ratio,
+                max_downsample=opts.token_merging_maximum_down_sampling,
+                sx=opts.token_merging_stride_x,
+                sy=opts.token_merging_stride_y,
+                use_rand=opts.token_merging_random,
+                merge_attn=opts.token_merging_merge_attention,
+                merge_crossattn=opts.token_merging_merge_cross_attention,
+                merge_mlp=opts.token_merging_merge_mlp
+            )
 
         res = process_images_inner(p)
 
     finally:
         # undo model optimizations made by tomesd
         if opts.token_merging:
+            print('removing token merging model optimizations')
             tomesd.remove_patch(p.sd_model)
 
         # restore opts to original state
@@ -961,6 +961,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
         # apply token merging optimizations from tomesd for high-res pass
         # check if hr_only so we don't redundantly apply patch
         if opts.token_merging and opts.token_merging_hr_only:
+            print("applying token merging for high-res pass")
             tomesd.apply_patch(
                 self.sd_model,
                 ratio=opts.token_merging_ratio,