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feat(nodes): move all invocation metadata (type, title, tags, category) to decorator

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All invocation metadata (type, title, tags and category) are now defined in decorators.

The decorators add the `type: Literal["invocation_type"]: "invocation_type"` field to the invocation.

Category is a new invocation metadata, but it is not used by the frontend just yet.

- `@invocation()` decorator for invocations

```py
@invocation(
    "sdxl_compel_prompt",
    title="SDXL Prompt",
    tags=["sdxl", "compel", "prompt"],
    category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
    ...
```

- `@invocation_output()` decorator for invocation outputs

```py
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
    ...
```

- update invocation docs
- add category to decorator
- regen frontend types
This commit is contained in:
psychedelicious 2023-08-30 18:35:12 +10:00
parent ae05d34584
commit 044d4c107a
23 changed files with 1523 additions and 2178 deletions
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639
docs/contributing/INVOCATIONS.md
View File

@ -29,12 +29,13 @@ The first set of things we need to do when creating a new Invocation are -
- Create a new class that derives from a predefined parent class called
`BaseInvocation`.
- The name of every Invocation must end with the word `Invocation` in order for
it to be recognized as an Invocation.
- Every Invocation must have a `docstring` that describes what this Invocation
does.
- Every Invocation must have a unique `type` field defined which becomes its
indentifier.
- While not strictly required, we suggest every invocation class name ends in
"Invocation", eg "CropImageInvocation".
- Every Invocation must use the `@invocation` decorator to provide its unique
invocation type. You may also provide its title, tags and category using the
decorator.
- Invocations are strictly typed. We make use of the native
[typing](https://docs.python.org/3/library/typing.html) library and the
installed [pydantic](https://pydantic-docs.helpmanual.io/) library for
@ -43,12 +44,11 @@ The first set of things we need to do when creating a new Invocation are -
So let us do that.
```python
from typing import Literal
from .baseinvocation import BaseInvocation
from .baseinvocation import BaseInvocation, invocation
@invocation('resize')
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
type: Literal['resize'] = 'resize'
```
That's great.
@ -62,8 +62,10 @@ our Invocation takes.
### **Inputs**
Every Invocation input is a pydantic `Field` and like everything else should be
strictly typed and defined.
Every Invocation input must be defined using the `InputField` function. This is
a wrapper around the pydantic `Field` function, which handles a few extra things
and provides type hints. Like everything else, this should be strictly typed and
defined.
So let us create these inputs for our Invocation. First up, the `image` input we
need. Generally, we can use standard variable types in Python but InvokeAI
@ -76,55 +78,51 @@ create your own custom field types later in this guide. For now, let's go ahead
and use it.
```python
from typing import Literal, Union
from pydantic import Field
from .baseinvocation import BaseInvocation
from ..models.image import ImageField
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
@invocation('resize')
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
type: Literal['resize'] = 'resize'
# Inputs
image: Union[ImageField, None] = Field(description="The input image", default=None)
image: ImageField = InputField(description="The input image")
```
Let us break down our input code.
```python
image: Union[ImageField, None] = Field(description="The input image", default=None)
image: ImageField = InputField(description="The input image")
```
| Part | Value | Description |
| --------- | ---------------------------------------------------- | -------------------------------------------------------------------------------------------------- |
| Name | `image` | The variable that will hold our image |
| Type Hint | `Union[ImageField, None]` | The types for our field. Indicates that the image can either be an `ImageField` type or `None` |
| Field | `Field(description="The input image", default=None)` | The image variable is a field which needs a description and a default value that we set to `None`. |
| Part | Value | Description |
| --------- | ------------------------------------------- | ------------------------------------------------------------------------------- |
| Name | `image` | The variable that will hold our image |
| Type Hint | `ImageField` | The types for our field. Indicates that the image must be an `ImageField` type. |
| Field | `InputField(description="The input image")` | The image variable is an `InputField` which needs a description. |
Great. Now let us create our other inputs for `width` and `height`
```python
from typing import Literal, Union
from pydantic import Field
from .baseinvocation import BaseInvocation
from ..models.image import ImageField
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
@invocation('resize')
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
type: Literal['resize'] = 'resize'
# Inputs
image: Union[ImageField, None] = Field(description="The input image", default=None)
width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
image: ImageField = InputField(description="The input image")
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
```
As you might have noticed, we added two new parameters to the field type for
`width` and `height` called `gt` and `le`. These basically stand for _greater
than or equal to_ and _less than or equal to_. There are various other param
types for field that you can find on the **pydantic** documentation.
As you might have noticed, we added two new arguments to the `InputField`
definition for `width` and `height`, called `gt` and `le`. They stand for
_greater than or equal to_ and _less than or equal to_.
These impose contraints on those fields, and will raise an exception if the
values do not meet the constraints. Field constraints are provided by
**pydantic**, so anything you see in the **pydantic docs** will work.
**Note:** _Any time it is possible to define constraints for our field, we
should do it so the frontend has more information on how to parse this field._
@ -141,20 +139,17 @@ that are provided by it by InvokeAI.
Let us create this function first.
```python
from typing import Literal, Union
from pydantic import Field
from .baseinvocation import BaseInvocation, InvocationContext
from ..models.image import ImageField
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
@invocation('resize')
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
type: Literal['resize'] = 'resize'
# Inputs
image: Union[ImageField, None] = Field(description="The input image", default=None)
width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
image: ImageField = InputField(description="The input image")
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
def invoke(self, context: InvocationContext):
pass
@ -173,21 +168,18 @@ all the necessary info related to image outputs. So let us use that.
