This fixes a weird issue where the list images method needed to handle `None` for its `limit` and `offset` arguments, in order to get a count of all intermediates.
On our local installs this will be a very minor change. For those running on remote servers, load times should be slightly improved.
It's a small change but I think correct.
This should prevent `index.html` from *ever* being cached, so UIs will never be out of date.
Minor organisation to accomodate this.
Deleting old unused files from the early days
Upgrade pydantic and fastapi to latest.
- pydantic~=2.4.2
- fastapi~=103.2
- fastapi-events~=0.9.1
**Big Changes**
There are a number of logic changes needed to support pydantic v2. Most changes are very simple, like using the new methods to serialized and deserialize models, but there are a few more complex changes.
**Invocations**
The biggest change relates to invocation creation, instantiation and validation.
Because pydantic v2 moves all validation logic into the rust pydantic-core, we may no longer directly stick our fingers into the validation pie.
Previously, we (ab)used models and fields to allow invocation fields to be optional at instantiation, but required when `invoke()` is called. We directly manipulated the fields and invocation models when calling `invoke()`.
With pydantic v2, this is much more involved. Changes to the python wrapper do not propagate down to the rust validation logic - you have to rebuild the model. This causes problem with concurrent access to the invocation classes and is not a free operation.
This logic has been totally refactored and we do not need to change the model any more. The details are in `baseinvocation.py`, in the `InputField` function and `BaseInvocation.invoke_internal()` method.
In the end, this implementation is cleaner.
**Invocation Fields**
In pydantic v2, you can no longer directly add or remove fields from a model.
Previously, we did this to add the `type` field to invocations.
**Invocation Decorators**
With pydantic v2, we instead use the imperative `create_model()` API to create a new model with the additional field. This is done in `baseinvocation.py` in the `invocation()` wrapper.
A similar technique is used for `invocation_output()`.
**Minor Changes**
There are a number of minor changes around the pydantic v2 models API.
**Protected `model_` Namespace**
All models' pydantic-provided methods and attributes are prefixed with `model_` and this is considered a protected namespace. This causes some conflict, because "model" means something to us, and we have a ton of pydantic models with attributes starting with "model_".
Forunately, there are no direct conflicts. However, in any pydantic model where we define an attribute or method that starts with "model_", we must tell set the protected namespaces to an empty tuple.
```py
class IPAdapterModelField(BaseModel):
model_name: str = Field(description="Name of the IP-Adapter model")
base_model: BaseModelType = Field(description="Base model")
model_config = ConfigDict(protected_namespaces=())
```
**Model Serialization**
Pydantic models no longer have `Model.dict()` or `Model.json()`.
Instead, we use `Model.model_dump()` or `Model.model_dump_json()`.
**Model Deserialization**
Pydantic models no longer have `Model.parse_obj()` or `Model.parse_raw()`, and there are no `parse_raw_as()` or `parse_obj_as()` functions.
Instead, you need to create a `TypeAdapter` object to parse python objects or JSON into a model.
```py
adapter_graph = TypeAdapter(Graph)
deserialized_graph_from_json = adapter_graph.validate_json(graph_json)
deserialized_graph_from_dict = adapter_graph.validate_python(graph_dict)
```
**Field Customisation**
Pydantic `Field`s no longer accept arbitrary args.
Now, you must put all additional arbitrary args in a `json_schema_extra` arg on the field.
**Schema Customisation**
FastAPI and pydantic schema generation now follows the OpenAPI version 3.1 spec.
This necessitates two changes:
- Our schema customization logic has been revised
- Schema parsing to build node templates has been revised
The specific aren't important, but this does present additional surface area for bugs.
**Performance Improvements**
Pydantic v2 is a full rewrite with a rust backend. This offers a substantial performance improvement (pydantic claims 5x to 50x depending on the task). We'll notice this the most during serialization and deserialization of sessions/graphs, which happens very very often - a couple times per node.
I haven't done any benchmarks, but anecdotally, graph execution is much faster. Also, very larges graphs - like with massive iterators - are much, much faster.
Facetools nodes were cutting off faces that extended beyond chunk boundaries in some cases. All faces found are considered and are coalesced rather than pruned, meaning that you should not see half a face any more.
- Make all metadata items optional. This will reduce errors related to metadata not being provided when we update the backend but old queue items still exist
- Fix a bug in t2i adapter metadata handling where it checked for ip adapter metadata instaed of t2i adapter metadata
- Fix some metadata fields that were not using `InputField`
* added HrfScale type with initial value
* working
* working
* working
* working
* working
* added addHrfToGraph
* continueing to implement this
* working on this
* comments
* working
* made hrf into its own collapse
* working on adding strength slider
* working
* working
* refactoring
* working
* change of this working: 0
* removed onnx support since apparently its not used
* working
* made scale integer
* trying out psycicpebbles idea
* working
* working on this
* working
* added toggle
* comments
* self review
* fixing things
* remove 'any' type
* fixing typing
* changed initial strength value to 3 (large values cause issues)
* set denoising start to be 1 - strength to resemble image to image
* set initial value
* added image to image
* pr1
* pr2
* updating to resolution finding
* working
* working
* working
* working
* working
* working
* working
* working
* working
* use memo
* connect rescale hw to noise
* working
* fixed min bug
* nit
* hides elements conditionally
* style
* feat(ui): add config for HRF, disable if feature disabled or ONNX model in use
* fix(ui): use `useCallback` for HRF toggle
---------
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
Refactor services folder/module structure.
**Motivation**
While working on our services I've repeatedly encountered circular imports and a general lack of clarity regarding where to put things. The structure introduced goes a long way towards resolving those issues, setting us up for a clean structure going forward.
