* 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.
- Cosmos now fully tested
- Preliminary support for essential Cosmos prompt "upsampler"
- Lumina tests
- Tweaks to language and image resizing nodes
- Fix for #31 all the samplers are now present again
- --panics-when=torch.cuda.OutOfMemory will now correctly panic and
exit the worker, giving it time to reply that the execution failed
and better dealing with irrecoverable out-of-memory errors
- --executor-factory=ProcessPoolExecutor will use a process instead of
a thread to execute comfyui workflows when using the worker. When
this process panics and exits, it will be correctly replaced, making
a more robust worker
- export_custom_nodes() finds all the classes that inherit from
CustomNode and exports them correctly for custom node discovery to
find
- regular expressions
- additional string formatting and parsing nodes
- fix#29 str(model) no longer raises exceptions like with
HyVideoModelLoader
- don't try to format CUDA tensors because that can sometimes raise
exceptions
- cudaAllocAsync has been disabled for now due to 2.6.0 bugs
- improve florence2 support
- add support for paligemma 2. This requires the fix for transformers
that is currently staged in another repo, install with
`uv pip install --no-deps "transformers@git+https://github.com/zucchini-nlp/transformers.git#branch=paligemma-fix-kwargs"`
- triton has been updated
- fix missing __init__.py files
- use their logger when running interactively
- move the extra nodes files to where this fork expects them
- add the mochi checkpoints to known models
- add a mochi workflow test