We will cover how to create your own output types later in this guide.
```python
from typing import Literal, Union
from pydantic import Field
from .baseinvocation import BaseInvocation, InvocationContext
from ..models.image import ImageField
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
from .image import ImageOutput
@invocation('resize')
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
type: Literal['resize'] = 'resize'
# Inputs
image: Union[ImageField, None] = Field(description="The input image", default=None)
width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
image: ImageField = InputField(description="The input image")
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
def invoke(self, context: InvocationContext) -> ImageOutput:
pass
@ -195,39 +187,34 @@ class ResizeInvocation(BaseInvocation):
Perfect. Now that we have our Invocation setup, let us do what we want to do.
- We will first load the image. Generally we do this using the `PIL` library but
we can use one of the services provided by InvokeAI to load the image.
- We will first load the image using one of the services provided by InvokeAI to
load the image.
- We will resize the image using `PIL` to our input data.
- We will output this image in the format we set above.
So let's do that.
```python
from typing import Literal, Union
from pydantic import Field
from .baseinvocation import BaseInvocation, InvocationContext
from ..models.image import ImageField, ResourceOrigin, ImageCategory
from .baseinvocation import BaseInvocation, InputField, invocation
from .primitives import ImageField
from .image import ImageOutput
@invocation("resize")
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
type: Literal['resize'] = 'resize'
"""Resizes an image"""
# Inputs
image: Union[ImageField, None] = Field(description="The input image", default=None)
width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
image: ImageField = InputField(description="The input image")
width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
def invoke(self, context: InvocationContext) -> ImageOutput:
# Load the image using InvokeAI's predefined Image Service.
image = context.services.images.get_pil_image(self.image.image_origin, self.image.image_name)
# Load the image using InvokeAI's predefined Image Service. Returns the PIL image.
image = context.services.images.get_pil_image(self.image.image_name)
# Resizing the image
# Because we used the above service, we already have a PIL image. So we can simply resize.
resized_image = image.resize((self.width, self.height))
# Preparing the image for output using InvokeAI's predefined Image Service.
# Save the image using InvokeAI's predefined Image Service. Returns the prepared PIL image.
output_image = context.services.images.create(
image=resized_image,
image_origin=ResourceOrigin.INTERNAL,
@ -241,7 +228,6 @@ class ResizeInvocation(BaseInvocation):
return ImageOutput(
image=ImageField(
image_name=output_image.image_name,
image_origin=output_image.image_origin,
),
width=output_image.width,
height=output_image.height,
@ -253,6 +239,20 @@ certain way that the images need to be dispatched in order to be stored and read
correctly. In 99% of the cases when dealing with an image output, you can simply
copy-paste the template above.
### Customization
We can use the `@invocation` decorator to provide some additional info to the
UI, like a custom title, tags and category.
```python
@invocation("resize", title="My Resizer", tags=["resize", "image"], category="My Invocations")
class ResizeInvocation(BaseInvocation):
"""Resizes an image"""
image: ImageField = InputField(description="The input image")
...
```
That's it. You made your own **Resize Invocation**.
## Result
@ -271,10 +271,57 @@ new Invocation ready to be used.
![resize node editor](../assets/contributing/resize_node_editor.png)
## Contributing Nodes
Once you've created a Node, the next step is to share it with the community! The best way to do this is to submit a Pull Request to add the Node to the [Community Nodes](nodes/communityNodes) list. If you're not sure how to do that, take a look a at our [contributing nodes overview](contributingNodes).
Once you've created a Node, the next step is to share it with the community! The
best way to do this is to submit a Pull Request to add the Node to the
[Community Nodes](nodes/communityNodes) list. If you're not sure how to do that,
take a look a at our [contributing nodes overview](contributingNodes).
## Advanced
-->
### Custom Output Types
Like with custom inputs, sometimes you might find yourself needing custom
outputs that InvokeAI does not provide. We can easily set one up.
Now that you are familiar with Invocations and Inputs, let us use that knowledge
to create an output that has an `image` field, a `color` field and a `string`
field.
- An invocation output is a class that derives from the parent class of
`BaseInvocationOutput`.
- All invocation outputs must use the `@invocation_output` decorator to provide
their unique output type.
- Output fields must use the provided `OutputField` function. This is very
similar to the `InputField` function described earlier - it's a wrapper around
`pydantic`'s `Field()`.
- It is not mandatory but we recommend using names ending with `Output` for
output types.
- It is not mandatory but we highly recommend adding a `docstring` to describe
what your output type is for.
Now that we know the basic rules for creating a new output type, let us go ahead
and make it.
```python
from .baseinvocation import BaseInvocationOutput, OutputField, invocation_output
from .primitives import ImageField, ColorField
@invocation_output('image_color_string_output')
class ImageColorStringOutput(BaseInvocationOutput):
'''Base class for nodes that output a single image'''
image: ImageField = OutputField(description="The image")
color: ColorField = OutputField(description="The color")
text: str = OutputField(description="The string")
```
That's all there is to it.
<!-- TODO: DANGER - we probably do not want people to create their own field types, because this requires a lot of work on the frontend to accomodate.
### Custom Input Fields
Now that you know how to create your own Invocations, let us dive into slightly
@ -329,172 +376,6 @@ like this.
color: ColorField = Field(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
```
**Extra Config**
All input fields also take an additional `Config` class that you can use to do
various advanced things like setting required parameters and etc.
Let us do that for our _ColorField_ and enforce all the values because we did
not define any defaults for our fields.
```python
class ColorField(BaseModel):
'''A field that holds the rgba values of a color'''
r: int = Field(ge=0, le=255, description="The red channel")
g: int = Field(ge=0, le=255, description="The green channel")
b: int = Field(ge=0, le=255, description="The blue channel")
a: int = Field(ge=0, le=255, description="The alpha channel")
class Config:
schema_extra = {"required": ["r", "g", "b", "a"]}
```
Now it becomes mandatory for the user to supply all the values required by our
input field.
We will discuss the `Config` class in extra detail later in this guide and how
