mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
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.
460 lines
16 KiB
Python
460 lines
16 KiB
Python
import copy
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from typing import List, Optional
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from pydantic import BaseModel, ConfigDict, Field
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from ...backend.model_management import BaseModelType, ModelType, SubModelType
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from .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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class ModelInfo(BaseModel):
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model_name: str = Field(description="Info to load submodel")
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base_model: BaseModelType = Field(description="Base model")
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model_type: ModelType = Field(description="Info to load submodel")
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submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
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model_config = ConfigDict(protected_namespaces=())
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class LoraInfo(ModelInfo):
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weight: float = Field(description="Lora's weight which to use when apply to model")
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class UNetField(BaseModel):
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unet: ModelInfo = Field(description="Info to load unet submodel")
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scheduler: ModelInfo = Field(description="Info to load scheduler submodel")
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loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
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seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
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class ClipField(BaseModel):
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tokenizer: ModelInfo = Field(description="Info to load tokenizer submodel")
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text_encoder: ModelInfo = Field(description="Info to load text_encoder submodel")
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skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
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loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
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class VaeField(BaseModel):
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# TODO: better naming?
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vae: ModelInfo = Field(description="Info to load vae submodel")
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seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
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@invocation_output("model_loader_output")
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class ModelLoaderOutput(BaseInvocationOutput):
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"""Model loader output"""
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unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
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clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
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vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
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class MainModelField(BaseModel):
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"""Main model field"""
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model_name: str = Field(description="Name of the model")
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base_model: BaseModelType = Field(description="Base model")
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model_type: ModelType = Field(description="Model Type")
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model_config = ConfigDict(protected_namespaces=())
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class LoRAModelField(BaseModel):
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"""LoRA model field"""
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model_name: str = Field(description="Name of the LoRA 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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@invocation(
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"main_model_loader",
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title="Main Model",
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tags=["model"],
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category="model",
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version="1.0.0",
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)
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class MainModelLoaderInvocation(BaseInvocation):
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"""Loads a main model, outputting its submodels."""
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model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
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# TODO: precision?
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def invoke(self, context: InvocationContext) -> ModelLoaderOutput:
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base_model = self.model.base_model
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model_name = self.model.model_name
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model_type = ModelType.Main
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# TODO: not found exceptions
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if not context.services.model_manager.model_exists(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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):
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raise Exception(f"Unknown {base_model} {model_type} model: {model_name}")
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"""
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if not context.services.model_manager.model_exists(
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model_name=self.model_name,
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model_type=SDModelType.Diffusers,
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submodel=SDModelType.Tokenizer,
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):
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raise Exception(
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f"Failed to find tokenizer submodel in {self.model_name}! Check if model corrupted"
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)
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if not context.services.model_manager.model_exists(
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model_name=self.model_name,
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model_type=SDModelType.Diffusers,
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submodel=SDModelType.TextEncoder,
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):
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raise Exception(
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f"Failed to find text_encoder submodel in {self.model_name}! Check if model corrupted"
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)
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if not context.services.model_manager.model_exists(
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model_name=self.model_name,
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model_type=SDModelType.Diffusers,
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submodel=SDModelType.UNet,
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):
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raise Exception(
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f"Failed to find unet submodel from {self.model_name}! Check if model corrupted"
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)
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"""
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return ModelLoaderOutput(
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unet=UNetField(
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unet=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.UNet,
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),
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scheduler=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.Scheduler,
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),
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loras=[],
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),
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clip=ClipField(
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tokenizer=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.Tokenizer,
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),
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text_encoder=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.TextEncoder,
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),
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loras=[],
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skipped_layers=0,
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),
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vae=VaeField(
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vae=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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submodel=SubModelType.Vae,
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),
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),
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)
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@invocation_output("lora_loader_output")
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class LoraLoaderOutput(BaseInvocationOutput):
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"""Model loader output"""
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unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
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clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
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@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.0")
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class LoraLoaderInvocation(BaseInvocation):
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"""Apply selected lora to unet and text_encoder."""
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lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
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weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
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unet: Optional[UNetField] = InputField(
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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clip: Optional[ClipField] = InputField(
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP",
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)
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def invoke(self, context: InvocationContext) -> LoraLoaderOutput:
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if self.lora is None:
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raise Exception("No LoRA provided")
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base_model = self.lora.base_model
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lora_name = self.lora.model_name
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if not context.services.model_manager.model_exists(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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):
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raise Exception(f"Unkown lora name: {lora_name}!")
