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c238a7f18b
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.
77 lines
2.9 KiB
Python
77 lines
2.9 KiB
Python
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
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import numpy as np
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from pydantic import ValidationInfo, field_validator
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from invokeai.app.invocations.primitives import IntegerCollectionOutput
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from invokeai.app.util.misc import SEED_MAX, get_random_seed
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from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
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@invocation(
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"range", title="Integer Range", tags=["collection", "integer", "range"], category="collections", version="1.0.0"
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)
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class RangeInvocation(BaseInvocation):
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"""Creates a range of numbers from start to stop with step"""
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start: int = InputField(default=0, description="The start of the range")
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stop: int = InputField(default=10, description="The stop of the range")
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step: int = InputField(default=1, description="The step of the range")
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@field_validator("stop")
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def stop_gt_start(cls, v: int, info: ValidationInfo):
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if "start" in info.data and v <= info.data["start"]:
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raise ValueError("stop must be greater than start")
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return v
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def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
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return IntegerCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
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@invocation(
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"range_of_size",
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title="Integer Range of Size",
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tags=["collection", "integer", "size", "range"],
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category="collections",
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version="1.0.0",
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)
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class RangeOfSizeInvocation(BaseInvocation):
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"""Creates a range from start to start + (size * step) incremented by step"""
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start: int = InputField(default=0, description="The start of the range")
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size: int = InputField(default=1, gt=0, description="The number of values")
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step: int = InputField(default=1, description="The step of the range")
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def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
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return IntegerCollectionOutput(
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collection=list(range(self.start, self.start + (self.step * self.size), self.step))
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)
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@invocation(
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"random_range",
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title="Random Range",
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tags=["range", "integer", "random", "collection"],
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category="collections",
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version="1.0.0",
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use_cache=False,
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)
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class RandomRangeInvocation(BaseInvocation):
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"""Creates a collection of random numbers"""
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low: int = InputField(default=0, description="The inclusive low value")
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high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
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size: int = InputField(default=1, description="The number of values to generate")
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seed: int = InputField(
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ge=0,
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le=SEED_MAX,
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description="The seed for the RNG (omit for random)",
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default_factory=get_random_seed,
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)
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def invoke(self, context: InvocationContext) -> IntegerCollectionOutput:
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rng = np.random.default_rng(self.seed)
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return IntegerCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size)))
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