mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
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.
This commit is contained in:
psychedelicious
@ -41,18 +41,18 @@ config = InvokeAIAppConfig.get_config()
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class SegmentedGrayscale(object):
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def __init__(self, image: Image, heatmap: torch.Tensor):
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def __init__(self, image: Image.Image, heatmap: torch.Tensor):
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self.heatmap = heatmap
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self.image = image
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def to_grayscale(self, invert: bool = False) -> Image:
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def to_grayscale(self, invert: bool = False) -> Image.Image:
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return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))
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def to_mask(self, threshold: float = 0.5) -> Image:
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def to_mask(self, threshold: float = 0.5) -> Image.Image:
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discrete_heatmap = self.heatmap.lt(threshold).int()
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return self._rescale(Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L"))
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def to_transparent(self, invert: bool = False) -> Image:
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def to_transparent(self, invert: bool = False) -> Image.Image:
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transparent_image = self.image.copy()
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# For img2img, we want the selected regions to be transparent,
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# but to_grayscale() returns the opposite. Thus invert.
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@ -61,7 +61,7 @@ class SegmentedGrayscale(object):
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return transparent_image
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# unscales and uncrops the 352x352 heatmap so that it matches the image again
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def _rescale(self, heatmap: Image) -> Image:
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def _rescale(self, heatmap: Image.Image) -> Image.Image:
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size = self.image.width if (self.image.width > self.image.height) else self.image.height
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resized_image = heatmap.resize((size, size), resample=Image.Resampling.LANCZOS)
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return resized_image.crop((0, 0, self.image.width, self.image.height))
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@ -82,7 +82,7 @@ class Txt2Mask(object):
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self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL, cache_dir=config.cache_dir)
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@torch.no_grad()
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def segment(self, image, prompt: str) -> SegmentedGrayscale:
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def segment(self, image: Image.Image, prompt: str) -> SegmentedGrayscale:
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"""
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Given a prompt string such as "a bagel", tries to identify the object in the
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provided image and returns a SegmentedGrayscale object in which the brighter
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@ -99,7 +99,7 @@ class Txt2Mask(object):
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heatmap = torch.sigmoid(outputs.logits)
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return SegmentedGrayscale(image, heatmap)
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def _scale_and_crop(self, image: Image) -> Image:
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def _scale_and_crop(self, image: Image.Image) -> Image.Image:
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scaled_image = Image.new("RGB", (CLIPSEG_SIZE, CLIPSEG_SIZE))
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if image.width > image.height: # width is constraint
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scale = CLIPSEG_SIZE / image.width
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@ -9,7 +9,7 @@ class InitImageResizer:
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def __init__(self, Image):
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self.image = Image
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def resize(self, width=None, height=None) -> Image:
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def resize(self, width=None, height=None) -> Image.Image:
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"""
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Return a copy of the image resized to fit within
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a box width x height. The aspect ratio is
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@ -793,7 +793,11 @@ def migrate_init_file(legacy_format: Path):
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old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
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new = InvokeAIAppConfig.get_config()
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fields = [x for x, y in InvokeAIAppConfig.__fields__.items() if y.field_info.extra.get("category") != "DEPRECATED"]
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fields = [
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x
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for x, y in InvokeAIAppConfig.model_fields.items()
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if (y.json_schema_extra.get("category", None) if y.json_schema_extra else None) != "DEPRECATED"
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]
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for attr in fields:
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if hasattr(old, attr):
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try:
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@ -236,13 +236,13 @@ import types
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from dataclasses import dataclass
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from pathlib import Path
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from shutil import move, rmtree
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from typing import Callable, Dict, List, Literal, Optional, Set, Tuple, Union
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from typing import Callable, Dict, List, Literal, Optional, Set, Tuple, Union, cast
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import torch
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import yaml
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from omegaconf import OmegaConf
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from omegaconf.dictconfig import DictConfig
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, ConfigDict, Field
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import invokeai.backend.util.logging as logger
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from invokeai.app.services.config import InvokeAIAppConfig
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@ -294,6 +294,8 @@ class AddModelResult(BaseModel):
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base_model: BaseModelType = Field(description="The base model")
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config: ModelConfigBase = Field(description="The configuration of the model")
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model_config = ConfigDict(protected_namespaces=())
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MAX_CACHE_SIZE = 6.0 # GB
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@ -576,7 +578,7 @@ class ModelManager(object):
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"""
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model_key = self.create_key(model_name, base_model, model_type)
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if model_key in self.models:
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return self.models[model_key].dict(exclude_defaults=True)
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return self.models[model_key].model_dump(exclude_defaults=True)
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else:
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return None # TODO: None or empty dict on not found
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@ -632,7 +634,7 @@ class ModelManager(object):
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continue
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model_dict = dict(
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**model_config.dict(exclude_defaults=True),
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**model_config.model_dump(exclude_defaults=True),
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# OpenAPIModelInfoBase
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model_name=cur_model_name,
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base_model=cur_base_model,
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@ -900,14 +902,16 @@ class ModelManager(object):
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Write current configuration out to the indicated file.
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"""
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data_to_save = dict()
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data_to_save["__metadata__"] = self.config_meta.dict()
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data_to_save["__metadata__"] = self.config_meta.model_dump()
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for model_key, model_config in self.models.items():
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model_name, base_model, model_type = self.parse_key(model_key)
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model_class = self._get_implementation(base_model, model_type)
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if model_class.save_to_config:
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# TODO: or exclude_unset better fits here?
