diff --git a/diffsynth/pipelines/flux_image.py b/diffsynth/pipelines/flux_image.py
index 5fa807d..c17e182 100644
--- a/diffsynth/pipelines/flux_image.py
+++ b/diffsynth/pipelines/flux_image.py
@@ -362,7 +362,7 @@ class FluxImagePipeline(BasePipeline):
             return {}, controlnet_image
         
         
-    def prepare_flex_kwargs(self, latents, flex_inpaint_image=None, flex_inpaint_mask=None, flex_control_image=None, tiled=False, tile_size=64, tile_stride=32):
+    def prepare_flex_kwargs(self, latents, flex_inpaint_image=None, flex_inpaint_mask=None, flex_control_image=None, flex_control_strength=0.5, flex_control_stop=0.5, tiled=False, tile_size=64, tile_stride=32):
         if self.dit.input_dim == 196:
             if flex_inpaint_image is None:
                 flex_inpaint_image = torch.zeros_like(latents)
@@ -375,13 +375,16 @@ class FluxImagePipeline(BasePipeline):
                 flex_inpaint_mask = flex_inpaint_mask.resize((latents.shape[3], latents.shape[2]))
                 flex_inpaint_mask = self.preprocess_image(flex_inpaint_mask).to(device=self.device, dtype=self.torch_dtype)
                 flex_inpaint_mask = (flex_inpaint_mask[:, 0:1, :, :] + 1) / 2
+            flex_inpaint_image = flex_inpaint_image * (1 - flex_inpaint_mask)
             if flex_control_image is None:
                 flex_control_image = torch.zeros_like(latents)
             else:
                 flex_control_image = self.preprocess_image(flex_control_image).to(device=self.device, dtype=self.torch_dtype)
-                flex_control_image = self.encode_image(flex_control_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride)
+                flex_control_image = self.encode_image(flex_control_image, tiled=tiled, tile_size=tile_size, tile_stride=tile_stride) * flex_control_strength
             flex_condition = torch.concat([flex_inpaint_image, flex_inpaint_mask, flex_control_image], dim=1)
-            flex_kwargs = {"flex_condition": flex_condition}
+            flex_uncondition = torch.concat([flex_inpaint_image, flex_inpaint_mask, torch.zeros_like(flex_control_image)], dim=1)
+            flex_control_stop_timestep = self.scheduler.timesteps[int(flex_control_stop * (len(self.scheduler.timesteps) - 1))]
+            flex_kwargs = {"flex_condition": flex_condition, "flex_uncondition": flex_uncondition, "flex_control_stop_timestep": flex_control_stop_timestep}
         else:
             flex_kwargs = {}
         return flex_kwargs
@@ -427,6 +430,8 @@ class FluxImagePipeline(BasePipeline):
         flex_inpaint_image=None,
         flex_inpaint_mask=None,
         flex_control_image=None,
+        flex_control_strength=0.5,
+        flex_control_stop=0.5,
         # TeaCache
         tea_cache_l1_thresh=None,
         # Tile
@@ -635,6 +640,8 @@ def lets_dance_flux(
     id_emb=None,
     infinityou_guidance=None,
     flex_condition=None,
+    flex_uncondition=None,
+    flex_control_stop_timestep=None,
     tea_cache: TeaCache = None,
     **kwargs
 ):
@@ -686,8 +693,12 @@ def lets_dance_flux(
             controlnet_frames, **controlnet_extra_kwargs
         )
         
+    # Flex
     if flex_condition is not None:
-        hidden_states = torch.concat([hidden_states, flex_condition], dim=1)
+        if timestep.tolist()[0] >= flex_control_stop_timestep:
+            hidden_states = torch.concat([hidden_states, flex_condition], dim=1)
+        else:
+            hidden_states = torch.concat([hidden_states, flex_uncondition], dim=1)
 
     if image_ids is None:
         image_ids = dit.prepare_image_ids(hidden_states)
diff --git a/examples/image_synthesis/flex_text_to_image.py b/examples/image_synthesis/flex_text_to_image.py
index 5a56923..3770764 100644
--- a/examples/image_synthesis/flex_text_to_image.py
+++ b/examples/image_synthesis/flex_text_to_image.py
@@ -23,22 +23,27 @@ image = pipe(
 image.save("image_1.jpg")
 
 mask = np.zeros((1024, 1024, 3), dtype=np.uint8)
-mask[0:300, 300:800] = 255
+mask[200:400, 400:700] = 255
 mask = Image.fromarray(mask)
 mask.save("image_mask.jpg")
 
-inpaint_image = np.array(image)
-inpaint_image[0:300, 300:800] = 0
-inpaint_image = Image.fromarray(inpaint_image)
-inpaint_image.save("image_inpaint.jpg")
+inpaint_image = image
+
+image = pipe(
+    prompt="portrait of a beautiful Asian girl with sunglasses, long hair, red t-shirt, sunshine, beach",
+    num_inference_steps=50, embedded_guidance=3.5,
+    flex_inpaint_image=inpaint_image, flex_inpaint_mask=mask,
+    seed=4
+)
+image.save("image_2.jpg")
 
 control_image = Annotator("canny")(image)
 control_image.save("image_control.jpg")
 
 image = pipe(
-    prompt="portrait of a beautiful Asian girl, long hair, yellow t-shirt, sunshine, beach",
+    prompt="portrait of a beautiful Asian girl with sunglasses, long hair, yellow t-shirt, sunshine, beach",
     num_inference_steps=50, embedded_guidance=3.5,
     flex_control_image=control_image,
     seed=4
 )
-image.save("image_2.jpg")
+image.save("image_3.jpg")