feat(api): chore: pydantic & fastapi upgrade

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.

invokeai/backend/model_management/models/__init__.py

@@ -2,7 +2,7 @@ import inspect
 from enum import Enum
 from typing import Literal, get_origin
 
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict, create_model
 
 from .base import (  # noqa: F401
     BaseModelType,
@@ -106,6 +106,8 @@ class OpenAPIModelInfoBase(BaseModel):
     base_model: BaseModelType
     model_type: ModelType
 
+    model_config = ConfigDict(protected_namespaces=())
+
 
 for base_model, models in MODEL_CLASSES.items():
     for model_type, model_class in models.items():
@@ -121,17 +123,11 @@ for base_model, models in MODEL_CLASSES.items():
             if openapi_cfg_name in vars():
                 continue
 
-            api_wrapper = type(
+            api_wrapper = create_model(
                 openapi_cfg_name,
-                (cfg, OpenAPIModelInfoBase),
-                dict(
-                    __annotations__=dict(
-                        model_type=Literal[model_type.value],
-                    ),
-                ),
+                __base__=(cfg, OpenAPIModelInfoBase),
+                model_type=(Literal[model_type], model_type),  # type: ignore
             )
-
-            # globals()[openapi_cfg_name] = api_wrapper
             vars()[openapi_cfg_name] = api_wrapper
             OPENAPI_MODEL_CONFIGS.append(api_wrapper)
 

invokeai/backend/model_management/models/base.py

@@ -19,7 +19,7 @@ from diffusers import logging as diffusers_logging
 from onnx import numpy_helper
 from onnxruntime import InferenceSession, SessionOptions, get_available_providers
 from picklescan.scanner import scan_file_path
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, ConfigDict, Field
 from transformers import logging as transformers_logging
 
 
@@ -86,14 +86,21 @@ class ModelError(str, Enum):
     NotFound = "not_found"
 
 
+def model_config_json_schema_extra(schema: dict[str, Any]) -> None:
+    if "required" not in schema:
+        schema["required"] = []
+    schema["required"].append("model_type")
+
+
 class ModelConfigBase(BaseModel):
     path: str  # or Path
     description: Optional[str] = Field(None)
     model_format: Optional[str] = Field(None)
     error: Optional[ModelError] = Field(None)
 
-    class Config:
-        use_enum_values = True
+    model_config = ConfigDict(
+        use_enum_values=True, protected_namespaces=(), json_schema_extra=model_config_json_schema_extra
+    )
 
 
 class EmptyConfigLoader(ConfigMixin):

invokeai/backend/model_management/models/ip_adapter.py

@@ -58,14 +58,16 @@ class IPAdapterModel(ModelBase):
 
     def get_model(
         self,
-        torch_dtype: Optional[torch.dtype],
+        torch_dtype: torch.dtype,
         child_type: Optional[SubModelType] = None,
     ) -> typing.Union[IPAdapter, IPAdapterPlus]:
         if child_type is not None:
             raise ValueError("There are no child models in an IP-Adapter model.")
 
         model = build_ip_adapter(
-            ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), device="cpu", dtype=torch_dtype
+            ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"),
+            device=torch.device("cpu"),
+            dtype=torch_dtype,
         )
 
         self.model_size = model.calc_size()