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https://github.com/invoke-ai/InvokeAI
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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.
72 lines
2.3 KiB
Python
72 lines
2.3 KiB
Python
from math import ceil, floor, sqrt
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from PIL import Image
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class InitImageResizer:
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"""Simple class to create resized copies of an Image while preserving the aspect ratio."""
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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.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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maintained. If neither width nor height are provided,
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then returns a copy of the original image. If one or the other is
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provided, then the other will be calculated from the
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aspect ratio.
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Everything is floored to the nearest multiple of 64 so
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that it can be passed to img2img()
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"""
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im = self.image
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ar = im.width / float(im.height)
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# Infer missing values from aspect ratio
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if not (width or height): # both missing
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width = im.width
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height = im.height
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elif not height: # height missing
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height = int(width / ar)
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elif not width: # width missing
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width = int(height * ar)
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w_scale = width / im.width
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h_scale = height / im.height
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scale = min(w_scale, h_scale)
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(rw, rh) = (int(scale * im.width), int(scale * im.height))
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# round everything to multiples of 64
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width, height, rw, rh = map(lambda x: x - x % 64, (width, height, rw, rh))
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# no resize necessary, but return a copy
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if im.width == width and im.height == height:
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return im.copy()
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# otherwise resize the original image so that it fits inside the bounding box
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resized_image = self.image.resize((rw, rh), resample=Image.Resampling.LANCZOS)
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return resized_image
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def make_grid(image_list, rows=None, cols=None):
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image_cnt = len(image_list)
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if None in (rows, cols):
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rows = floor(sqrt(image_cnt)) # try to make it square
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cols = ceil(image_cnt / rows)
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width = image_list[0].width
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height = image_list[0].height
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grid_img = Image.new("RGB", (width * cols, height * rows))
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i = 0
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for r in range(0, rows):
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for c in range(0, cols):
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if i >= len(image_list):
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break
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grid_img.paste(image_list[i], (c * width, r * height))
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i = i + 1
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return grid_img
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