**Services**
Services are now in their own folder with a few files:
- `services/{service_name}/__init__.py`: init as needed, mostly empty now
- `services/{service_name}/{service_name}_base.py`: the base class for the service
- `services/{service_name}/{service_name}_{impl_type}.py`: the default concrete implementation of the service - typically one of `sqlite`, `default`, or `memory`
- `services/{service_name}/{service_name}_common.py`: any common items - models, exceptions, utilities, etc
Though it's a bit verbose to have the service name both as the folder name and the prefix for files, I found it is _extremely_ confusing to have all of the base classes just be named `base.py`. So, at the cost of some verbosity when importing things, I've included the service name in the filename.
There are some minor logic changes. For example, in `InvocationProcessor`, instead of assigning the model manager service to a variable to be used later in the file, the service is used directly via the `Invoker`.
**Shared**
Things that are used across disparate services are in `services/shared/`:
- `default_graphs.py`: previously in `services/`
- `graphs.py`: previously in `services/`
- `paginatation`: generic pagination models used in a few services
- `sqlite`: the `SqliteDatabase` class, other sqlite-specific things
**Service Dependencies**
Services that depend on other services now access those services via the `Invoker` object. This object is provided to the service as a kwarg to its `start()` method.
Until now, most services did not utilize this feature, and several services required their dependencies to be initialized and passed in on init.
Additionally, _all_ services are now registered as invocation services - including the low-level services. This obviates issues with inter-dependent services we would otherwise experience as we add workflow storage.
**Database Access**
Previously, we were passing in a separate sqlite connection and corresponding lock as args to services in their init. A good amount of posturing was done in each service that uses the db.
These objects, along with the sqlite startup and cleanup logic, is now abstracted into a simple `SqliteDatabase` class. This creates the shared connection and lock objects, enables foreign keys, and provides a `clean()` method to do startup db maintenance.
This is not a service as it's only used by sqlite services.
* UI for bulk downloading boards or groups of images
* placeholder route for bulk downloads that does nothing
* lint
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
The UI will always re-fetch queue and batch status on receiving this event, so we may as well jsut include that data in the event and save the extra network roundtrips.
- Update backend metadata for t2i adapter
- Fix typo in `T2IAdapterInvocation`: `ip_adapter_model` -> `t2i_adapter_model`
- Update linear graphs to use t2i adapter
- Add client metadata recall for t2i adapter
- Fix bug with controlnet metadata recall - processor should be set to 'none' when recalling a control adapter
* Bump diffusers to 0.21.2.
* Add T2IAdapterInvocation boilerplate.
* Add T2I-Adapter model to model-management.
* (minor) Tidy prepare_control_image(...).
* Add logic to run the T2I-Adapter models at the start of the DenoiseLatentsInvocation.
* Add logic for applying T2I-Adapter weights and accumulating.
* Add T2IAdapter to MODEL_CLASSES map.
* yarn typegen
* Add model probes for T2I-Adapter models.
* Add all of the frontend boilerplate required to use T2I-Adapter in the nodes editor.
* Add T2IAdapterModel.convert_if_required(...).
* Fix errors in T2I-Adapter input image sizing logic.
* Fix bug with handling of multiple T2I-Adapters.
* black / flake8
* Fix typo
* yarn build
* Add num_channels param to prepare_control_image(...).
* Link to upstream diffusers bugfix PR that currently requires a workaround.
* feat: Add Color Map Preprocessor
Needed for the color T2I Adapter
* feat: Add Color Map Preprocessor to Linear UI
* Revert "feat: Add Color Map Preprocessor"
This reverts commit a1119a00bf.
* Revert "feat: Add Color Map Preprocessor to Linear UI"
This reverts commit bd8a9b82d8.
* Fix T2I-Adapter field rendering in workflow editor.
* yarn build, yarn typegen
---------
Co-authored-by: blessedcoolant <54517381+blessedcoolant@users.noreply.github.com>
Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
When the processor has an error and it has a queue item, mark that item failed.
This addresses processor errors resulting in `in_progress` queue items, which create a soft lock of the processor, requiring the user to cancel the `in_progress` item before anything else processes.
Makes graph validation logic more rigorous, validating graphs when they are created as part of a session or batch.
`validate_self()` method added to `Graph` model. It does all the validation that `is_valid()` did, plus a few extras:
- unique `node.id` values across graph
- node ids match their key in `Graph.nodes`
- recursively validate subgraphs
- validate all edges
- validate graph is acyclical
The new method is required because `is_valid()` just returned a boolean. That behaviour is retained, but `validate_self()` now raises appropriate exceptions for validation errors. This are then surfaced to the client.
The function is named `validate_self()` because pydantic reserves `validate()`.
There are two main places where graphs are created - in batches and in sessions.
Field validators are added to each of these for their `graph` fields, which call the new validation logic.
**Closes #4744**
In this issue, a batch is enqueued with an invalid graph. The output field is typed as optional while the input field is required. The field types themselves are not relevant - this change addresses the case where an invalid graph was created.
The mismatched types problem is not noticed until we attempt to invoke the graph, because the graph was never *fully* validated. An error is raised during the call to `graph_execution_state.next()` in `invoker.py`. This function prepares the edges and validates them, raising an exception due to the mismatched types.
This exception is caught by the session processor, but it doesn't handle this situation well - the graph is not marked as having an error and the queue item status is never changed. The queue item is therefore forever `in_progress`, so no new queue items are popped - the app won't do anything until the queue item is canceled manually.
This commit addresses this by preventing invalid graphs from being created in the first place, addressing a substantial number of fail cases.
The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = fastest, largest filesize, 9 = slowest, smallest filesize
Closes#4786