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data_to_save[model_key] = model_config.dict(exclude_defaults=True, exclude={"error"})
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data_to_save[model_key] = cast(BaseModel, model_config).model_dump(
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exclude_defaults=True, exclude={"error"}, mode="json"
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)
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# alias for config file
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data_to_save[model_key]["format"] = data_to_save[model_key].pop("model_format")
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@ -2,7 +2,7 @@ import inspect
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from enum import Enum
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from typing import Literal, get_origin
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from pydantic import BaseModel
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from pydantic import BaseModel, ConfigDict, create_model
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from .base import ( # noqa: F401
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BaseModelType,
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@ -106,6 +106,8 @@ class OpenAPIModelInfoBase(BaseModel):
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base_model: BaseModelType
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model_type: ModelType
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model_config = ConfigDict(protected_namespaces=())
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for base_model, models in MODEL_CLASSES.items():
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for model_type, model_class in models.items():
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@ -121,17 +123,11 @@ for base_model, models in MODEL_CLASSES.items():
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if openapi_cfg_name in vars():
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continue
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api_wrapper = type(
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api_wrapper = create_model(
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openapi_cfg_name,
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(cfg, OpenAPIModelInfoBase),
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dict(
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__annotations__=dict(
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model_type=Literal[model_type.value],
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),
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),
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__base__=(cfg, OpenAPIModelInfoBase),
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model_type=(Literal[model_type], model_type), # type: ignore
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)
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# globals()[openapi_cfg_name] = api_wrapper
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vars()[openapi_cfg_name] = api_wrapper
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OPENAPI_MODEL_CONFIGS.append(api_wrapper)
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@ -19,7 +19,7 @@ from diffusers import logging as diffusers_logging
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from onnx import numpy_helper
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from onnxruntime import InferenceSession, SessionOptions, get_available_providers
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from picklescan.scanner import scan_file_path
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from pydantic import BaseModel, Field
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from pydantic import BaseModel, ConfigDict, Field
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from transformers import logging as transformers_logging
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@ -86,14 +86,21 @@ class ModelError(str, Enum):
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NotFound = "not_found"
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def model_config_json_schema_extra(schema: dict[str, Any]) -> None:
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if "required" not in schema:
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schema["required"] = []
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schema["required"].append("model_type")
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class ModelConfigBase(BaseModel):
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path: str # or Path
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description: Optional[str] = Field(None)
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model_format: Optional[str] = Field(None)
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error: Optional[ModelError] = Field(None)
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class Config:
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use_enum_values = True
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model_config = ConfigDict(
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use_enum_values=True, protected_namespaces=(), json_schema_extra=model_config_json_schema_extra
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)
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class EmptyConfigLoader(ConfigMixin):
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@ -58,14 +58,16 @@ class IPAdapterModel(ModelBase):
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def get_model(
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self,
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torch_dtype: Optional[torch.dtype],
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torch_dtype: torch.dtype,
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child_type: Optional[SubModelType] = None,
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) -> typing.Union[IPAdapter, IPAdapterPlus]:
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if child_type is not None:
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raise ValueError("There are no child models in an IP-Adapter model.")
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model = build_ip_adapter(
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ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"), device="cpu", dtype=torch_dtype
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ip_adapter_ckpt_path=os.path.join(self.model_path, "ip_adapter.bin"),
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device=torch.device("cpu"),
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dtype=torch_dtype,
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)
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self.model_size = model.calc_size()
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@ -96,7 +96,7 @@ def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL], seamless_axe
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finally:
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for module, orig_conv_forward in to_restore:
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module._conv_forward = orig_conv_forward
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if hasattr(m, "asymmetric_padding_mode"):
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del m.asymmetric_padding_mode
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if hasattr(m, "asymmetric_padding"):
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del m.asymmetric_padding
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if hasattr(module, "asymmetric_padding_mode"):
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del module.asymmetric_padding_mode
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if hasattr(module, "asymmetric_padding"):
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del module.asymmetric_padding
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@ -1,7 +1,8 @@
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import math
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from typing import Optional
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import PIL
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import torch
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from PIL import Image
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from torchvision.transforms.functional import InterpolationMode
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from torchvision.transforms.functional import resize as tv_resize
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@ -11,7 +12,7 @@ class AttentionMapSaver:
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self.token_ids = token_ids
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self.latents_shape = latents_shape
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# self.collated_maps = #torch.zeros([len(token_ids), latents_shape[0], latents_shape[1]])
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self.collated_maps = {}
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self.collated_maps: dict[str, torch.Tensor] = {}
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def clear_maps(self):
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self.collated_maps = {}
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@ -38,9 +39,10 @@ class AttentionMapSaver:
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def write_maps_to_disk(self, path: str):
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pil_image = self.get_stacked_maps_image()
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pil_image.save(path, "PNG")
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if pil_image is not None:
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pil_image.save(path, "PNG")
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def get_stacked_maps_image(self) -> PIL.Image:
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def get_stacked_maps_image(self) -> Optional[Image.Image]:
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"""
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Scale all collected attention maps to the same size, blend them together and return as an image.
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:return: An image containing a vertical stack of blended attention maps, one for each requested token.
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@ -95,4 +97,4 @@ class AttentionMapSaver:
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return None
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merged_bytes = merged.mul(0xFF).byte()
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return PIL.Image.fromarray(merged_bytes.numpy(), mode="L")
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return Image.fromarray(merged_bytes.numpy(), mode="L")
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