diff --git a/scripts/pag.py b/scripts/pag.py
index bacffe9..1e96a26 100644
--- a/scripts/pag.py
+++ b/scripts/pag.py
@@ -333,11 +333,11 @@ def pag_pre_hook(module, input, kwargs, output):
                                 # oops we forgot to unhook
                                 return
 
-                        batch_size, seq_len, inner_dim = output.shape
-                        identity = torch.eye(seq_len).expand(batch_size, -1, -1).to(shared.device)
-
                         # get the last to_v output and save it
                         last_to_v = getattr(module, 'pag_last_to_v', None)
+
+                        batch_size, seq_len, inner_dim = output.shape
+                        identity = torch.eye(seq_len, dtype=last_to_v.dtype, device=shared.device).expand(batch_size, -1, -1)
                         if last_to_v is not None:    
                                 new_output = torch.einsum('bij,bjk->bik', identity, last_to_v)
                                 return new_output
@@ -836,4 +836,4 @@ def _remove_child_hooks(
     _remove_child_hooks(module, hook_fn_name)
 
     # Remove hooks from the target module
-    _remove_hooks(module, hook_fn_name)
\ No newline at end of file
+    _remove_hooks(module, hook_fn_name)
diff --git a/scripts/t2i_zero.py b/scripts/t2i_zero.py
index d768916..35ed728 100644
--- a/scripts/t2i_zero.py
+++ b/scripts/t2i_zero.py
@@ -407,15 +407,16 @@ def ready_hijack_forward(self, alpha, width, height, ema_factor, step_start, ste
                 plot_num = 0
                 for module in cross_attn_modules:
                         self.add_field_cross_attn_modules(module, 't2i0_last_attn_map', None)
-                        self.add_field_cross_attn_modules(module, 't2i0_step', torch.tensor([-1]).to(device=shared.device))
-                        self.add_field_cross_attn_modules(module, 't2i0_step_start', torch.tensor([step_start]).to(device=shared.device))
-                        self.add_field_cross_attn_modules(module, 't2i0_step_end', torch.tensor([step_end]).to(device=shared.device))
+                        self.add_field_cross_attn_modules(module, 't2i0_step', int(-1))
+                        self.add_field_cross_attn_modules(module, 't2i0_step_start', int(step_start))
+                        self.add_field_cross_attn_modules(module, 't2i0_step_end', int(step_end))
                         self.add_field_cross_attn_modules(module, 't2i0_ema', None)
-                        self.add_field_cross_attn_modules(module, 't2i0_ema_factor', torch.tensor([ema_factor]).to(device=shared.device, dtype=torch.float16))
-                        self.add_field_cross_attn_modules(module, 'plot_num', torch.tensor([plot_num]).to(device=shared.device))
+                        self.add_field_cross_attn_modules(module, 't2i0_ema_factor', float(ema_factor))
+                        self.add_field_cross_attn_modules(module, 'plot_num', int(plot_num))
                         self.add_field_cross_attn_modules(module, 't2i0_to_v_map', None)
                         self.add_field_cross_attn_modules(module.to_v, 't2i0_parent_module', [module])
-                        self.add_field_cross_attn_modules(module, 't2i0_token_count', torch.tensor(token_count).to(device=shared.device, dtype=torch.int64))
+                        self.add_field_cross_attn_modules(module, 't2i0_token_count', int(token_count))
+                        self.add_field_cross_attn_modules(module, 'gaussian_blur', GaussianBlur(kernel_size=3, sigma=1).to(device=shared.device))
                         if tokens is not None:
                                 self.add_field_cross_attn_modules(module, 't2i0_tokens', torch.tensor(tokens).to(device=shared.device, dtype=torch.int64))
                         else:
@@ -476,9 +477,9 @@ def cross_token_non_maximum_suppression(module, input, kwargs, output):
                         attention_map = output.view(batch_size, downscale_height, downscale_width, inner_dim)
 
                         if token_indices is None:
-                                selected_tokens = torch.tensor(list(range(1, token_count.item())))
+                                selected_tokens = torch.arange(1, token_count, device=output.device)
                         elif len(token_indices) == 0:
-                                selected_tokens = torch.tensor(list(range(1, token_count.item())))
+                                selected_tokens = torch.arange(1, token_count, device=output.device)
                         else:
                                 selected_tokens = module.t2i0_tokens
 
@@ -490,7 +491,7 @@ def cross_token_non_maximum_suppression(module, input, kwargs, output):
 
                         # Extract and process the selected attention maps
                         # GaussianBlur expects the input [..., C, H, W]
-                        gaussian_blur = GaussianBlur(kernel_size=3, sigma=1)
+                        gaussian_blur = module.gaussian_blur
                         AC = AC.permute(0, 3, 1, 2)
                         AC = gaussian_blur(AC)  # Applying Gaussian smoothing
                         AC = AC.permute(0, 2, 3, 1)