you can use it to make your Invocations more robust.
### Custom Output Types
Like with custom inputs, sometimes you might find yourself needing custom
outputs that InvokeAI does not provide. We can easily set one up.
Now that you are familiar with Invocations and Inputs, let us use that knowledge
to put together a custom output type for an Invocation that returns _width_,
_height_ and _background_color_ that we need to create a blank image.
- A custom output type is a class that derives from the parent class of
`BaseInvocationOutput`.
- It is not mandatory but we recommend using names ending with `Output` for
output types. So we'll call our class `BlankImageOutput`
- It is not mandatory but we highly recommend adding a `docstring` to describe
what your output type is for.
- Like Invocations, each output type should have a `type` variable that is
**unique**
Now that we know the basic rules for creating a new output type, let us go ahead
and make it.
```python
from typing import Literal
from pydantic import Field
from .baseinvocation import BaseInvocationOutput
class BlankImageOutput(BaseInvocationOutput):
'''Base output type for creating a blank image'''
type: Literal['blank_image_output'] = 'blank_image_output'
# Inputs
width: int = Field(description='Width of blank image')
height: int = Field(description='Height of blank image')
bg_color: ColorField = Field(description='Background color of blank image')
class Config:
schema_extra = {"required": ["type", "width", "height", "bg_color"]}
```
All set. We now have an output type that requires what we need to create a
blank_image. And if you noticed it, we even used the `Config` class to ensure
the fields are required.
### Custom Configuration
As you might have noticed when making inputs and outputs, we used a class called
`Config` from _pydantic_ to further customize them. Because our inputs and
outputs essentially inherit from _pydantic_'s `BaseModel` class, all
[configuration options](https://docs.pydantic.dev/latest/usage/schema/#schema-customization)
that are valid for _pydantic_ classes are also valid for our inputs and outputs.
You can do the same for your Invocations too but InvokeAI makes our life a
little bit easier on that end.
InvokeAI provides a custom configuration class called `InvocationConfig`
particularly for configuring Invocations. This is exactly the same as the raw
`Config` class from _pydantic_ with some extra stuff on top to help faciliate
parsing of the scheme in the frontend UI.
At the current moment, tihs `InvocationConfig` class is further improved with
the following features related the `ui`.
| Config Option | Field Type | Example |
| ------------- | ------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- |
| type_hints | `Dict[str, Literal["integer", "float", "boolean", "string", "enum", "image", "latents", "model", "control"]]` | `type_hint: "model"` provides type hints related to the model like displaying a list of available models |
| tags | `List[str]` | `tags: ['resize', 'image']` will classify your invocation under the tags of resize and image. |
| title | `str` | `title: 'Resize Image` will rename your to this custom title rather than infer from the name of the Invocation class. |
So let us update your `ResizeInvocation` with some extra configuration and see
how that works.
```python
from typing import Literal, Union
from pydantic import Field
from .baseinvocation import BaseInvocation, InvocationContext, InvocationConfig
from ..models.image import ImageField, ResourceOrigin, ImageCategory
from .image import ImageOutput
class ResizeInvocation(BaseInvocation):
'''Resizes an image'''
type: Literal['resize'] = 'resize'
# Inputs
image: Union[ImageField, None] = Field(description="The input image", default=None)
width: int = Field(default=512, ge=64, le=2048, description="Width of the new image")
height: int = Field(default=512, ge=64, le=2048, description="Height of the new image")
class Config(InvocationConfig):
schema_extra: {
ui: {
tags: ['resize', 'image'],
title: ['My Custom Resize']
}
}
def invoke(self, context: InvocationContext) -> ImageOutput:
# Load the image using InvokeAI's predefined Image Service.
image = context.services.images.get_pil_image(self.image.image_origin, self.image.image_name)
# Resizing the image
# Because we used the above service, we already have a PIL image. So we can simply resize.
resized_image = image.resize((self.width, self.height))
# Preparing the image for output using InvokeAI's predefined Image Service.
output_image = context.services.images.create(
image=resized_image,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
# Returning the Image
return ImageOutput(
image=ImageField(
image_name=output_image.image_name,
image_origin=output_image.image_origin,
),
width=output_image.width,
height=output_image.height,
)
```
We now customized our code to let the frontend know that our Invocation falls
under `resize` and `image` categories. So when the user searches for these
particular words, our Invocation will show up too.
We also set a custom title for our Invocation. So instead of being called
`Resize`, it will be called `My Custom Resize`.
As simple as that.
As time goes by, InvokeAI will further improve and add more customizability for
Invocation configuration. We will have more documentation regarding this at a
later time.
# **[TODO]**
### Custom Components For Frontend
Every backend input type should have a corresponding frontend component so the
@ -513,282 +394,4 @@ Let us create a new component for our custom color field we created above. When
we use a color field, let us say we want the UI to display a color picker for
the user to pick from rather than entering values. That is what we will build
now.
---
<!-- # OLD -- TO BE DELETED OR MOVED LATER
---
## Creating a new invocation
To create a new invocation, either find the appropriate module file in
`/ldm/invoke/app/invocations` to add your invocation to, or create a new one in
that folder. All invocations in that folder will be discovered and made
available to the CLI and API automatically. Invocations make use of
[typing](https://docs.python.org/3/library/typing.html) and
[pydantic](https://pydantic-docs.helpmanual.io/) for validation and integration
into the CLI and API.
An invocation looks like this:
```py
class UpscaleInvocation(BaseInvocation):
"""Upscales an image."""
# fmt: off
type: Literal["upscale"] = "upscale"
# Inputs
image: Union[ImageField, None] = Field(description="The input image", default=None)
strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
level: Literal[2, 4] = Field(default=2, description="The upscale level")
# fmt: on
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["upscaling", "image"],
},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(
self.image.image_origin, self.image.image_name
)
results = context.services.restoration.upscale_and_reconstruct(
image_list=[[image, 0]],
upscale=(self.level, self.strength),
strength=0.0, # GFPGAN strength
save_original=False,
image_callback=None,
)
# Results are image and seed, unwrap for now
# TODO: can this return multiple results?
image_dto = context.services.images.create(
image=results[0][0],
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
return ImageOutput(
image=ImageField(
