diff --git a/gradio_server.py b/gradio_server.py
index 0ad2d8c..b61c326 100644
--- a/gradio_server.py
+++ b/gradio_server.py
@@ -223,7 +223,7 @@ def _parse_args():
         "--res",
         type=str,
         default="480p",
-        choices=["480p", "720p", "823p", "1024p", "1280p"],
+        choices=["480p", "720p", "832p", "1024p", "1280p"],
         help="Default resolution for the video (480p, 720p, 823p, 1024p or 1280p)"
     )
 
@@ -730,7 +730,7 @@ def apply_changes(  state,
     text_encoder_filename = server_config["text_encoder_filename"]
     vae_config = server_config["vae_config"]
     boost = server_config["boost"]
-    if  all(change in ["attention_mode", "vae_config", "default_ui", "boost"] for change in changes ):
+    if  all(change in ["attention_mode", "vae_config", "default_ui", "boost", "save_path", "metadata_choice"] for change in changes ):
         if "attention_mode" in changes:
             pass
 
@@ -1218,7 +1218,7 @@ def generate_video(
                 gc.collect()
                 torch.cuda.empty_cache()
                 s = str(e)
-                keyword_list = ["vram", "VRAM", "memory", "triton", "cuda", "allocat"]
+                keyword_list = ["vram", "VRAM", "memory","allocat"]
                 VRAM_crash= False
                 if any( keyword in s for keyword in keyword_list):
                     VRAM_crash = True
@@ -1895,7 +1895,7 @@ def create_demo():
                                 # ("624x832 (3:4, 540p)", "624x832"),
                                 # ("720x720 (1:1, 540p)", "720x720"),
                             ],
-                            value={"480p": "832x480","720p": "1280x720","823p": "480x832","1024p": "1024x1024","1280p": "720x1280",}.get(res, "832x480"),
+                            value={"480p": "832x480","720p": "1280x720","832p": "480x832","1024p": "1024x1024","1280p": "720x1280",}.get(res, "832x480"),
                             label="Resolution"
                         )
 
@@ -1930,7 +1930,7 @@ def create_demo():
                         with gr.Row():
                             negative_prompt = gr.Textbox(label="Negative Prompt", value="")
                         with gr.Column(visible = len(loras)>0) as loras_column:
-                            gr.Markdown("<B>Loras can be used to create special effects on the video by mentioned a trigger word in the Prompt. You can save Loras combinations in presets.</B>")
+                            gr.Markdown("<B>Loras can be used to create special effects on the video by mentioning a trigger word in the Prompt. You can save Loras combinations in presets.</B>")
                             loras_choices = gr.Dropdown(
                                 choices=[
                                     (lora_name, str(i) ) for i, lora_name in enumerate(loras_names)
diff --git a/wan/modules/sage2_core.py b/wan/modules/sage2_core.py
index c4dddc7..d83d255 100644
--- a/wan/modules/sage2_core.py
+++ b/wan/modules/sage2_core.py
@@ -137,9 +137,10 @@ def sageattn(
 
 @torch.compiler.disable
 def sageattn_qk_int8_pv_fp16_triton(
-    q: torch.Tensor, 
-    k: torch.Tensor, 
-    v: torch.Tensor, 
+    qkv_list,
+    # q: torch.Tensor, 
+    # k: torch.Tensor, 
+    # v: torch.Tensor, 
     tensor_layout: str = "HND",
     quantization_backend: str = "triton",
     is_causal: bool =False, 
@@ -211,7 +212,8 @@ def sageattn_qk_int8_pv_fp16_triton(
     - All tensors must be on the same cuda device.
     - `smooth_k` will introduce slight overhead but will improve the accuracy under most circumstances.
     """
-
+    q, k, v = qkv_list
+    qkv_list.clear()
     dtype = q.dtype
     assert q.is_cuda, "Input tensors must be on cuda."
     assert dtype in [torch.float16, torch.bfloat16], "Input tensors must be in dtype of torch.float16 or torch.bfloat16"
@@ -266,6 +268,8 @@ def sageattn_qk_int8_pv_fp16_triton(
         q_int8, q_scale, k_int8, k_scale = per_block_int8_cuda(q, k, km=km, sm_scale=sm_scale, tensor_layout=tensor_layout)
     else:
         raise ValueError(f"Unsupported quantization backend: {quantization_backend}")
+    del q,k, km
+
     if is_causal:
         o, lse = attn_true(q_int8, k_int8, v, q_scale, k_scale, tensor_layout=tensor_layout, output_dtype=dtype, return_lse=return_lse)
     else: