diff --git a/comfy/text_encoders/krea2.py b/comfy/text_encoders/krea2.py
index 408a03566..0341dd6e3 100644
--- a/comfy/text_encoders/krea2.py
+++ b/comfy/text_encoders/krea2.py
@@ -20,6 +20,20 @@ KREA2_TAP_LAYERS = [2, 5, 8, 11, 14, 17, 20, 23, 26, 29, 32, 35]
 KREA2_TEMPLATE = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n"
 
 
+def _krea2_template_end(tok_pairs):
+    image_position = next((i for i, pair in enumerate(tok_pairs) if isinstance(pair[0], dict) and pair[0].get("type") == "image"), len(tok_pairs))
+    template_end = -1
+    for i in range(image_position):
+        if i + 2 >= len(tok_pairs):
+            break
+        values = [tok_pairs[j][0] for j in range(i, i + 3)]
+        if all(not torch.is_tensor(value) and isinstance(value, numbers.Integral) for value in values) and values == [151644, 872, 198]:
+            template_end = i + 3
+    if template_end == -1:
+        raise ValueError("Could not locate the Krea 2 user prompt template.")
+    return template_end
+
+
 class Krea2Tokenizer(comfy.text_encoders.qwen3vl.Qwen3VLTokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, model_type="qwen3vl_4b")
@@ -44,19 +58,8 @@ class Krea2TEModel(sd1_clip.SD1ClipModel):
         out, pooled, extra = super().encode_token_weights(token_weight_pairs)  # out: (B, 12, seq, 2560)
         tok_pairs = token_weight_pairs["qwen3vl_4b"][0]
 
-        # Strip the system + user-opening prefix
-        count_im_start = 0
         if template_end == -1:
-            for i, v in enumerate(tok_pairs):
-                elem = v[0]
-                if not torch.is_tensor(elem) and isinstance(elem, numbers.Integral):
-                    if elem == 151644 and count_im_start < 2:
-                        template_end = i
-                        count_im_start += 1
-            if out.shape[2] > (template_end + 3):
-                if tok_pairs[template_end + 1][0] == 872:      # "user"
-                    if tok_pairs[template_end + 2][0] == 198:   # "\n"
-                        template_end += 3
+            template_end = _krea2_template_end(tok_pairs)
 
         out = out[:, :, template_end:]
 
diff --git a/tests-unit/comfy_extras_test/test_qwen_visual_fusion.py b/tests-unit/comfy_extras_test/test_qwen_visual_fusion.py
index d878ee0be..ff3c104c7 100644
--- a/tests-unit/comfy_extras_test/test_qwen_visual_fusion.py
+++ b/tests-unit/comfy_extras_test/test_qwen_visual_fusion.py
@@ -8,6 +8,7 @@ if not torch.cuda.is_available():
     cli_args.cpu = True
 
 try:
+    from comfy.text_encoders.krea2 import KREA2_TEMPLATE, Krea2Tokenizer, _krea2_template_end
     from comfy_extras.nodes_cond import CLIPTextEncodeImageFusion, _flatten_images, _fuse_conditionings, _resize_visual_tokens, _spatial_fusion_mask, _visual_grid, _visual_token_span
 finally:
     cli_args.cpu = prior_cpu
@@ -62,6 +63,22 @@ def test_visual_span_accounts_for_stripped_prefix():
     assert _visual_token_span(tokens, cond_length=9, visual_tokens=4) == (1, 5)
 
 
+def test_krea_template_stripping_preserves_visual_tokens():
+    tokenizer = Krea2Tokenizer()
+    image = torch.zeros(1, 64, 64, 3)
+    image_prompt = "<|vision_start|><|image_pad|><|vision_end|>test prompt"
+
+    for text in ("test prompt", KREA2_TEMPLATE.format(image_prompt)):
+        tokens = tokenizer.tokenize_with_weights(text, images=[image])
+        token_pairs = tokens["qwen3vl_4b"][0]
+        image_position = next(i for i, pair in enumerate(token_pairs) if isinstance(pair[0], dict))
+        template_end = _krea2_template_end(token_pairs)
+        cond_length = len(token_pairs) - 1 + 4 - template_end
+
+        assert template_end <= image_position
+        assert _visual_token_span(tokens, cond_length, 4) == (image_position - template_end, image_position - template_end + 4)
+
+
 def test_fusion_replaces_only_visual_tokens_and_preserves_dtype_and_metadata():
     tokens = [_tokens(), _tokens()]
     first = torch.tensor([[[10], [10], [10], [10], [10], [20]]], dtype=torch.float16)