Support for async node functions (#8830)

* Support for async execution functions

This commit adds support for node execution functions defined as async. When
a node's execution function is defined as async, we can continue
executing other nodes while it is processing.

Standard uses of `await` should "just work", but people will still have
to be careful if they spawn actual threads. Because torch doesn't really
have async/await versions of functions, this won't particularly help
with most locally-executing nodes, but it does work for e.g. web
requests to other machines.

In addition to the execute function, the `VALIDATE_INPUTS` and
`check_lazy_status` functions can also be defined as async, though we'll
only resolve one node at a time right now for those.

* Add the execution model tests to CI

* Add a missing file

It looks like this got caught by .gitignore? There's probably a better
place to put it, but I'm not sure what that is.

* Add the websocket library for automated tests

* Add additional tests for async error cases

Also fixes one bug that was found when an async function throws an error
after being scheduled on a task.

* Add a feature flags message to reduce bandwidth

We now only send 1 preview message of the latest type the client can
support.

We'll add a console warning when the client fails to send a feature
flags message at some point in the future.

* Add async tests to CI

* Don't actually add new tests in this PR

Will do it in a separate PR

* Resolve unit test in GPU-less runner

* Just remove the tests that GHA can't handle

* Change line endings to UNIX-style

* Avoid loading model_management.py so early

Because model_management.py has a top-level `logging.info`, we have to
be careful not to import that file before we call `setup_logging`. If we
do, we end up having the default logging handler registered in addition
to our custom one.

comfy_execution/utils.py

@@ -0,0 +1,46 @@
+import contextvars
+from typing import Optional, NamedTuple
+
+class ExecutionContext(NamedTuple):
+    """
+    Context information about the currently executing node.
+
+    Attributes:
+        node_id: The ID of the currently executing node
+        list_index: The index in a list being processed (for operations on batches/lists)
+    """
+    prompt_id: str
+    node_id: str
+    list_index: Optional[int]
+
+current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None)
+
+def get_executing_context() -> Optional[ExecutionContext]:
+    return current_executing_context.get(None)
+
+class CurrentNodeContext:
+    """
+    Context manager for setting the current executing node context.
+
+    Sets the current_executing_context on enter and resets it on exit.
+
+    Example:
+        with CurrentNodeContext(node_id="123", list_index=0):
+            # Code that should run with the current node context set
+            process_image()
+    """
+    def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None):
+        self.context = ExecutionContext(
+            prompt_id= prompt_id,
+            node_id= node_id,
+            list_index= list_index
+        )
+        self.token = None
+
+    def __enter__(self):
+        self.token = current_executing_context.set(self.context)
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        if self.token is not None:
+            current_executing_context.reset(self.token)