From 9ca7e143afe6f09734c9aefcc85f491c5c0dc6e0 Mon Sep 17 00:00:00 2001
From: rattus <46076784+rattus128@users.noreply.github.com>
Date: Mon, 29 Dec 2025 15:19:34 -0800
Subject: [PATCH 1/3] mm: discard async errors from pinning failures (#10738)

Pretty much every error cudaHostRegister can throw also queues the same
error on the async GPU queue. This was fixed for repinning error case,
but there is the bad mmap and just enomem cases that are harder to
detect.

Do some dummy GPU work to clean the error state.
---
 comfy/model_management.py | 14 ++++++++++++++
 1 file changed, 14 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index e5554e225..9fcb699bc 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1126,6 +1126,16 @@ if not args.disable_pinned_memory:
 
 PINNING_ALLOWED_TYPES = set(["Parameter", "QuantizedTensor"])
 
+def discard_cuda_async_error():
+    try:
+        a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
+        b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
+        _ = a + b
+        torch.cuda.synchronize()
+    except torch.AcceleratorError:
+        #Dump it! We already know about it from the synchronous return
+        pass
+
 def pin_memory(tensor):
     global TOTAL_PINNED_MEMORY
     if MAX_PINNED_MEMORY <= 0:
@@ -1158,6 +1168,8 @@ def pin_memory(tensor):
         PINNED_MEMORY[ptr] = size
         TOTAL_PINNED_MEMORY += size
         return True
+    else:
+        discard_cuda_async_error()
 
     return False
 
@@ -1186,6 +1198,8 @@ def unpin_memory(tensor):
         if len(PINNED_MEMORY) == 0:
             TOTAL_PINNED_MEMORY = 0
         return True
+    else:
+        discard_cuda_async_error()
 
     return False
 

From 0e6221cc79a3f3cbf0e15a8321bfe75fcffbe667 Mon Sep 17 00:00:00 2001
From: comfyanonymous <121283862+comfyanonymous@users.noreply.github.com>
Date: Mon, 29 Dec 2025 15:26:42 -0800
Subject: [PATCH 2/3] Add some warnings for pin and unpin errors. (#11561)

---
 comfy/model_management.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 9fcb699bc..87baedd73 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1169,6 +1169,7 @@ def pin_memory(tensor):
         TOTAL_PINNED_MEMORY += size
         return True
     else:
+        logging.warning("Pin error.")
         discard_cuda_async_error()
 
     return False
@@ -1199,6 +1200,7 @@ def unpin_memory(tensor):
             TOTAL_PINNED_MEMORY = 0
         return True
     else:
+        logging.warning("Unpin error.")
         discard_cuda_async_error()
 
     return False

From d7111e426a48127a97922227b03d31391eb4eba2 Mon Sep 17 00:00:00 2001
From: Tavi Halperin <tavi@lightricks.com>
Date: Tue, 30 Dec 2025 03:07:29 +0200
Subject: [PATCH 3/3] ResizeByLongerSide: support video (#11555)

(cherry picked from commit 98c6840aa4e5fd5407ba9ab113d209011e474bf6)
---
 comfy_extras/nodes_dataset.py | 23 +++++++++++++----------
 1 file changed, 13 insertions(+), 10 deletions(-)

diff --git a/comfy_extras/nodes_dataset.py b/comfy_extras/nodes_dataset.py
index 513aecf3a..5ef851bd0 100644
--- a/comfy_extras/nodes_dataset.py
+++ b/comfy_extras/nodes_dataset.py
@@ -667,16 +667,19 @@ class ResizeImagesByLongerEdgeNode(ImageProcessingNode):
 
     @classmethod
     def _process(cls, image, longer_edge):
-        img = tensor_to_pil(image)
-        w, h = img.size
-        if w > h:
-            new_w = longer_edge
-            new_h = int(h * (longer_edge / w))
-        else:
-            new_h = longer_edge
-            new_w = int(w * (longer_edge / h))
-        img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
-        return pil_to_tensor(img)
+        resized_images = []
+        for image_i in image:
+            img = tensor_to_pil(image_i)
+            w, h = img.size
+            if w > h:
+                new_w = longer_edge
+                new_h = int(h * (longer_edge / w))
+            else:
+                new_h = longer_edge
+                new_w = int(w * (longer_edge / h))
+            img = img.resize((new_w, new_h), Image.Resampling.LANCZOS)
+            resized_images.append(pil_to_tensor(img))
+        return torch.cat(resized_images, dim=0)
 
 
 class CenterCropImagesNode(ImageProcessingNode):