diff --git a/tests-unit/comfy_extras_test/test_qwen_visual_fusion.py b/tests-unit/comfy_extras_test/test_qwen_visual_fusion.py
new file mode 100644
index 000000000..bed7fe9f4
--- /dev/null
+++ b/tests-unit/comfy_extras_test/test_qwen_visual_fusion.py
@@ -0,0 +1,102 @@
+import torch
+
+from comfy_extras.nodes_qwen import TextEncodeQwenImageEditFusion, _flatten_images, _fuse_conditionings, _spatial_fusion_mask, _visual_token_span
+
+
+def _tokens(image_position=1, suffix=1):
+    pairs = [(1, 1.0)] * image_position
+    pairs.append(({"type": "image", "data": torch.zeros(1, 32, 32, 3)}, 1.0))
+    pairs.extend([(2, 1.0)] * suffix)
+    return {"qwen3vl_4b": [pairs]}
+
+
+def test_checkerboard_mask_multiple_sources():
+    mask = _spatial_fusion_mask(2, 3, 3, "spatial-checkerboard", 2, 0.5, "cpu")
+    assert mask.tolist() == [0, 1, 2, 1, 2, 0]
+
+
+def test_block_interleave_mask():
+    mask = _spatial_fusion_mask(4, 4, 2, "spatial-block-interleave", 2, 0.5, "cpu")
+    assert mask.reshape(4, 4).tolist() == [
+        [0, 0, 1, 1],
+        [0, 0, 1, 1],
+        [1, 1, 0, 0],
+        [1, 1, 0, 0],
+    ]
+
+
+def test_dither_mask_is_deterministic_and_honors_two_source_ratio():
+    first = _spatial_fusion_mask(4, 4, 2, "spatial-dither-random", 2, 0.5, "cpu")
+    second = _spatial_fusion_mask(4, 4, 2, "spatial-dither-random", 2, 0.5, "cpu")
+    assert torch.equal(first, second)
+    assert _spatial_fusion_mask(2, 2, 2, "spatial-dither-random", 2, 1.0, "cpu").tolist() == [0, 0, 0, 0]
+    assert _spatial_fusion_mask(2, 2, 2, "spatial-dither-random", 2, 0.0, "cpu").tolist() == [1, 1, 1, 1]
+
+
+def test_visual_span_accounts_for_stripped_prefix():
+    tokens = _tokens(image_position=3, suffix=4)
+    assert _visual_token_span(tokens, cond_length=9, visual_tokens=4) == (1, 5)
+
+
+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)
+    second = torch.tensor([[[30], [30], [30], [30], [30], [40]]], dtype=torch.float16)
+    metadata = {"pooled_output": torch.tensor([1.0]), "marker": "first"}
+    conditionings = [
+        [[first, metadata]],
+        [[second, {"pooled_output": torch.tensor([2.0])}]],
+    ]
+
+    fused = _fuse_conditionings(conditionings, tokens, 2, 2, "spatial-checkerboard", 2, 0.5)
+    output, output_metadata = fused[0]
+
+    assert output.dtype == torch.float16
+    assert output.flatten().tolist() == [10, 10, 30, 30, 10, 20]
+    assert output_metadata == metadata
+    assert output_metadata is not metadata
+
+
+def test_flatten_images_uses_numeric_input_order_and_splits_batches():
+    images = {
+        "image_10": torch.full((1, 2, 2, 3), 10.0),
+        "image_2": torch.stack([torch.full((2, 2, 3), 2.0), torch.full((2, 2, 3), 3.0)]),
+        "image_1": torch.full((1, 2, 2, 3), 1.0),
+    }
+
+    sources = _flatten_images(images)
+    assert [source[0, 0, 0, 0].item() for source in sources] == [1.0, 2.0, 3.0, 10.0]
+
+
+def test_node_uses_custom_krea_prompt_and_returns_fused_conditioning():
+    class FakeClip:
+        def tokenize(self, text, images):
+            assert text.startswith("<|im_start|>system\nDescribe the image by detailing")
+            assert "Picture 1:" not in text
+            pairs = [
+                (1, 1.0),
+                ({"type": "image", "data": images[0]}, 1.0),
+                (2, 1.0),
+            ]
+            return {"qwen3vl_4b": [pairs]}
+
+        def encode_from_tokens_scheduled(self, tokens):
+            image = next(pair[0]["data"] for pair in tokens["qwen3vl_4b"][0] if isinstance(pair[0], dict))
+            value = image.mean()
+            return [[torch.full((1, 146, 1), value, dtype=torch.float16), {"source": float(value)}]]
+
+    result = TextEncodeQwenImageEditFusion.execute(
+        FakeClip(),
+        "test prompt",
+        {"image_1": torch.zeros(1, 32, 32, 3), "image_2": torch.ones(1, 32, 32, 3)},
+        "spatial-checkerboard",
+    )
+    conditioning = result.args[0]
+    output, metadata = conditioning[0]
+
+    assert output.dtype == torch.float16
+    assert output.shape == (1, 146, 1)
+    assert output[:, 0].item() == 0.0
+    assert output[:, -1].item() == 0.0
+    assert set(output[:, 1:-1].flatten().tolist()) == {0.0, 1.0}
+    assert metadata == {"source": 0.0}