image_name=image_dto.image_name,
image_origin=image_dto.image_origin,
),
width=image_dto.width,
height=image_dto.height,
)
```
Each portion is important to implement correctly.
### Class definition and type
```py
class UpscaleInvocation(BaseInvocation):
"""Upscales an image."""
type: Literal['upscale'] = 'upscale'
```
All invocations must derive from `BaseInvocation`. They should have a docstring
that declares what they do in a single, short line. They should also have a
`type` with a type hint that's `Literal["command_name"]`, where `command_name`
is what the user will type on the CLI or use in the API to create this
invocation. The `command_name` must be unique. The `type` must be assigned to
the value of the literal in the type hint.
### Inputs
```py
# Inputs
image: Union[ImageField,None] = Field(description="The input image")
strength: float = Field(default=0.75, gt=0, le=1, description="The strength")
level: Literal[2,4] = Field(default=2, description="The upscale level")
```
Inputs consist of three parts: a name, a type hint, and a `Field` with default,
description, and validation information. For example:
| Part | Value | Description |
| --------- | ------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------- |
| Name | `strength` | This field is referred to as `strength` |
| Type Hint | `float` | This field must be of type `float` |
| Field | `Field(default=0.75, gt=0, le=1, description="The strength")` | The default value is `0.75`, the value must be in the range (0,1], and help text will show "The strength" for this field. |
Notice that `image` has type `Union[ImageField,None]`. The `Union` allows this
field to be parsed with `None` as a value, which enables linking to previous
invocations. All fields should either provide a default value or allow `None` as
a value, so that they can be overwritten with a linked output from another
invocation.
The special type `ImageField` is also used here. All images are passed as
`ImageField`, which protects them from pydantic validation errors (since images
only ever come from links).
Finally, note that for all linking, the `type` of the linked fields must match.
If the `name` also matches, then the field can be **automatically linked** to a
previous invocation by name and matching.
### Config
```py
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["upscaling", "image"],
},
}
```
This is an optional configuration for the invocation. It inherits from
pydantic's model `Config` class, and it used primarily to customize the
autogenerated OpenAPI schema.
The UI relies on the OpenAPI schema in two ways:
- An API client & Typescript types are generated from it. This happens at build
time.
- The node editor parses the schema into a template used by the UI to create the
node editor UI. This parsing happens at runtime.
In this example, a `ui` key has been added to the `schema_extra` dict to provide
some tags for the UI, to facilitate filtering nodes.
See the Schema Generation section below for more information.
### Invoke Function
```py
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(
self.image.image_origin, self.image.image_name
)
results = context.services.restoration.upscale_and_reconstruct(
image_list=[[image, 0]],
upscale=(self.level, self.strength),
strength=0.0, # GFPGAN strength
save_original=False,
image_callback=None,
)
# Results are image and seed, unwrap for now
# TODO: can this return multiple results?
image_dto = context.services.images.create(
image=results[0][0],
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
return ImageOutput(
image=ImageField(
image_name=image_dto.image_name,
image_origin=image_dto.image_origin,
),
width=image_dto.width,
height=image_dto.height,
)
```
The `invoke` function is the last portion of an invocation. It is provided an
`InvocationContext` which contains services to perform work as well as a
`session_id` for use as needed. It should return a class with output values that
derives from `BaseInvocationOutput`.
Before being called, the invocation will have all of its fields set from
defaults, inputs, and finally links (overriding in that order).
Assume that this invocation may be running simultaneously with other
invocations, may be running on another machine, or in other interesting
scenarios. If you need functionality, please provide it as a service in the
`InvocationServices` class, and make sure it can be overridden.
### Outputs
```py
class ImageOutput(BaseInvocationOutput):
"""Base class for invocations that output an image"""
# fmt: off
type: Literal["image_output"] = "image_output"
image: ImageField = Field(default=None, description="The output image")
width: int = Field(description="The width of the image in pixels")
height: int = Field(description="The height of the image in pixels")
# fmt: on
class Config:
schema_extra = {"required": ["type", "image", "width", "height"]}
```
Output classes look like an invocation class without the invoke method. Prefer
to use an existing output class if available, and prefer to name inputs the same
as outputs when possible, to promote automatic invocation linking.
## Schema Generation
Invocation, output and related classes are used to generate an OpenAPI schema.
### Required Properties
The schema generation treat all properties with default values as optional. This
makes sense internally, but when when using these classes via the generated
schema, we end up with e.g. the `ImageOutput` class having its `image` property
marked as optional.
We know that this property will always be present, so the additional logic
needed to always check if the property exists adds a lot of extraneous cruft.
To fix this, we can leverage `pydantic`'s
[schema customisation](https://docs.pydantic.dev/usage/schema/#schema-customization)
to mark properties that we know will always be present as required.
Here's that `ImageOutput` class, without the needed schema customisation:
```python
class ImageOutput(BaseInvocationOutput):
"""Base class for invocations that output an image"""
# fmt: off
type: Literal["image_output"] = "image_output"
image: ImageField = Field(default=None, description="The output image")
width: int = Field(description="The width of the image in pixels")
height: int = Field(description="The height of the image in pixels")
# fmt: on
```
The OpenAPI schema that results from this `ImageOutput` will have the `type`,
`image`, `width` and `height` properties marked as optional, even though we know
they will always have a value.
```python
class ImageOutput(BaseInvocationOutput):
"""Base class for invocations that output an image"""
# fmt: off
type: Literal["image_output"] = "image_output"
image: ImageField = Field(default=None, description="The output image")
width: int = Field(description="The width of the image in pixels")
height: int = Field(description="The height of the image in pixels")
# fmt: on
# Add schema customization
class Config:
schema_extra = {"required": ["type", "image", "width", "height"]}
```
With the customization in place, the schema will now show these properties as
required, obviating the need for extensive null checks in client code.
See this `pydantic` issue for discussion on this solution:
<https://github.com/pydantic/pydantic/discussions/4577> -->
-->

