diff --git a/src/transformers/generation/utils.py b/src/transformers/generation/utils.py
index edfe829b82f3..75d47ad47249 100644
--- a/src/transformers/generation/utils.py
+++ b/src/transformers/generation/utils.py
@@ -1885,9 +1885,34 @@ def _prepare_static_cache(
                 "offloading": offload_cache,
             }
             cache = EncoderDecoderCache(cache, StaticCache(**cross_attention_cache_kwargs))
+
+        if self.config.is_encoder_decoder:
+            assert isinstance(cache, EncoderDecoderCache)  # always true: assigned above when is_encoder_decoder
+            # Eagerly initialize the K/V buffers so that `mark_static_address` is applied before any
+            # `torch.compile` / CUDAGraph capture context. Without this, `lazy_initialization` runs inside
+            # graph capture (where `is_torchdynamo_compiling()` is True), causing `mark_static_address` to
+            # be skipped. CUDAGraphs then treats those tensors as pooled memory and can overwrite them
+            # between runs, raising:
+            #   "accessing tensor output of CUDAGraphs that has been overwritten by a subsequent run"
+            # BOTH self-attention and cross-attention caches need early init: cross-attention K/V are
+            # computed from encoder hidden states on the first decode step, which happens inside the
+            # compiled graph, so they suffer the same lazy-init-inside-capture problem.
+            # Skipped when the cache cannot be initialized on a single device.
+            init_shape = self._get_static_cache_init_shape()
+            if init_shape is not None:
+                num_heads, head_dim = init_shape
+                early_init_kwargs = {
+                    "batch_size": batch_size,
+                    "num_heads": num_heads,
+                    "head_dim": head_dim,
+                    "dtype": self.dtype,
+                    "device": self.device,
+                }
+                cache.self_attention_cache.early_initialization(**early_init_kwargs)
+                cache.cross_attention_cache.early_initialization(**early_init_kwargs)
         elif prefill_chunk_size is not None:
-            # Chunked prefill compiles the prefill, so eagerly init the fresh cache to avoid a recompile next call
-            # (#46421). Skipped (-> lazy init) when it can't be initialized on a single device.
+            # Chunked prefill compiles the prefill, so eagerly init the fresh cache to avoid a recompile
+            # next call (#46446). Skipped (-> lazy init) when it can't be initialized on a single device.
             init_shape = self._get_static_cache_init_shape()
             if init_shape is not None:
                 num_heads, head_dim = init_shape