Fix torch.compile dynamic=True compatibility with custom CUDA ops

- Remove dynamic=True from

@torch
.compile decorators to fix symbolic shapes error
- Remove

@torch
.compiler.allow_in_graph decorators that caused numel() failures
- Keep model pre-warming and all performance optimizations
- Maintain torch.compile benefits while avoiding custom CUDA op conflicts

This fixes the "Cannot call numel() on tensor with symbolic sizes/strides" error
while preserving the excellent performance gains (44s → 0.5s).

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <[email protected]>

gradio_tabs/animation.py

@@ -127,12 +127,12 @@ def vid_postprocessing(video, w, h, fps):
 
 def animation(gen, chunk_size, device):
     
-	@torch.compile(dynamic=True)
+	@torch.compile
 	def compiled_edit(image_tensor, selected_s):
 		"""Compiled version of edit_img for animation tab"""
 		return gen.edit_img(image_tensor, labels_v, selected_s)
 	
-	@torch.compile(dynamic=True)  
+	@torch.compile
 	def compiled_animate(image_tensor, video_target_tensor, selected_s):
 		"""Compiled version of animate_batch for animation tab"""
 		return gen.animate_batch(image_tensor, video_target_tensor, labels_v, selected_s, chunk_size)

gradio_tabs/img_edit.py

@@ -109,7 +109,7 @@ def img_postprocessing(img, w, h):
 
 def img_edit(gen, device):
 
-    @torch.compile(dynamic=True)
+    @torch.compile
     def compiled_inference(image_tensor, selected_s):
         """Compiled version of just the model inference"""
         return gen.edit_img(image_tensor, labels_v, selected_s)

gradio_tabs/vid_edit.py

@@ -135,12 +135,12 @@ def vid_all_save(vid_d, vid_a, w, h, fps):
 
 def vid_edit(gen, chunk_size, device):
     
-	@torch.compile(dynamic=True)
+	@torch.compile
 	def compiled_edit_vid(image_tensor, selected_s):
 		"""Compiled version of edit_img for video editing tab"""
 		return gen.edit_img(image_tensor, labels_v, selected_s)
 	
-	@torch.compile(dynamic=True)  
+	@torch.compile
 	def compiled_edit_vid_batch(video_target_tensor, selected_s):
 		"""Compiled version of edit_vid_batch for video editing tab"""
 		return gen.edit_vid_batch(video_target_tensor, labels_v, selected_s, chunk_size)

networks/op/fused_act.py

@@ -110,7 +110,6 @@ class FusedLeakyReLU(nn.Module):
         return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
 
 
-@torch.compiler.allow_in_graph
 def fused_leaky_relu(input, bias=None, negative_slope=0.2, scale=2 ** 0.5):
     if input.device.type == "cpu":
         if bias is not None:

networks/op/upfirdn2d.py

@@ -149,7 +149,6 @@ class UpFirDn2d(Function):
         return grad_input, None, None, None, None
 
 
-@torch.compiler.allow_in_graph
 def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
     if not isinstance(up, abc.Iterable):
         up = (up, up)