151
invokeai/app/invocations/baseinvocation.py
View File

@ -2,16 +2,18 @@
from __future__ import annotations
import json
from abc import ABC, abstractmethod
from enum import Enum
from inspect import signature
import json
import re
from typing import (
TYPE_CHECKING,
AbstractSet,
Any,
Callable,
ClassVar,
Literal,
Mapping,
Optional,
Type,
@ -22,7 +24,7 @@ from typing import (
)
from pydantic import BaseModel, Field, validator
from pydantic.fields import Undefined
from pydantic.fields import Undefined, ModelField
from pydantic.typing import NoArgAnyCallable
if TYPE_CHECKING:
@ -368,8 +370,7 @@ def OutputField(
class UIConfigBase(BaseModel):
"""
Provides additional node configuration to the UI.
This is used internally by the @tags and @title decorator logic. You probably want to use those
decorators, though you may add this class to a node definition to specify the title and tags.
This is used internally by the @invocation decorator logic. Do not use this directly.
"""
tags: Optional[list[str]] = Field(default_factory=None, description="The node's tags")
@ -387,10 +388,11 @@ class InvocationContext:
class BaseInvocationOutput(BaseModel):
"""Base class for all invocation outputs"""
"""
Base class for all invocation outputs.
# All outputs must include a type name like this:
# type: Literal['your_output_name'] # noqa f821
All invocation outputs must use the `@invocation_output` decorator to provide their unique type.
"""
@classmethod
def get_all_subclasses_tuple(cls):
@ -426,12 +428,12 @@ class MissingInputException(Exception):
class BaseInvocation(ABC, BaseModel):
"""A node to process inputs and produce outputs.
May use dependency injection in __init__ to receive providers.
"""
A node to process inputs and produce outputs.
May use dependency injection in __init__ to receive providers.
# All invocations must include a type name like this:
# type: Literal['your_output_name'] # noqa f821
All invocations must use the `@invocation` decorator to provide their unique type.
"""
@classmethod
def get_all_subclasses(cls):
@ -511,9 +513,11 @@ class BaseInvocation(ABC, BaseModel):
raise MissingInputException(self.__fields__["type"].default, field_name)
return self.invoke(context)
id: str = Field(description="The id of this node. Must be unique among all nodes.")
id: str = Field(
description="The id of this instance of an invocation. Must be unique among all instances of invocations."
)
is_intermediate: bool = InputField(
default=False, description="Whether or not this node is an intermediate node.", ui_type=UIType.IsIntermediate
default=False, description="Whether or not this is an intermediate invocation.", ui_type=UIType.IsIntermediate
)
workflow: Optional[str] = InputField(
default=None,
@ -534,66 +538,85 @@ class BaseInvocation(ABC, BaseModel):
UIConfig: ClassVar[Type[UIConfigBase]]
T = TypeVar("T", bound=BaseInvocation)
GenericBaseInvocation = TypeVar("GenericBaseInvocation", bound=BaseInvocation)
def title(title: str) -> Callable[[Type[T]], Type[T]]:
"""Adds a title to the invocation. Use this to override the default title generation, which is based on the class name."""
def wrapper(cls: Type[T]) -> Type[T]:
uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
cls.UIConfig = type(uiconf_name, (UIConfigBase,), dict())
cls.UIConfig.title = title
return cls
return wrapper
def tags(*tags: str) -> Callable[[Type[T]], Type[T]]:
"""Adds tags to the invocation. Use this to improve the streamline finding the invocation in the UI."""
def wrapper(cls: Type[T]) -> Type[T]:
uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
cls.UIConfig = type(uiconf_name, (UIConfigBase,), dict())
cls.UIConfig.tags = list(tags)
return cls
return wrapper
def category(category: str) -> Callable[[Type[T]], Type[T]]:
"""Adds a category to the invocation. This is used to group invocations in the UI."""
def wrapper(cls: Type[T]) -> Type[T]:
uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
cls.UIConfig = type(uiconf_name, (UIConfigBase,), dict())
cls.UIConfig.category = category
return cls
return wrapper
def node(
title: Optional[str] = None, tags: Optional[list[str]] = None, category: Optional[str] = None
) -> Callable[[Type[T]], Type[T]]:
def invocation(
invocation_type: str, title: Optional[str] = None, tags: Optional[list[str]] = None, category: Optional[str] = None
) -> Callable[[Type[GenericBaseInvocation]], Type[GenericBaseInvocation]]:
"""
Adds metadata to the invocation as a decorator.
Adds metadata to an invocation.
:param Optional[str] title: Adds a title to the node. Use if the auto-generated title isn't quite right. Defaults to None.
:param Optional[list[str]] tags: Adds tags to the node. Nodes may be searched for by their tags. Defaults to None.
:param Optional[str] category: Adds a category to the node. Used to group the nodes in the UI. Defaults to None.
:param str invocation_type: The type of the invocation. Must be unique among all invocations.
:param Optional[str] title: Adds a title to the invocation. Use if the auto-generated title isn't quite right. Defaults to None.
:param Optional[list[str]] tags: Adds tags to the invocation. Invocations may be searched for by their tags. Defaults to None.
:param Optional[str] category: Adds a category to the invocation. Used to group the invocations in the UI. Defaults to None.
"""
def wrapper(cls: Type[T]) -> Type[T]:
def wrapper(cls: Type[GenericBaseInvocation]) -> Type[GenericBaseInvocation]:
# Validate invocation types on creation of invocation classes
# TODO: ensure unique?
if re.compile(r"^\S+$").match(invocation_type) is None:
raise ValueError(f'"invocation_type" must consist of non-whitespace characters, got "{invocation_type}"')
# Add OpenAPI schema extras
uiconf_name = cls.__qualname__ + ".UIConfig"
if not hasattr(cls, "UIConfig") or cls.UIConfig.__qualname__ != uiconf_name:
cls.UIConfig = type(uiconf_name, (UIConfigBase,), dict())
cls.UIConfig.title = title
cls.UIConfig.tags = tags
cls.UIConfig.category = category
if title is not None:
cls.UIConfig.title = title
if tags is not None:
cls.UIConfig.tags = tags
if category is not None:
cls.UIConfig.category = category
# Add the invocation type to the pydantic model of the invocation
invocation_type_annotation = Literal[invocation_type] # type: ignore
invocation_type_field = ModelField.infer(
name="type",
value=invocation_type,
annotation=invocation_type_annotation,
class_validators=None,
config=cls.__config__,
)
cls.__fields__.update({"type": invocation_type_field})
cls.__annotations__.update({"type": invocation_type_annotation})
return cls
return wrapper
GenericBaseInvocationOutput = TypeVar("GenericBaseInvocationOutput", bound=BaseInvocationOutput)
def invocation_output(
output_type: str,
) -> Callable[[Type[GenericBaseInvocationOutput]], Type[GenericBaseInvocationOutput]]:
"""
Adds metadata to an invocation output.
:param str output_type: The type of the invocation output. Must be unique among all invocation outputs.
"""
def wrapper(cls: Type[GenericBaseInvocationOutput]) -> Type[GenericBaseInvocationOutput]:
# Validate output types on creation of invocation output classes
# TODO: ensure unique?
if re.compile(r"^\S+$").match(output_type) is None:
raise ValueError(f'"output_type" must consist of non-whitespace characters, got "{output_type}"')
# Add the output type to the pydantic model of the invocation output
output_type_annotation = Literal[output_type] # type: ignore
output_type_field = ModelField.infer(
name="type",
value=output_type,
annotation=output_type_annotation,
class_validators=None,
config=cls.__config__,
)
cls.__fields__.update({"type": output_type_field})
cls.__annotations__.update({"type": output_type_annotation})
return cls
return wrapper

