From 2865cb730941627ee8ff2606dad9fd0d27e87914 Mon Sep 17 00:00:00 2001
From: Alex Butler <alexheretic@gmail.com>
Date: Wed, 23 Apr 2025 21:30:30 +0100
Subject: [PATCH 1/2] vae handle HIP OOM exceptions

---
 comfy/model_management.py | 8 ++++++++
 comfy/sd.py               | 5 +++--
 2 files changed, 11 insertions(+), 2 deletions(-)

diff --git a/comfy/model_management.py b/comfy/model_management.py
index 43e40224343..464947d5dfd 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -235,6 +235,14 @@ def mac_version():
 except:
     OOM_EXCEPTION = Exception
 
+
+def is_oom_exception(ex):
+    if isinstance(ex, OOM_EXCEPTION):
+        return True
+    # handle also other kinds of oom, e.g. "HIP error: out of memory"
+    msg = str(ex)
+    return "out of memory" in msg
+
 XFORMERS_VERSION = ""
 XFORMERS_ENABLED_VAE = True
 if args.disable_xformers:
diff --git a/comfy/sd.py b/comfy/sd.py
index 8aba5d65552..788bf9e7b80 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -592,8 +592,9 @@ def encode(self, pixel_samples):
                 if samples is None:
                     samples = torch.empty((pixel_samples.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 samples[x:x + batch_number] = out
-
-        except model_management.OOM_EXCEPTION:
+        except Exception as ex:
+            if not model_management.is_oom_exception(ex):
+                raise
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
             if self.latent_dim == 3:
                 tile = 256

From 398805d794b400b74619ad857c6e52ed260fca0c Mon Sep 17 00:00:00 2001
From: Alex Butler <alexheretic@gmail.com>
Date: Wed, 23 Apr 2025 21:33:58 +0100
Subject: [PATCH 2/2] vae decode handle HIP oom exceptions

---
 comfy/sd.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/comfy/sd.py b/comfy/sd.py
index 788bf9e7b80..1b8bc2927aa 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -529,7 +529,9 @@ def decode(self, samples_in, vae_options={}):
                 if pixel_samples is None:
                     pixel_samples = torch.empty((samples_in.shape[0],) + tuple(out.shape[1:]), device=self.output_device)
                 pixel_samples[x:x+batch_number] = out
-        except model_management.OOM_EXCEPTION:
+        except Exception as ex:
+            if not model_management.is_oom_exception(ex):
+                raise
             logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             dims = samples_in.ndim - 2
             if dims == 1: