feat(latent): add batch_size as 4th output from EmptyLatentBatchNode

Exposes batch_size as an output so downstream nodes can reference it
directly without needing a separate input.

kikotools/tools/empty_latent_batch/node.py

@@ -93,14 +93,14 @@ class EmptyLatentBatchNode(ComfyAssetsBaseNode):
             }
         }
 
-    RETURN_TYPES = ("LATENT", "INT", "INT")
-    RETURN_NAMES = ("latent", "width", "height")
+    RETURN_TYPES = ("LATENT", "INT", "INT", "INT")
+    RETURN_NAMES = ("latent", "width", "height", "batch_size")
     FUNCTION = "create_empty_latent"
     CATEGORY = "🫶 ComfyAssets/📦 Latents"
 
     def create_empty_latent(
         self, preset: str, width: int, height: int, batch_size: int
-    ) -> Tuple[Dict[str, torch.Tensor], int, int]:
+    ) -> Tuple[Dict[str, torch.Tensor], int, int, int]:
         """
         Create empty latent tensor with specified dimensions and batch size.
 
@@ -111,7 +111,7 @@ class EmptyLatentBatchNode(ComfyAssetsBaseNode):
             batch_size: Number of latents in the batch
 
         Returns:
-            Tuple containing (latent dictionary with 'samples' tensor, width, height)
+            Tuple containing (latent dict, width, height, batch_size)
         """
         try:
             # Extract original preset name from formatted string if needed
@@ -160,7 +160,7 @@ class EmptyLatentBatchNode(ComfyAssetsBaseNode):
                 f"(pixel dims: {final_width}×{final_height})"
             )
 
-            return (latent_dict, final_width, final_height)
+            return (latent_dict, final_width, final_height, batch_size)
 
         except Exception as e:
             # Handle any unexpected errors gracefully

tests/unit/tools/test_empty_latent_batch.py

@@ -131,8 +131,8 @@ class TestEmptyLatentBatchNode:
 
     def test_node_attributes(self):
         """Test node class attributes."""
-        assert EmptyLatentBatchNode.RETURN_TYPES == ("LATENT", "INT", "INT")
-        assert EmptyLatentBatchNode.RETURN_NAMES == ("latent", "width", "height")
+        assert EmptyLatentBatchNode.RETURN_TYPES == ("LATENT", "INT", "INT", "INT")
+        assert EmptyLatentBatchNode.RETURN_NAMES == ("latent", "width", "height", "batch_size")
         assert EmptyLatentBatchNode.FUNCTION == "create_empty_latent"
         assert EmptyLatentBatchNode.CATEGORY == "🫶 ComfyAssets/📦 Latents"
 
@@ -141,13 +141,14 @@ class TestEmptyLatentBatchNode:
         result = self.node.create_empty_latent("custom", 512, 512, 1)
 
         assert isinstance(result, tuple)
-        assert len(result) == 3  # Now returns (latent, width, height)
+        assert len(result) == 4  # Returns (latent, width, height, batch_size)
 
-        latent_dict, width, height = result
+        latent_dict, width, height, batch_size = result
         assert isinstance(latent_dict, dict)
         assert "samples" in latent_dict
         assert width == 512
         assert height == 512
+        assert batch_size == 1
 
         samples = latent_dict["samples"]
         assert isinstance(samples, torch.Tensor)
@@ -155,12 +156,13 @@ class TestEmptyLatentBatchNode:
 
     def test_create_empty_latent_with_batch(self):
         """Test empty latent creation with batch size."""
-        batch_size = 3
-        result = self.node.create_empty_latent("custom", 1024, 768, batch_size)
+        input_batch_size = 3
+        result = self.node.create_empty_latent("custom", 1024, 768, input_batch_size)
 
-        latent_dict, width, height = result
+        latent_dict, width, height, batch_size = result
         assert width == 1024
         assert height == 768
+        assert batch_size == 3
         samples = latent_dict["samples"]
         assert samples.shape == (3, 4, 96, 128)  # batch=3, 768/8=96, 1024/8=128
 
@@ -169,10 +171,11 @@ class TestEmptyLatentBatchNode:
         # Input dimensions not divisible by 8
         result = self.node.create_empty_latent("custom", 513, 515, 1)
 
-        latent_dict, width, height = result
+        latent_dict, width, height, batch_size = result
         # Dimensions should be rounded UP to nearest multiple of 8
         assert width == 520  # 513 -> 520
         assert height == 520  # 515 -> 520
+        assert batch_size == 1
         samples = latent_dict["samples"]
         # Should be adjusted to 520x520 -> 65x65 latent
         assert samples.shape == (1, 4, 65, 65)