28
invokeai/app/invocations/collections.py
View File

@ -1,6 +1,5 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
from typing import Literal
import numpy as np
from pydantic import validator
@ -8,16 +7,13 @@ from pydantic import validator
from invokeai.app.invocations.primitives import IntegerCollectionOutput
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from .baseinvocation import BaseInvocation, InputField, InvocationContext, node
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@node(title="Integer Range", tags=["collection", "integer", "range"], category="collections")
@invocation("range", title="Integer Range", tags=["collection", "integer", "range"], category="collections")
class RangeInvocation(BaseInvocation):
"""Creates a range of numbers from start to stop with step"""
type: Literal["range"] = "range"
# Inputs
start: int = InputField(default=0, description="The start of the range")
stop: int = InputField(default=10, description="The stop of the range")
step: int = InputField(default=1, description="The step of the range")
@ -32,13 +28,15 @@ class RangeInvocation(BaseInvocation):
return IntegerCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
@node(title="Integer Range of Size", tags=["collection", "integer", "size", "range"], category="collections")
@invocation(
"range_of_size",
title="Integer Range of Size",
tags=["collection", "integer", "size", "range"],
category="collections",
)
class RangeOfSizeInvocation(BaseInvocation):
"""Creates a range from start to start + size with step"""
type: Literal["range_of_size"] = "range_of_size"
# Inputs
start: int = InputField(default=0, description="The start of the range")
size: int = InputField(default=1, description="The number of values")
step: int = InputField(default=1, description="The step of the range")
@ -47,13 +45,15 @@ class RangeOfSizeInvocation(BaseInvocation):
return IntegerCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step)))
@node(title="Random Range", tags=["range", "integer", "random", "collection"], category="collections")
@invocation(
"random_range",
title="Random Range",
tags=["range", "integer", "random", "collection"],
category="collections",
)
class RandomRangeInvocation(BaseInvocation):
"""Creates a collection of random numbers"""
type: Literal["random_range"] = "random_range"
# Inputs
low: int = InputField(default=0, description="The inclusive low value")
high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")
size: int = InputField(default=1, description="The number of values to generate")

43
invokeai/app/invocations/compel.py
View File

@ -1,6 +1,6 @@
import re
from dataclasses import dataclass
from typing import List, Literal, Union
from typing import List, Union
import torch
from compel import Compel, ReturnedEmbeddingsType
@ -26,9 +26,8 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIComponent,
category,
tags,
title,
invocation,
invocation_output,
)
from .model import ClipField
@ -45,14 +44,10 @@ class ConditioningFieldData:
# PerpNeg = "perp_neg"
@title("Prompt")
@tags("prompt", "compel")
@category("conditioning")
@invocation("compel", title="Prompt", tags=["prompt", "compel"], category="conditioning")
class CompelInvocation(BaseInvocation):
"""Parse prompt using compel package to conditioning."""
type: Literal["compel"] = "compel"
prompt: str = InputField(
default="",
description=FieldDescriptions.compel_prompt,
@ -267,14 +262,15 @@ class SDXLPromptInvocationBase:
return c, c_pooled, ec
@title("SDXL Prompt")
@tags("sdxl", "compel", "prompt")
@category("conditioning")
@invocation(
"sdxl_compel_prompt",
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
type: Literal["sdxl_compel_prompt"] = "sdxl_compel_prompt"
prompt: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
style: str = InputField(default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea)
original_width: int = InputField(default=1024, description="")
@ -327,14 +323,15 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
)
@title("SDXL Refiner Prompt")
@tags("sdxl", "compel", "prompt")
@category("conditioning")
@invocation(
"sdxl_refiner_compel_prompt",
title="SDXL Refiner Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
)
class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
type: Literal["sdxl_refiner_compel_prompt"] = "sdxl_refiner_compel_prompt"
style: str = InputField(
default="", description=FieldDescriptions.compel_prompt, ui_component=UIComponent.Textarea
) # TODO: ?
@ -376,21 +373,17 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
)
@invocation_output("clip_skip_output")
class ClipSkipInvocationOutput(BaseInvocationOutput):
"""Clip skip node output"""
type: Literal["clip_skip_output"] = "clip_skip_output"
clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@title("CLIP Skip")
@tags("clipskip", "clip", "skip")
@category("conditioning")
@invocation("clip_skip", title="CLIP Skip", tags=["clipskip", "clip", "skip"], category="conditioning")
class ClipSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model."""
type: Literal["clip_skip"] = "clip_skip"
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection, title="CLIP")
skipped_layers: int = InputField(default=0, description=FieldDescriptions.skipped_layers)

