torch compile for vae loader

mochi_preview/vae/model.py

@@ -33,7 +33,7 @@ class SafeConv3d(torch.nn.Conv3d):
     NOTE: No support for padding along time dimension.
           Input must already be padded along time.
     """
-
+    @torch.compiler.disable()
     def forward(self, input):
         memory_count = torch.prod(torch.tensor(input.shape)).item() * 2 / 1024**3
         if memory_count > 2:

nodes.py

@@ -1,4 +1,7 @@
 import os
+# import torch._dynamo
+# torch._dynamo.config.suppress_errors = True
+
 import torch
 import folder_paths
 import comfy.model_management as mm
@@ -230,6 +233,9 @@ class MochiVAELoader:
             "required": { 
                 "model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "The name of the checkpoint (vae) to load."}),
             },
+            "optional": {
+                "torch_compile_args": ("MOCHICOMPILEARGS", {"tooltip": "Optional torch.compile arguments",}),
+            },
         }
 
     RETURN_TYPES = ("MOCHIVAE",)
@@ -237,7 +243,7 @@ class MochiVAELoader:
     FUNCTION = "loadmodel"
     CATEGORY = "MochiWrapper"
 
-    def loadmodel(self, model_name):
+    def loadmodel(self, model_name, torch_compile_args=None):
 
         device = mm.get_torch_device()
         offload_device = mm.unet_offload_device()
@@ -264,12 +270,21 @@ class MochiVAELoader:
         vae_sd = load_torch_file(vae_path)
         if is_accelerate_available:
             for key in vae_sd:
-                set_module_tensor_to_device(vae, key, dtype=torch.float32, device=device, value=vae_sd[key])
+                set_module_tensor_to_device(vae, key, dtype=torch.float32, device=offload_device, value=vae_sd[key])
         else:
             vae.load_state_dict(vae_sd, strict=True)
-            vae.eval().to(torch.bfloat16).to("cpu")
+            vae.to(torch.bfloat16).to("cpu")
+        vae.eval()
         del vae_sd
 
+        if torch_compile_args is not None:
+            vae.to(device)
+            # for i, block in enumerate(vae.blocks):
+            #     if "CausalUpsampleBlock" in str(type(block)): 
+            #         print("Compiling block", block)
+            vae = torch.compile(vae, fullgraph=torch_compile_args["fullgraph"], mode=torch_compile_args["mode"], dynamic=False, backend=torch_compile_args["backend"])
+    
+
         return (vae,)
     
 class MochiTextEncode: