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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.
108 lines
4.7 KiB
Python
108 lines
4.7 KiB
Python
import os
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from builtins import float
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from typing import List, Union
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from pydantic import BaseModel, ConfigDict, Field
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from invokeai.app.invocations.baseinvocation import (
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BaseInvocation,
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BaseInvocationOutput,
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FieldDescriptions,
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Input,
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InputField,
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InvocationContext,
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OutputField,
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UIType,
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invocation,
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invocation_output,
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)
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from invokeai.app.invocations.primitives import ImageField
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from invokeai.backend.model_management.models.base import BaseModelType, ModelType
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from invokeai.backend.model_management.models.ip_adapter import get_ip_adapter_image_encoder_model_id
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class IPAdapterModelField(BaseModel):
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model_name: str = Field(description="Name of the IP-Adapter model")
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base_model: BaseModelType = Field(description="Base model")
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model_config = ConfigDict(protected_namespaces=())
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class CLIPVisionModelField(BaseModel):
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model_name: str = Field(description="Name of the CLIP Vision image encoder model")
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base_model: BaseModelType = Field(description="Base model (usually 'Any')")
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model_config = ConfigDict(protected_namespaces=())
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class IPAdapterField(BaseModel):
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image: ImageField = Field(description="The IP-Adapter image prompt.")
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ip_adapter_model: IPAdapterModelField = Field(description="The IP-Adapter model to use.")
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image_encoder_model: CLIPVisionModelField = Field(description="The name of the CLIP image encoder model.")
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weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
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# weight: float = Field(default=1.0, ge=0, description="The weight of the IP-Adapter.")
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begin_step_percent: float = Field(
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default=0, ge=0, le=1, description="When the IP-Adapter is first applied (% of total steps)"
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)
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end_step_percent: float = Field(
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default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
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)
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@invocation_output("ip_adapter_output")
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class IPAdapterOutput(BaseInvocationOutput):
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# Outputs
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ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
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@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.0.0")
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class IPAdapterInvocation(BaseInvocation):
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"""Collects IP-Adapter info to pass to other nodes."""
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# Inputs
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image: ImageField = InputField(description="The IP-Adapter image prompt.")
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ip_adapter_model: IPAdapterModelField = InputField(
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description="The IP-Adapter model.", title="IP-Adapter Model", input=Input.Direct, ui_order=-1
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)
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# weight: float = InputField(default=1.0, description="The weight of the IP-Adapter.", ui_type=UIType.Float)
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weight: Union[float, List[float]] = InputField(
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default=1, ge=0, description="The weight given to the IP-Adapter", ui_type=UIType.Float, title="Weight"
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)
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begin_step_percent: float = InputField(
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default=0, ge=-1, le=2, description="When the IP-Adapter is first applied (% of total steps)"
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)
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end_step_percent: float = InputField(
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default=1, ge=0, le=1, description="When the IP-Adapter is last applied (% of total steps)"
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)
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def invoke(self, context: InvocationContext) -> IPAdapterOutput:
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# Lookup the CLIP Vision encoder that is intended to be used with the IP-Adapter model.
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ip_adapter_info = context.services.model_manager.model_info(
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self.ip_adapter_model.model_name, self.ip_adapter_model.base_model, ModelType.IPAdapter
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)
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# HACK(ryand): This is bad for a couple of reasons: 1) we are bypassing the model manager to read the model
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# directly, and 2) we are reading from disk every time this invocation is called without caching the result.
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# A better solution would be to store the image encoder model reference in the IP-Adapter model info, but this
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# is currently messy due to differences between how the model info is generated when installing a model from
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# disk vs. downloading the model.
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image_encoder_model_id = get_ip_adapter_image_encoder_model_id(
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os.path.join(context.services.configuration.get_config().models_path, ip_adapter_info["path"])
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)
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image_encoder_model_name = image_encoder_model_id.split("/")[-1].strip()
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image_encoder_model = CLIPVisionModelField(
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model_name=image_encoder_model_name,
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base_model=BaseModelType.Any,
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)
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return IPAdapterOutput(
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ip_adapter=IPAdapterField(
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image=self.image,
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ip_adapter_model=self.ip_adapter_model,
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image_encoder_model=image_encoder_model,
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weight=self.weight,
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begin_step_percent=self.begin_step_percent,
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end_step_percent=self.end_step_percent,
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),
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)
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