186
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -40,10 +40,8 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIType,
category,
node,
tags,
title,
invocation,
invocation_output,
)
@ -89,22 +87,18 @@ class ControlField(BaseModel):
return v
@invocation_output("control_output")
class ControlOutput(BaseInvocationOutput):
"""node output for ControlNet info"""
type: Literal["control_output"] = "control_output"
# Outputs
control: ControlField = OutputField(description=FieldDescriptions.control)
@node(title="ControlNet", tags=["controlnet"], category="controlnet")
@invocation("controlnet", title="ControlNet", tags=["controlnet"], category="controlnet")
class ControlNetInvocation(BaseInvocation):
"""Collects ControlNet info to pass to other nodes"""
type: Literal["controlnet"] = "controlnet"
# Inputs
image: ImageField = InputField(description="The control image")
control_model: ControlNetModelField = InputField(
default="lllyasviel/sd-controlnet-canny", description=FieldDescriptions.controlnet_model, input=Input.Direct
@ -135,12 +129,10 @@ class ControlNetInvocation(BaseInvocation):
)
@invocation("image_processor", title="Base Image Processor", tags=["controlnet"], category="controlnet")
class ImageProcessorInvocation(BaseInvocation):
"""Base class for invocations that preprocess images for ControlNet"""
type: Literal["image_processor"] = "image_processor"
# Inputs
image: ImageField = InputField(description="The image to process")
def run_processor(self, image):
@ -176,15 +168,15 @@ class ImageProcessorInvocation(BaseInvocation):
)
@title("Canny Processor")
@tags("controlnet", "canny")
@category("controlnet")
@invocation(
"canny_image_processor",
title="Canny Processor",
tags=["controlnet", "canny"],
category="controlnet",
)
class CannyImageProcessorInvocation(ImageProcessorInvocation):
"""Canny edge detection for ControlNet"""
type: Literal["canny_image_processor"] = "canny_image_processor"
# Input
low_threshold: int = InputField(
default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)"
)
@ -198,15 +190,15 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("HED (softedge) Processor")
@tags("controlnet", "hed", "softedge")
@category("controlnet")
@invocation(
"hed_image_processor",
title="HED (softedge) Processor",
tags=["controlnet", "hed", "softedge"],
category="controlnet",
)
class HedImageProcessorInvocation(ImageProcessorInvocation):
"""Applies HED edge detection to image"""
type: Literal["hed_image_processor"] = "hed_image_processor"
# Inputs
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
# safe not supported in controlnet_aux v0.0.3
@ -226,15 +218,15 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Lineart Processor")
@tags("controlnet", "lineart")
@category("controlnet")
@invocation(
"lineart_image_processor",
title="Lineart Processor",
tags=["controlnet", "lineart"],
category="controlnet",
)
class LineartImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art processing to image"""
type: Literal["lineart_image_processor"] = "lineart_image_processor"
# Inputs
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
coarse: bool = InputField(default=False, description="Whether to use coarse mode")
@ -247,15 +239,15 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Lineart Anime Processor")
@tags("controlnet", "lineart", "anime")
@category("controlnet")
@invocation(
"lineart_anime_image_processor",
title="Lineart Anime Processor",
tags=["controlnet", "lineart", "anime"],
category="controlnet",
)
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art anime processing to image"""
type: Literal["lineart_anime_image_processor"] = "lineart_anime_image_processor"
# Inputs
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
@ -269,15 +261,15 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Openpose Processor")
@tags("controlnet", "openpose", "pose")
@category("controlnet")
@invocation(
"openpose_image_processor",
title="Openpose Processor",
tags=["controlnet", "openpose", "pose"],
category="controlnet",
)
class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Openpose processing to image"""
type: Literal["openpose_image_processor"] = "openpose_image_processor"
# Inputs
hand_and_face: bool = InputField(default=False, description="Whether to use hands and face mode")
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
@ -293,15 +285,15 @@ class OpenposeImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Midas (Depth) Processor")
@tags("controlnet", "midas", "depth")
@category("controlnet")
@invocation(
"midas_depth_image_processor",
title="Midas Depth Processor",
tags=["controlnet", "midas"],
category="controlnet",
)
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Midas depth processing to image"""
type: Literal["midas_depth_image_processor"] = "midas_depth_image_processor"
# Inputs
a_mult: float = InputField(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
bg_th: float = InputField(default=0.1, ge=0, description="Midas parameter `bg_th`")
# depth_and_normal not supported in controlnet_aux v0.0.3
@ -319,15 +311,15 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Normal BAE Processor")
@tags("controlnet", "normal", "bae")
@category("controlnet")
@invocation(
"normalbae_image_processor",
title="Normal BAE Processor",
tags=["controlnet"],
category="controlnet",
)
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies NormalBae processing to image"""
type: Literal["normalbae_image_processor"] = "normalbae_image_processor"
# Inputs
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
@ -339,15 +331,10 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("MLSD Processor")
@tags("controlnet", "mlsd")
@category("controlnet")
@invocation("mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet")
class MlsdImageProcessorInvocation(ImageProcessorInvocation):
"""Applies MLSD processing to image"""
type: Literal["mlsd_image_processor"] = "mlsd_image_processor"
# Inputs
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
thr_v: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_v`")
@ -365,15 +352,10 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("PIDI Processor")
@tags("controlnet", "pidi")
@category("controlnet")
@invocation("pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet")
class PidiImageProcessorInvocation(ImageProcessorInvocation):
"""Applies PIDI processing to image"""
type: Literal["pidi_image_processor"] = "pidi_image_processor"
# Inputs
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
@ -391,15 +373,15 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Content Shuffle Processor")
@tags("controlnet", "contentshuffle")
@category("controlnet")
@invocation(
"content_shuffle_image_processor",
title="Content Shuffle Processor",
tags=["controlnet", "contentshuffle"],
category="controlnet",
)
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
"""Applies content shuffle processing to image"""
type: Literal["content_shuffle_image_processor"] = "content_shuffle_image_processor"
# Inputs
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
h: Optional[int] = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
@ -420,29 +402,30 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
# should work with controlnet_aux >= 0.0.4 and timm <= 0.6.13
@title("Zoe (Depth) Processor")
@tags("controlnet", "zoe", "depth")
@category("controlnet")
@invocation(
"zoe_depth_image_processor",
title="Zoe (Depth) Processor",
tags=["controlnet", "zoe", "depth"],
category="controlnet",
)
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Zoe depth processing to image"""
type: Literal["zoe_depth_image_processor"] = "zoe_depth_image_processor"
def run_processor(self, image):
zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = zoe_depth_processor(image)
return processed_image
@title("Mediapipe Face Processor")
@tags("controlnet", "mediapipe", "face")
@category("controlnet")
@invocation(
"mediapipe_face_processor",
title="Mediapipe Face Processor",
tags=["controlnet", "mediapipe", "face"],
category="controlnet",
)
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
"""Applies mediapipe face processing to image"""
type: Literal["mediapipe_face_processor"] = "mediapipe_face_processor"
# Inputs
max_faces: int = InputField(default=1, ge=1, description="Maximum number of faces to detect")
min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
@ -456,15 +439,15 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Leres (Depth) Processor")
@tags("controlnet", "leres", "depth")
@category("controlnet")
@invocation(
"leres_image_processor",
title="Leres (Depth) Processor",
tags=["controlnet", "leres", "depth"],
category="controlnet",
)
class LeresImageProcessorInvocation(ImageProcessorInvocation):
"""Applies leres processing to image"""
type: Literal["leres_image_processor"] = "leres_image_processor"
# Inputs
thr_a: float = InputField(default=0, description="Leres parameter `thr_a`")
thr_b: float = InputField(default=0, description="Leres parameter `thr_b`")
boost: bool = InputField(default=False, description="Whether to use boost mode")
@ -484,15 +467,15 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Tile Resample Processor")
@tags("controlnet", "tile")
@category("controlnet")
@invocation(
"tile_image_processor",
title="Tile Resample Processor",
tags=["controlnet", "tile"],
category="controlnet",
)
class TileResamplerProcessorInvocation(ImageProcessorInvocation):
"""Tile resampler processor"""
type: Literal["tile_image_processor"] = "tile_image_processor"
# Inputs
# res: int = InputField(default=512, ge=0, le=1024, description="The pixel resolution for each tile")
down_sampling_rate: float = InputField(default=1.0, ge=1.0, le=8.0, description="Down sampling rate")
@ -523,14 +506,15 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
return processed_image
@title("Segment Anything Processor")
@tags("controlnet", "segmentanything")
@category("controlnet")
@invocation(
"segment_anything_processor",
title="Segment Anything Processor",
tags=["controlnet", "segmentanything"],
category="controlnet",
)
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
"""Applies segment anything processing to image"""
type: Literal["segment_anything_processor"] = "segment_anything_processor"
def run_processor(self, image):
# segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
segment_anything_processor = SamDetectorReproducibleColors.from_pretrained(

