diff --git a/ACL_PyTorch/built-in/cv/SAM/README.md b/ACL_PyTorch/built-in/cv/SAM/README.md
index 4ca3afa1f55cc9977408741eddf3a8e66c4a0e2a..7e0577be265a77f3f4f40aed9f7e61e7cfeac751 100644
--- a/ACL_PyTorch/built-in/cv/SAM/README.md
+++ b/ACL_PyTorch/built-in/cv/SAM/README.md
@@ -78,7 +78,7 @@ cd ModelZoo-PyTorch/ACL_PyTorch/built-in/cv/SAM
 git clone https://github.com/facebookresearch/segment-anything.git
 cd segment-anything
 git reset --hard 6fdee8f2727f4506cfbbe553e23b895e27956588
-patch -p2 < ../segment_anything_diff.patch
+git apply ../segment_anything_diff.patch
 pip3 install -e .
 cd ..
 ```
diff --git a/ACL_PyTorch/built-in/cv/SAM/requirements.txt b/ACL_PyTorch/built-in/cv/SAM/requirements.txt
index 6722adb464f8d1cbc54c229ff0e6e974124339b4..b4969b22635980cd34e286a64d4bbf4968e918c1 100644
--- a/ACL_PyTorch/built-in/cv/SAM/requirements.txt
+++ b/ACL_PyTorch/built-in/cv/SAM/requirements.txt
@@ -1,5 +1,5 @@
 torch==2.1.0
-torch_npu==2.1.0.post17.dev20250905
+torch_npu==2.1.0.post10
 torchvision==0.16.0
 torchaudio==2.1.0
 decorator
diff --git a/ACL_PyTorch/built-in/cv/SAM/segment_anything_diff.patch b/ACL_PyTorch/built-in/cv/SAM/segment_anything_diff.patch
index aec413383ecb17bc2ed7eb15def82ad337b44ff7..96284944bc9f131e8c6f4819e25304bcc553f8a2 100644
--- a/ACL_PyTorch/built-in/cv/SAM/segment_anything_diff.patch
+++ b/ACL_PyTorch/built-in/cv/SAM/segment_anything_diff.patch
@@ -1,6 +1,7 @@
-diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/scripts/export_onnx_model.py
---- a/segment-anything/scripts/export_onnx_model.py	2023-11-13 16:25:26.000000000 +0800
-+++ b/segment-anything/scripts/export_onnx_model.py	2023-11-18 16:15:20.088025762 +0800
+diff --git a/scripts/export_onnx_model.py b/scripts/export_onnx_model.py
+index 5c6f838..0bfaff2 100644
+--- a/scripts/export_onnx_model.py
++++ b/scripts/export_onnx_model.py
 @@ -6,8 +6,12 @@
  
  import torch
@@ -14,7 +15,7 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
  
  import argparse
  import warnings
-@@ -24,11 +28,30 @@
+@@ -24,11 +28,30 @@ parser = argparse.ArgumentParser(
  )
  
  parser.add_argument(
@@ -47,7 +48,7 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
  )
  
  parser.add_argument(
-@@ -56,11 +79,21 @@
+@@ -56,11 +79,21 @@ parser.add_argument(
  )
  
  parser.add_argument(
@@ -71,7 +72,7 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
          "Quantization is performed with quantize_dynamic from onnxruntime.quantization.quantize."
      ),
  )
-@@ -97,7 +130,9 @@
+@@ -97,7 +130,9 @@ parser.add_argument(
  def run_export(
      model_type: str,
      checkpoint: str,
@@ -82,7 +83,7 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
      opset: int,
      return_single_mask: bool,
      gelu_approximate: bool = False,
-@@ -107,6 +142,74 @@
+@@ -107,6 +142,74 @@ def run_export(
      print("Loading model...")
      sam = sam_model_registry[model_type](checkpoint=checkpoint)
  
@@ -157,7 +158,7 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
      onnx_model = SamOnnxModel(
          model=sam,
          return_single_mask=return_single_mask,
-@@ -129,16 +232,17 @@
+@@ -129,16 +232,17 @@ def run_export(
      mask_input_size = [4 * x for x in embed_size]
      dummy_inputs = {
          "image_embeddings": torch.randn(1, embed_dim, *embed_size, dtype=torch.float),
@@ -178,7 +179,7 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
  
      with warnings.catch_warnings():
          warnings.filterwarnings("ignore", category=torch.jit.TracerWarning)
-@@ -164,7 +268,7 @@
+@@ -164,7 +268,7 @@ def run_export(
          providers = ["CPUExecutionProvider"]
          ort_session = onnxruntime.InferenceSession(output, providers=providers)
          _ = ort_session.run(None, ort_inputs)
@@ -187,7 +188,7 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
  
  
  def to_numpy(tensor):
-@@ -176,7 +280,9 @@
+@@ -176,7 +280,9 @@ if __name__ == "__main__":
      run_export(
          model_type=args.model_type,
          checkpoint=args.checkpoint,
@@ -198,7 +199,7 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
          opset=args.opset,
          return_single_mask=args.return_single_mask,
          gelu_approximate=args.gelu_approximate,
-@@ -184,18 +290,34 @@
+@@ -184,18 +290,34 @@ if __name__ == "__main__":
          return_extra_metrics=args.return_extra_metrics,
      )
  
@@ -238,10 +239,11 @@ diff -Naru a/segment-anything/scripts/export_onnx_model.py b/segment-anything/sc
 +        )
 +        print("Done!")
