Traces now include the arguments for executing a node, wherever it makes sense to do so.

comfy/cmd/execution.py

@@ -66,7 +66,35 @@ def get_input_data(inputs, class_def, unique_id, outputs=None, prompt=None, extr
     return input_data_all
 
 
-def map_node_over_list(obj, input_data_all, func, allow_interrupt=False):
+@tracer.start_as_current_span("Execute Node")
+def map_node_over_list(obj, input_data_all: typing.Dict[str, typing.Any], func: str, allow_interrupt=False):
+    span = get_current_span()
+    class_type = obj.__class__.__name__
+    span.set_attribute("class_type", class_type)
+    if input_data_all is not None:
+        for kwarg_name, kwarg_value in input_data_all.items():
+            if isinstance(kwarg_value, str) or isinstance(kwarg_value, bool) or isinstance(kwarg_value, int) or isinstance(kwarg_value, float):
+                span.set_attribute(f"input_data_all.{kwarg_name}", kwarg_value)
+            else:
+                try:
+                    items_to_display = []
+                    if hasattr(kwarg_value, "shape"):
+                        # if the object has a shape attribute (likely a NumPy array or similar), get up to the first ten elements
+                        flat_values = kwarg_value.flatten() if hasattr(kwarg_value, "flatten") else kwarg_value
+                        items_to_display = [flat_values[i] for i in range(min(10, flat_values.size))]
+                    elif hasattr(kwarg_value, "__getitem__") and hasattr(kwarg_value, "__len__"):
+                        # If the object is indexable and has a length, get the first ten items
+                        items_to_display = [kwarg_value[i] for i in range(min(10, len(kwarg_value)))]
+
+                    filtered_items = [
+                        item for item in items_to_display if isinstance(item, (str, bool, int, float))
+                    ]
+
+                    if filtered_items:
+                        span.set_attribute(f"input_data_all.{kwarg_name}", filtered_items)
+                except TypeError:
+                    pass
+
     # check if node wants the lists
     input_is_list = False
     if hasattr(obj, "INPUT_IS_LIST"):

comfy/cmd/main_pre.py

@@ -15,6 +15,7 @@ import warnings
 from opentelemetry import trace
 from opentelemetry.exporter.otlp.proto.http.trace_exporter import OTLPSpanExporter
 from opentelemetry.instrumentation.aio_pika import AioPikaInstrumentor
+from opentelemetry.instrumentation.requests import RequestsInstrumentor
 from opentelemetry.sdk.resources import Resource
 from opentelemetry.sdk.trace import TracerProvider
 from opentelemetry.sdk.trace.export import BatchSpanProcessor, ConsoleSpanExporter, SpanExporter
@@ -74,6 +75,7 @@ def _create_tracer():
     patch_spanbuilder_set_channel()
     AioPikaInstrumentor().instrument()
     AioHttpServerInstrumentor().instrument()
+    RequestsInstrumentor().instrument()
     return trace.get_tracer(args.otel_service_name)
 
 

comfy/model_management_types.py

@@ -25,25 +25,25 @@ class ModelManageable(Protocol):
         ...
 
     def is_clone(self, other: torch.nn.Module) -> bool:
-        pass
+        ...
 
     def clone_has_same_weights(self, clone: torch.nn.Module) -> bool:
-        pass
+        ...
 
     def model_size(self) -> int:
-        pass
+        ...
 
     def model_patches_to(self, arg: torch.device | torch.dtype):
-        pass
+        ...
 
     def model_dtype(self) -> torch.dtype:
-        pass
+        ...
 
     def patch_model_lowvram(self, device_to: torch.device, lowvram_model_memory: int) -> torch.nn.Module:
-        pass
+        ...
 
     def patch_model(self, device_to: torch.device, patch_weights: bool) -> torch.nn.Module:
-        pass
+        ...
 
     def unpatch_model(self, offload_device: torch.device, unpatch_weights: Optional[bool] = False) -> torch.nn.Module:
-        pass
+        ...

requirements.txt

@@ -43,5 +43,6 @@ opentelemetry-propagator-jaeger
 opentelemetry-instrumentation
 opentelemetry-util-http
 opentelemetry-instrumentation-aio-pika
+opentelemetry-instrumentation-requests
 opentelemetry-semantic-conventions
 wrapt>=1.16.0