diff --git a/dumas_h3_longvideos.py b/dumas_h3_longvideos.py
index c6f1a6f..eb6bfcb 100644
--- a/dumas_h3_longvideos.py
+++ b/dumas_h3_longvideos.py
@@ -4136,6 +4136,12 @@ def _param_value(mapping, key, default):
     return default if value is None else value
 
 
+def _tag_oom_stage(exc, stage):
+    if _is_oom(exc):
+        exc._h3_stage = stage
+    return exc
+
+
 def _grid_1d(size, tile, ol, min_tile):
     if size <= tile:
         return [0], [size], [0]
@@ -6645,9 +6651,7 @@ class H3LongVideos:
             # OOM retry cannot help an OOM raised here -- it just re-runs the whole
             # sampling pass and fails the same way, which on a 362-frame shot is four
             # more minutes for nothing.
-            if _is_oom(e):
-                e._h3_stage = "sampling"
-            raise
+            raise _tag_oom_stage(e, "sampling")
         refined_out = out
         latent_upscale_param = latent_upscale_param or None
         latent_upscale_mode = _latent_upscale_mode(latent_upscale_param)
@@ -6832,9 +6836,7 @@ class H3LongVideos:
                 del upscale_latent, upscaled_video, full_audio
                 mm.soft_empty_cache()
             except Exception as e:
-                if _is_oom(e):
-                    e._h3_stage = "sampling"
-                raise
+                raise _tag_oom_stage(e, "latent_upscale")
         # Keep a CPU copy of the sampled latent BEFORE decoding, for the `latent`
         # output. Latents are ~1000x smaller than the frames they decode to (a
         # 1344x768 124f shot is ~1.5MB against ~1.5GB), so carrying one per shot for
@@ -7446,13 +7448,19 @@ class H3LongVideos:
                             shot_refs, ref_image_size, shot_aug, shot_silent,
                             latent_upscale_param=latent_upscale_param, timing_sink=shot_timing)
                         break
-                    except (torch.cuda.OutOfMemoryError, RuntimeError) as e:
-                        shot_retry_elapsed += time.perf_counter() - attempt_start
-                        if not _is_oom(e):
-                            raise
-                        mm.soft_empty_cache(True)
-                        if not tiled:
-                            tiled = True; backoff.append("tiled decode")
+                    except (torch.cuda.OutOfMemoryError, RuntimeError) as e:
+                        shot_retry_elapsed += time.perf_counter() - attempt_start
+                        if not _is_oom(e):
+                            raise
+                        if getattr(e, "_h3_stage", "") == "latent_upscale":
+                            raise RuntimeError(
+                                f"H3 Long Videos: shot {i + 1} of {len(gens)} ran out of VRAM "
+                                f"during latent upscale. The latent-upscale stage is too large "
+                                f"for this card at {w}x{h} with the current spatial settings."
+                            ) from e
+                        mm.soft_empty_cache(True)
+                        if not tiled:
+                            tiled = True; backoff.append("tiled decode")
                         elif allow_res_backoff and min(w, h) > 384:
                             nw, nh = res_down(w, h); backoff.append(f"res->{nw}x{nh}"); w, h = nw, nh
                         else:
@@ -7467,13 +7475,20 @@ class H3LongVideos:
                         tiled, sa, shot_handoff, decode_tile_frames, decode_tile_size,
                         shot_refs, ref_image_size, shot_aug, shot_silent,
                         latent_upscale_param=latent_upscale_param, timing_sink=shot_timing)
-                except (torch.cuda.OutOfMemoryError, RuntimeError) as e:
-                    shot_retry_elapsed += time.perf_counter() - attempt_start
-                    if _is_oom(e) and getattr(e, "_h3_stage", "") == "sampling":
-                        # Retrying with tiles would re-run the whole sampling pass and
-                        # fail identically. Fail now, and say what actually shrinks it.
-                        raise RuntimeError(
-                            f"H3 Long Videos: shot {i + 1} of {len(gens)} ran out of VRAM "
+                except (torch.cuda.OutOfMemoryError, RuntimeError) as e:
+                    shot_retry_elapsed += time.perf_counter() - attempt_start
+                    stage = getattr(e, "_h3_stage", "")
+                    if _is_oom(e) and stage == "latent_upscale":
+                        raise RuntimeError(
+                            f"H3 Long Videos: shot {i + 1} of {len(gens)} ran out of VRAM "
+                            f"during latent upscale. The latent-upscale stage is too large "
+                            f"for this card at {w}x{h} with the current spatial settings."
+                        ) from e
+                    if _is_oom(e) and getattr(e, "_h3_stage", "") == "sampling":
+                        # Retrying with tiles would re-run the whole sampling pass and
+                        # fail identically. Fail now, and say what actually shrinks it.
+                        raise RuntimeError(
+                            f"H3 Long Videos: shot {i + 1} of {len(gens)} ran out of VRAM "
                             f"while sampling. " + sampling_oom_help(w, h, ln_i, fps, megapixels)
                         ) from e
                     if not _is_oom(e) or tiled:
diff --git a/tests/test_dumas_h3_longvideos.py b/tests/test_dumas_h3_longvideos.py
index f54a01d..61601b0 100644
--- a/tests/test_dumas_h3_longvideos.py
+++ b/tests/test_dumas_h3_longvideos.py
@@ -1154,6 +1154,12 @@ class DumasH3LongVideosHelperTests(unittest.TestCase):
         self.assertEqual(self.module._latent_upscale_mode({"mode": "model"}), "model")
         self.assertEqual(self.module._latent_upscale_mode({}), "off")
 
+    def test_tag_oom_stage_marks_oom_exceptions(self):
+        exc = RuntimeError("CUDA out of memory")
+        tagged = self.module._tag_oom_stage(exc, "latent_upscale")
+        self.assertIs(tagged, exc)
+        self.assertEqual(getattr(tagged, "_h3_stage", ""), "latent_upscale")
+
     def test_compose_persistent_does_not_expand_ambiguous_plural_to_full_cast(self):
         active = self.module.parse_wardrobe(
             "Maya = she, red jacket\n"