* 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.
* Make torch compile node use wrapper instead of object_patch for the entire diffusion_models object, allowing key assotiations on diffusion_models to not break (loras, getting attributes, etc.)
* Moved torch compile code into comfy_api so it can be used by custom nodes with a degree of confidence
* Refactor set_torch_compile_wrapper to support a list of keys instead of just diffusion_model, as well as additional torch.compile args
* remove unused import
* Moved torch compile kwargs to be stored in model_options instead of attachments; attachments are more intended for things to be 'persisted', AKA not deepcopied
* Add some comments
* Remove random line of code, not sure how it got there
* Add basic support for videos as types
This PR adds support for VIDEO as first-class types. In order to avoid
unnecessary costs, VIDEO outputs must implement the `VideoInput` ABC,
but their implementation details can vary. Included are two
implementations of this type which can be returned by other nodes:
* `VideoFromFile` - Created with either a path on disk (as a string) or
a `io.BytesIO` containing the contents of a file in a supported format
(like .mp4). This implementation won't actually load the video unless
necessary. It will also avoid re-encoding when saving if possible.
* `VideoFromComponents` - Created from an image tensor and an optional
audio tensor.
Currently, only h264 encoded videos in .mp4 containers are supported for
saving, but the plan is to add additional encodings/containers in the
near future (particularly .webm).
* Add optimization to avoid parsing entire video
* Improve type declarations to reduce warnings
* Make sure bytesIO objects can be read many times
* Fix a potential issue when saving long videos
* Fix incorrect type annotation
* Add a `LoadVideo` node to make testing easier
* Refactor new types out of the base comfy folder
I've created a new `comfy_api` top-level module. The intention is that
anything within this folder would be covered by semver-style versioning
that would allow custom nodes to rely on them not introducing breaking
changes.
* Fix linting issue
