We have two different classes named `ModelInfo` which might need to be used by API consumers. We need to export both but have to deal with this naming collision.
The `ModelInfo` I've renamed here is the one that is returned when a model is loaded. It's the object least likely to be used by API consumers.
Replace `delete_on_startup: bool` & associated logic with `ephemeral: bool` and `TemporaryDirectory`.
The temp dir is created inside of `output_dir`. For example, if `output_dir` is `invokeai/outputs/tensors/`, then the temp dir might be `invokeai/outputs/tensors/tmpvj35ht7b/`.
The temp dir is cleaned up when the service is stopped, or when it is GC'd if not properly stopped.
In the event of a catastrophic crash where the temp files are not cleaned up, the user can delete the tempdir themselves.
This situation may not occur in normal use, but if you kill the process, python cannot clean up the temp dir itself. This includes running the app in a debugger and killing the debugger process - something I do relatively often.
Tests updated.
- The default is to not delete on startup - feels safer.
- The two services using this class _do_ delete on startup.
- The class has "ephemeral" removed from its name.
- Tests & app updated for this change.
`_delete_all` logged how many items it deleted, and had to be called _after_ service start bc it needed access to logger.
Move the logger call to the startup method and return the the deleted stats from `_delete_all`. This lets `_delete_all` be called at any time.
Turns out they are just different enough in purpose that the implementations would be rather unintuitive. I've made a separate ObjectSerializer service to handle tensors and conditioning.
Refined the class a bit too.
Turns out `ItemStorageABC` was almost identical to `PickleStorageBase`. Instead of maintaining separate classes, we can use `ItemStorageABC` for both.
There's only one change needed - the `ItemStorageABC.set` method must return the newly stored item's ID. This allows us to let the service handle the responsibility of naming the item, but still create the requisite output objects during node execution.
The naming implementation is improved here. It extracts the name of the generic and appends a UUID to that string when saving items.
- New generic class `PickleStorageBase`, implements the same API as `LatentsStorageBase`, use for storing non-serializable data via pickling
- Implementation `PickleStorageTorch` uses `torch.save` and `torch.load`, same as `LatentsStorageDisk`
- Add `tensors: PickleStorageBase[torch.Tensor]` to `InvocationServices`
- Add `conditioning: PickleStorageBase[ConditioningFieldData]` to `InvocationServices`
- Remove `latents` service and all `LatentsStorage` classes
- Update `InvocationContext` and all usage of old `latents` service to use the new services/context wrapper methods
This class works the same way as `WithMetadata` - it simply adds a `board` field to the node. The context wrapper function is able to pull the board id from this. This allows image-outputting nodes to get a board field "for free", and have their outputs automatically saved to it.
This is a breaking change for node authors who may have a field called `board`, because it makes `board` a reserved field name. I'll look into how to avoid this - maybe by naming this invoke-managed field `_board` to avoid collisions?
Supporting changes:
- `WithBoard` is added to all image-outputting nodes, giving them the ability to save to board.
- Unused, duplicate `WithMetadata` and `WithWorkflow` classes are deleted from `baseinvocation.py`. The "real" versions are in `fields.py`.
- Remove `LinearUIOutputInvocation`. Now that all nodes that output images also have a `board` field by default, this node is no longer necessary. See comment here for context: https://github.com/invoke-ai/InvokeAI/pull/5491#discussion_r1480760629
- Without `LinearUIOutputInvocation`, the `ImagesInferface.update` method is no longer needed, and removed.
Note: This commit does not bump all node versions. I will ensure that is done correctly before merging the PR of which this commit is a part.
Note: A followup commit will implement the frontend changes to support this change.
- The config is already cached by the config class's `get_config()` method.
- The config mutates itself in its `root_path` property getter. Freezing the class makes any attempt to grab a path from the config error. Unfortunately this means we cannot easily freeze the class without fiddling with the inner workings of `InvokeAIAppConfig`, which is outside the scope here.
Update all invocations to use the new context. The changes are all fairly simple, but there are a lot of them.
Supporting minor changes:
- Patch bump for all nodes that use the context
- Update invocation processor to provide new context
- Minor change to `EventServiceBase` to accept a node's ID instead of the dict version of a node
- Minor change to `ModelManagerService` to support the new wrapped context
- Fanagling of imports to avoid circular dependencies
Methods `get_node` and `complete` were typed as returning a dynamically created unions `InvocationsUnion` and `InvocationOutputsUnion`, respectively.
Static type analysers cannot work with dynamic objects, so these methods end up as effectively un-annotated, returning `Unknown`.
They now return `BaseInvocation` and `BaseInvocationOutput`, respectively, which are the superclasses of all members of each union. This gives us the best type annotation that is possible.
Note: the return types of these methods are never introspected, so it doesn't really matter what they are at runtime.
The change to memory session storage brings a subtle behaviour change.
Previously, we serialized and deserialized everything (e.g. field state, invocation outputs, etc) constantly. The meant we were effectively working with deep-copied objects at all time. We could mutate objects freely without worrying about other references to the object.
With memory storage, objects are now passed around by reference, and we cannot handle them in the same way.
This is problematic for nodes that mutate their own inputs. There are two ways this causes a problem:
- An output is used as input for multiple nodes. If the first node mutates the output object while `invoke`ing, the next node will get the mutated object.
- The invocation cache stores live python objects. When a node mutates an output pulled from the cache, the next node that uses the cached object will get the mutated object.
The solution is to deep-copy a node's inputs as they are set, effectively reproducing the same behaviour as we had with the SQLite session storage. Nodes can safely mutate their inputs and those changes never leave the node's scope.
Closes #5665
The stats service was logging error messages when attempting to retrieve stats for a graph that it wasn't tracking. This was rather noisy.
Instead of logging these errors within the service, we now will just raise the error and let the consumer of the service decide whether or not to log. Our usage of the service at this time is to suppress errors - we don't want to log anything to the console.
Note: With the improvements in the previous two commits, we shouldn't get these errors moving forward, but I still think this change is correct.
When an invocation is canceled, we consider the graph canceled. Log its graph's stats before resetting its graph's stats. No reason to not log these stats.
We also should stop the profiler at this point, because this graph is finished. If we don't stop it manually, it will stop itself and write the profile to disk when it is next started, but the resultant profile will include more than just its target graph.
Now we get both stats and profiles for canceled graphs.
When an invocation errored, we clear the stats for the whole graph. Later on, we check the graph for errors and see the failed invocation, and we consider the graph failed. We then attempts to log the stats for the failed graph.
Except now the failed graph has no stats, and the stats raises an error.
The user sees, in the terminal:
- An invocation error
- A stats error (scary!)
- No stats for the failed graph (uninformative!)
What the user should see:
- An invocation error
- Graph stats
The fix is simple - don't reset the graph stats when an invocation has an error.
