diff --git a/py/easyNodes.py b/py/easyNodes.py
index 4264d6e..3446215 100644
--- a/py/easyNodes.py
+++ b/py/easyNodes.py
@@ -2217,7 +2217,8 @@ class icLightApply:
                 "image": ("IMAGE",),
                 "vae": ("VAE",),
                 "lighting": (['None', 'Left Light', 'Right Light', 'Top Light', 'Bottom Light', 'Circle Light'],{"default": "None"}),
-                "source": (['Use Background Image', 'Use Flipped Background Image', 'Left Light', 'Right Light', 'Top Light', 'Bottom Light', 'Ambient'],{"default": "Use Background Image"})
+                "source": (['Use Background Image', 'Use Flipped Background Image', 'Left Light', 'Right Light', 'Top Light', 'Bottom Light', 'Ambient'],{"default": "Use Background Image"}),
+                "remove_bg": ("BOOLEAN", {"default": True}),
             },
         }
 
@@ -2243,16 +2244,20 @@ class icLightApply:
             image, _ = results['result']
             return image
 
-    def apply(self, mode, model, image, vae, lighting, source):
+    def apply(self, mode, model, image, vae, lighting, source, remove_bg):
         model_type = get_sd_version(model)
         if model_type == 'sdxl':
             raise Exception("IC Light model is not supported for SDXL now")
 
-        batch_size = image.shape[0]
-        if image.shape[3] == 3:
+        batch_size, height, width, channel = image.shape
+        if channel == 3:
             # remove bg
             if mode == 'Foreground' or batch_size == 1:
-                image = self.removebg(image)
+                if remove_bg:
+                    image = self.removebg(image)
+                else:
+                    mask = torch.full((1, height, width), 1.0, dtype=torch.float32, device="cpu")
+                    image, = JoinImageWithAlpha().join_image_with_alpha(image, mask)
 
         iclight = ICLight()
         if mode == 'Foreground':
diff --git a/py/ic_light/func.py b/py/ic_light/func.py
index ae301e0..5ef68d2 100644
--- a/py/ic_light/func.py
+++ b/py/ic_light/func.py
@@ -35,18 +35,17 @@ class VAEEncodeArgMax(VAEEncode):
 class ICLight:
 
     @staticmethod
-    def apply_c_concat(cond, uncond, c_concat: torch.Tensor):
-        def write_c_concat(cond):
-            new_cond = []
-            for t in cond:
-                n = [t[0], t[1].copy()]
-                if "model_conds" not in n[1]:
-                    n[1]["model_conds"] = {}
-                n[1]["model_conds"]["c_concat"] = CONDRegular(c_concat)
-                new_cond.append(n)
-            return new_cond
-
-        return (write_c_concat(cond), write_c_concat(uncond))
+    def apply_c_concat(params: UnetParams, concat_conds) -> UnetParams:
+        """Apply c_concat on unet call."""
+        sample = params["input"]
+        params["c"]["c_concat"] = torch.cat(
+            (
+                    [concat_conds.to(sample.device)]
+                    * (sample.shape[0] // concat_conds.shape[0])
+            ),
+            dim=0,
+        )
+        return params
 
     @staticmethod
     def create_custom_conv(
@@ -161,20 +160,19 @@ class ICLight:
         # [1, 4 * B, H, W]
         concat_conds = torch.cat([c[None, ...] for c in concat_conds], dim=1)
 
+        def unet_dummy_apply(unet_apply: Callable, params: UnetParams):
+            """A dummy unet apply wrapper serving as the endpoint of wrapper
+            chain."""
+            return unet_apply(x=params["input"], t=params["timestep"], **params["c"])
 
-        def wrapped_unet(unet_apply: Callable, params: UnetParams):
-            # Apply concat.
-            sample = params["input"]
-            params["c"]["c_concat"] = torch.cat(
-                (
-                    [concat_conds.to(sample.device)]
-                    * (sample.shape[0] // concat_conds.shape[0])
-                ),
-                dim=0,
-            )
-            return unet_apply(x=sample, t=params["timestep"], **params["c"])
+        existing_wrapper = work_model.model_options.get(
+            "model_function_wrapper", unet_dummy_apply
+        )
 
-        work_model.set_model_unet_function_wrapper(wrapped_unet)
+        def wrapper_func(unet_apply: Callable, params: UnetParams):
+            return existing_wrapper(unet_apply, params=self.apply_c_concat(params, concat_conds))
+
+        work_model.set_model_unet_function_wrapper(wrapper_func)
         ic_model = load_unet(ic_model_path)
         ic_model_state_dict = ic_model.model.diffusion_model.state_dict()
 
diff --git a/web/js/easy/easyDynamicWidgets.js b/web/js/easy/easyDynamicWidgets.js
index 4f74ad1..03a777a 100644
--- a/web/js/easy/easyDynamicWidgets.js
+++ b/web/js/easy/easyDynamicWidgets.js
@@ -128,10 +128,12 @@ function widgetLogic(node, widget) {
 			case 'easy icLightApply':
 				if (widget.value === "Foreground") {
 					toggleWidget(node, findWidgetByName(node, 'lighting'), true)
+					toggleWidget(node, findWidgetByName(node, 'remove_bg'), true)
 					toggleWidget(node, findWidgetByName(node, 'source'))
 				} else {
 					toggleWidget(node, findWidgetByName(node, 'lighting'))
 					toggleWidget(node, findWidgetByName(node, 'source'), true)
+					toggleWidget(node, findWidgetByName(node, 'remove_bg'))
 				}
 				break
 		}