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if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
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raise Exception(f'Lora "{lora_name}" already applied to unet')
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if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
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raise Exception(f'Lora "{lora_name}" already applied to clip')
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output = LoraLoaderOutput()
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if self.unet is not None:
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output.unet = copy.deepcopy(self.unet)
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output.unet.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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if self.clip is not None:
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output.clip = copy.deepcopy(self.clip)
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output.clip.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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return output
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@invocation_output("sdxl_lora_loader_output")
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class SDXLLoraLoaderOutput(BaseInvocationOutput):
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"""SDXL LoRA Loader Output"""
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unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
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clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
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clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
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@invocation(
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"sdxl_lora_loader",
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title="SDXL LoRA",
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tags=["lora", "model"],
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category="model",
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version="1.0.0",
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)
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class SDXLLoraLoaderInvocation(BaseInvocation):
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"""Apply selected lora to unet and text_encoder."""
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lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
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weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
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unet: Optional[UNetField] = InputField(
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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clip: Optional[ClipField] = InputField(
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP 1",
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)
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clip2: Optional[ClipField] = InputField(
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default=None,
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description=FieldDescriptions.clip,
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input=Input.Connection,
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title="CLIP 2",
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)
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def invoke(self, context: InvocationContext) -> SDXLLoraLoaderOutput:
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if self.lora is None:
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raise Exception("No LoRA provided")
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base_model = self.lora.base_model
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lora_name = self.lora.model_name
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if not context.services.model_manager.model_exists(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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):
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raise Exception(f"Unknown lora name: {lora_name}!")
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if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
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raise Exception(f'Lora "{lora_name}" already applied to unet')
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if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
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raise Exception(f'Lora "{lora_name}" already applied to clip')
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if self.clip2 is not None and any(lora.model_name == lora_name for lora in self.clip2.loras):
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raise Exception(f'Lora "{lora_name}" already applied to clip2')
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output = SDXLLoraLoaderOutput()
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if self.unet is not None:
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output.unet = copy.deepcopy(self.unet)
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output.unet.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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if self.clip is not None:
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output.clip = copy.deepcopy(self.clip)
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output.clip.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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if self.clip2 is not None:
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output.clip2 = copy.deepcopy(self.clip2)
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output.clip2.loras.append(
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LoraInfo(
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base_model=base_model,
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model_name=lora_name,
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model_type=ModelType.Lora,
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submodel=None,
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weight=self.weight,
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)
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)
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return output
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class VAEModelField(BaseModel):
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"""Vae model field"""
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model_name: str = Field(description="Name of the 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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@invocation_output("vae_loader_output")
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class VaeLoaderOutput(BaseInvocationOutput):
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"""VAE output"""
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vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
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@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.0")
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class VaeLoaderInvocation(BaseInvocation):
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"""Loads a VAE model, outputting a VaeLoaderOutput"""
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vae_model: VAEModelField = InputField(
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description=FieldDescriptions.vae_model,
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input=Input.Direct,
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ui_type=UIType.VaeModel,
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title="VAE",
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)
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def invoke(self, context: InvocationContext) -> VaeLoaderOutput:
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base_model = self.vae_model.base_model
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model_name = self.vae_model.model_name
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model_type = ModelType.Vae
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if not context.services.model_manager.model_exists(
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base_model=base_model,
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model_name=model_name,
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model_type=model_type,
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):
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raise Exception(f"Unkown vae name: {model_name}!")
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return VaeLoaderOutput(
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vae=VaeField(
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vae=ModelInfo(
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model_name=model_name,
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base_model=base_model,
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model_type=model_type,
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)
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)
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)
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@invocation_output("seamless_output")
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class SeamlessModeOutput(BaseInvocationOutput):
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"""Modified Seamless Model output"""
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unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
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vae: Optional[VaeField] = OutputField(default=None, description=FieldDescriptions.vae, title="VAE")
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@invocation(
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"seamless",
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title="Seamless",
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tags=["seamless", "model"],
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category="model",
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version="1.0.0",
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)
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class SeamlessModeInvocation(BaseInvocation):
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"""Applies the seamless transformation to the Model UNet and VAE."""
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unet: Optional[UNetField] = InputField(
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default=None,
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description=FieldDescriptions.unet,
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input=Input.Connection,
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title="UNet",
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)
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vae: Optional[VaeField] = InputField(
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default=None,
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description=FieldDescriptions.vae_model,
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input=Input.Connection,
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title="VAE",
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)
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seamless_y: bool = InputField(default=True, input=Input.Any, description="Specify whether Y axis is seamless")
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seamless_x: bool = InputField(default=True, input=Input.Any, description="Specify whether X axis is seamless")
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def invoke(self, context: InvocationContext) -> SeamlessModeOutput:
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# Conditionally append 'x' and 'y' based on seamless_x and seamless_y
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unet = copy.deepcopy(self.unet)
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vae = copy.deepcopy(self.vae)
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seamless_axes_list = []
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|
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if self.seamless_x:
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seamless_axes_list.append("x")
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if self.seamless_y:
|
|
seamless_axes_list.append("y")
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|
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if unet is not None:
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unet.seamless_axes = seamless_axes_list
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if vae is not None:
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vae.seamless_axes = seamless_axes_list
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|
|
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return SeamlessModeOutput(unet=unet, vae=vae)
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