 \ No newline at end of file
-diff -Naru a/segment-anything/segment_anything/modeling/image_encoder.py b/segment-anything/segment_anything/modeling/image_encoder.py
---- a/segment-anything/segment_anything/modeling/image_encoder.py	2023-11-13 16:25:26.000000000 +0800
-+++ b/segment-anything/segment_anything/modeling/image_encoder.py	2023-11-13 19:26:32.000000000 +0800
-@@ -253,8 +253,8 @@
+diff --git a/segment_anything/modeling/image_encoder.py b/segment_anything/modeling/image_encoder.py
+index 66351d9..31d622c 100644
+--- a/segment_anything/modeling/image_encoder.py
++++ b/segment_anything/modeling/image_encoder.py
+@@ -253,8 +253,8 @@ def window_partition(x: torch.Tensor, window_size: int) -> Tuple[torch.Tensor, T
      """
      B, H, W, C = x.shape
  
@@ -252,7 +254,7 @@ diff -Naru a/segment-anything/segment_anything/modeling/image_encoder.py b/segme
      if pad_h > 0 or pad_w > 0:
          x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
      Hp, Wp = H + pad_h, W + pad_w
-@@ -322,6 +322,15 @@
+@@ -322,6 +322,15 @@ def get_rel_pos(q_size: int, k_size: int, rel_pos: torch.Tensor) -> torch.Tensor
      return rel_pos_resized[relative_coords.long()]
  
  
@@ -268,7 +270,7 @@ diff -Naru a/segment-anything/segment_anything/modeling/image_encoder.py b/segme
  def add_decomposed_rel_pos(
      attn: torch.Tensor,
      q: torch.Tensor,
-@@ -351,8 +360,8 @@
+@@ -351,8 +360,8 @@ def add_decomposed_rel_pos(
  
      B, _, dim = q.shape
      r_q = q.reshape(B, q_h, q_w, dim)
@@ -279,10 +281,33 @@ diff -Naru a/segment-anything/segment_anything/modeling/image_encoder.py b/segme
  
      attn = (
          attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]
-diff -Naru a/segment-anything/segment_anything/utils/onnx.py b/segment-anything/segment_anything/utils/onnx.py
---- a/segment-anything/segment_anything/utils/onnx.py	2023-11-13 16:25:26.000000000 +0800
-+++ b/segment-anything/segment_anything/utils/onnx.py	2023-11-18 16:14:01.512027850 +0800
-@@ -112,7 +112,6 @@
+diff --git a/segment_anything/modeling/mask_decoder.py b/segment_anything/modeling/mask_decoder.py
+index 5d2fdb0..ee8da94 100644
+--- a/segment_anything/modeling/mask_decoder.py
++++ b/segment_anything/modeling/mask_decoder.py
+@@ -123,9 +123,15 @@ class MaskDecoder(nn.Module):
+         tokens = torch.cat((output_tokens, sparse_prompt_embeddings), dim=1)
+ 
+         # Expand per-image data in batch direction to be per-mask
+-        src = torch.repeat_interleave(image_embeddings, tokens.shape[0], dim=0)
++        N = tokens.shape[0]
++        B, C, H, W = image_embeddings.shape
++        src = image_embeddings.unsqueeze(1).expand(B, N, C, H, W).reshape(B * N, C, H, W)
++
+         src = src + dense_prompt_embeddings
+-        pos_src = torch.repeat_interleave(image_pe, tokens.shape[0], dim=0)
++
++        B, C, H, W = image_pe.shape
++        pos_src = image_pe.unsqueeze(1).expand(B, N, C, H, W).reshape(B * N, C, H, W)
++
+         b, c, h, w = src.shape
+ 
+         # Run the transformer
+diff --git a/segment_anything/utils/onnx.py b/segment_anything/utils/onnx.py
+index 3196bdf..e718afc 100644
+--- a/segment_anything/utils/onnx.py
++++ b/segment_anything/utils/onnx.py
+@@ -112,7 +112,6 @@ class SamOnnxModel(nn.Module):
          point_labels: torch.Tensor,
          mask_input: torch.Tensor,
          has_mask_input: torch.Tensor,
@@ -290,7 +315,7 @@ diff -Naru a/segment-anything/segment_anything/utils/onnx.py b/segment-anything/
      ):
          sparse_embedding = self._embed_points(point_coords, point_labels)
          dense_embedding = self._embed_masks(mask_input, has_mask_input)
-@@ -131,14 +130,4 @@
+@@ -131,14 +130,4 @@ class SamOnnxModel(nn.Module):
  
          if self.return_single_mask:
              masks, scores = self.select_masks(masks, scores, point_coords.shape[1])
@@ -306,3 +331,4 @@ diff -Naru a/segment-anything/segment_anything/utils/onnx.py b/segment-anything/
 -
 -        return upscaled_masks, scores, masks
 +        return scores, masks
+