Merge branch 'master' into dr-support-pip-cm

Author: ltdrdata

README.md

@@ -197,7 +197,9 @@ comfy install
 
 ## Manual Install (Windows, Linux)
 
-Python 3.13 is very well supported. If you have trouble with some custom node dependencies you can try 3.12
+Python 3.14 will work if you comment out the `kornia` dependency in the requirements.txt file (breaks the canny node) and install pytorch nightly but it is not recommended.
+
+Python 3.13 is very well supported. If you have trouble with some custom node dependencies on 3.13 you can try 3.12
 
 Git clone this repo.
 

comfy/ops.py

@@ -24,6 +24,8 @@
 import comfy.rmsnorm
 import contextlib
 
+def run_every_op():
+    comfy.model_management.throw_exception_if_processing_interrupted()
 
 def scaled_dot_product_attention(q, k, v, *args, **kwargs):
     return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
@@ -109,6 +111,7 @@ def forward_comfy_cast_weights(self, input):
             return torch.nn.functional.linear(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -123,6 +126,7 @@ def forward_comfy_cast_weights(self, input):
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -137,6 +141,7 @@ def forward_comfy_cast_weights(self, input):
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -151,6 +156,7 @@ def forward_comfy_cast_weights(self, input):
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -165,6 +171,7 @@ def forward_comfy_cast_weights(self, input):
             return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -183,6 +190,7 @@ def forward_comfy_cast_weights(self, input):
             return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -202,6 +210,7 @@ def forward_comfy_cast_weights(self, input):
             # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -223,6 +232,7 @@ def forward_comfy_cast_weights(self, input, output_size=None):
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -244,6 +254,7 @@ def forward_comfy_cast_weights(self, input, output_size=None):
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -262,6 +273,7 @@ def forward_comfy_cast_weights(self, input, out_dtype=None):
             return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:

comfy_api/latest/__init__.py

@@ -114,7 +114,9 @@ class Types:
     ComfyAPISync: Type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
 ComfyAPISync = create_sync_class(ComfyAPI_latest)
 
-comfy_io = io  # create the new alias for io
+# create new aliases for io and ui
+IO = io
+UI = ui
 
 __all__ = [
     "ComfyAPI",
@@ -124,6 +126,7 @@ class Types:
     "Types",
     "ComfyExtension",
     "io",
-    "comfy_io",
+    "IO",
     "ui",
+    "UI",
 ]

comfy_api_nodes/apinode_utils.py

@@ -3,6 +3,7 @@
 import io
 import logging
 import mimetypes
+import os
 from typing import Optional, Union
 from comfy.utils import common_upscale
 from comfy_api.input_impl import VideoFromFile
@@ -702,3 +703,16 @@ def image_tensor_pair_to_batch(
             "center",
         ).movedim(1, -1)
     return torch.cat((image1, image2), dim=0)
+
+
+def get_size(path_or_object: Union[str, io.BytesIO]) -> int:
+    if isinstance(path_or_object, str):
+        return os.path.getsize(path_or_object)
+    return len(path_or_object.getvalue())
+
+
+def validate_container_format_is_mp4(video: VideoInput) -> None:
+    """Validates video container format is MP4."""
+    container_format = video.get_container_format()
+    if container_format not in ["mp4", "mov,mp4,m4a,3gp,3g2,mj2"]:
+        raise ValueError(f"Only MP4 container format supported. Got: {container_format}")

comfy_api_nodes/apis/client.py

@@ -845,7 +845,7 @@ def _display_text_on_node(self, text: str):
         if not self.node_id:
             return
         if self.extracted_price is not None:
-            text = f"Price: {self.extracted_price}$\n{text}"
+            text = f"Price: ${self.extracted_price}\n{text}"
         PromptServer.instance.send_progress_text(text, self.node_id)
 
     def _display_time_progress_on_node(self, time_completed: int | float):