diff --git a/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py b/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py
index 669b2b691..5579ababe 100644
--- a/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py
+++ b/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py
@@ -26,7 +26,7 @@ def main():
     )
 
     sampling_param = SamplingParam.from_pretrained("FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers")
-    sampling_param.num_frames = 73
+    sampling_param.num_frames = 81
     sampling_param.width = 832
     sampling_param.height = 480
     sampling_param.seed = 1000
diff --git a/fastvideo/pipelines/stages/causal_denoising.py b/fastvideo/pipelines/stages/causal_denoising.py
index f7b612ee9..777314547 100644
--- a/fastvideo/pipelines/stages/causal_denoising.py
+++ b/fastvideo/pipelines/stages/causal_denoising.py
@@ -400,6 +400,10 @@ class CausalDMDDenosingStage(DenoisingStage):
 
                 start_index += current_num_frames
 
+        if boundary_timestep is not None:
+            num_frames_to_remove = self.num_frames_per_block - 1
+            latents = latents[:, :, :-num_frames_to_remove, :, :]
+
         batch.latents = latents
         return batch
 
diff --git a/fastvideo/pipelines/stages/input_validation.py b/fastvideo/pipelines/stages/input_validation.py
index 180ab4e71..20e4e949e 100644
--- a/fastvideo/pipelines/stages/input_validation.py
+++ b/fastvideo/pipelines/stages/input_validation.py
@@ -120,14 +120,14 @@ class InputValidationStage(PipelineStage):
             scale = max(ow / iw, oh / ih)
             img = img.resize((round(iw * scale), round(ih * scale)),
                              Image.LANCZOS)
-            logger.info("resized img height: %s, img width: %s", img.height,
-                        img.width)
 
             # center-crop
             x1 = (img.width - ow) // 2
             y1 = (img.height - oh) // 2
             img = img.crop((x1, y1, x1 + ow, y1 + oh))
             assert img.width == ow and img.height == oh
+            logger.info("final processed img height: %s, img width: %s", img.height,
+                        img.width)
 
             # to tensor
             img = TF.to_tensor(img).sub_(0.5).div_(0.5).to(