diff --git a/cogvideox_fun/pipeline_cogvideox_control.py b/cogvideox_fun/pipeline_cogvideox_control.py
index 7130d50..5dcda4a 100644
--- a/cogvideox_fun/pipeline_cogvideox_control.py
+++ b/cogvideox_fun/pipeline_cogvideox_control.py
@@ -444,6 +444,9 @@ class CogVideoX_Fun_Pipeline_Control(VideoSysPipeline):
         callback_on_step_end_tensor_inputs: List[str] = ["latents"],
         max_sequence_length: int = 226,
         comfyui_progressbar: bool = False,
+        control_strength: float = 1.0,
+        control_start_percent: float = 0.0,
+        control_end_percent: float = 1.0,
     ) -> Union[CogVideoX_Fun_PipelineOutput, Tuple]:
         """
         Function invoked when calling the pipeline for generation.
@@ -610,6 +613,8 @@ class CogVideoX_Fun_Pipeline_Control(VideoSysPipeline):
         )
         control_latents = rearrange(control_video_latents_input, "b c f h w -> b f c h w")
 
+        control_latents = control_latents * control_strength
+
         if comfyui_progressbar:
             pbar.update(1)
 
@@ -636,6 +641,13 @@ class CogVideoX_Fun_Pipeline_Control(VideoSysPipeline):
                 latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                 latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
 
+                # Calculate the current step percentage
+                current_step_percentage = i / num_inference_steps
+
+                # Determine if control_latents should be applied
+                apply_control = control_start_percent <= current_step_percentage <= control_end_percent
+                current_control_latents = control_latents if apply_control else torch.zeros_like(control_latents)
+
                 # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
                 timestep = t.expand(latent_model_input.shape[0])
 
@@ -646,7 +658,7 @@ class CogVideoX_Fun_Pipeline_Control(VideoSysPipeline):
                     timestep=timestep,
                     image_rotary_emb=image_rotary_emb,
                     return_dict=False,
-                    control_latents=control_latents,
+                    control_latents=current_control_latents,
                 )[0]
                 noise_pred = noise_pred.float()
 
diff --git a/nodes.py b/nodes.py
index ed74095..62529ec 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1105,6 +1105,9 @@ class CogVideoXFunVid2VidSampler:
              "optional":{
                 "validation_video": ("IMAGE",),
                 "control_video": ("IMAGE",),
+                "control_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "control_start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "control_end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
             },
         }
     
@@ -1113,7 +1116,8 @@ class CogVideoXFunVid2VidSampler:
     FUNCTION = "process"
     CATEGORY = "CogVideoWrapper"
 
-    def process(self, pipeline, positive, negative, video_length, base_resolution, seed, steps, cfg, denoise_strength, scheduler, validation_video=None, control_video=None):
+    def process(self, pipeline, positive, negative, video_length, base_resolution, seed, steps, cfg, denoise_strength, scheduler, 
+                validation_video=None, control_video=None, control_strength=1.0, control_start_percent=0.0, control_end_percent=1.0):
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
         pipe = pipeline["pipe"]
@@ -1177,7 +1181,10 @@ class CogVideoXFunVid2VidSampler:
             if control_video is not None:
                 latents = pipe(
                     **common_params,
-                    control_video=input_video
+                    control_video=input_video,
+                    control_strength=control_strength,
+                    control_start_percent=control_start_percent,
+                    control_end_percent=control_end_percent
                 )
             else:
                 latents = pipe(