15
invokeai/app/invocations/cv.py
View File

@ -1,6 +1,5 @@
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654)
from typing import Literal
import cv2 as cv
import numpy
@ -8,18 +7,18 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, category, tags, title
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@title("OpenCV Inpaint")
@tags("opencv", "inpaint")
@category("inpaint")
@invocation(
"cv_inpaint",
title="OpenCV Inpaint",
tags=["opencv", "inpaint"],
category="inpaint",
)
class CvInpaintInvocation(BaseInvocation):
"""Simple inpaint using opencv."""
type: Literal["cv_inpaint"] = "cv_inpaint"
# Inputs
image: ImageField = InputField(description="The image to inpaint")
mask: ImageField = InputField(description="The mask to use when inpainting")

155
invokeai/app/invocations/image.py
View File

@ -13,18 +13,13 @@ from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.safety_checker import SafetyChecker
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, tags, title
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
@title("Show Image")
@tags("image")
@invocation("show_image", title="Show Image", tags=["image"], category="image")
class ShowImageInvocation(BaseInvocation):
"""Displays a provided image, and passes it forward in the pipeline."""
"""Displays a provided image using the OS image viewer, and passes it forward in the pipeline."""
# Metadata
type: Literal["show_image"] = "show_image"
# Inputs
image: ImageField = InputField(description="The image to show")
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -41,15 +36,10 @@ class ShowImageInvocation(BaseInvocation):
)
@title("Blank Image")
@tags("image")
@invocation("blank_image", title="Blank Image", tags=["image"], category="image")
class BlankImageInvocation(BaseInvocation):
"""Creates a blank image and forwards it to the pipeline"""
# Metadata
type: Literal["blank_image"] = "blank_image"
# Inputs
width: int = InputField(default=512, description="The width of the image")
height: int = InputField(default=512, description="The height of the image")
mode: Literal["RGB", "RGBA"] = InputField(default="RGB", description="The mode of the image")
@ -75,15 +65,10 @@ class BlankImageInvocation(BaseInvocation):
)
@title("Crop Image")
@tags("image", "crop")
@invocation("img_crop", title="Crop Image", tags=["image", "crop"], category="image")
class ImageCropInvocation(BaseInvocation):
"""Crops an image to a specified box. The box can be outside of the image."""
# Metadata
type: Literal["img_crop"] = "img_crop"
# Inputs
image: ImageField = InputField(description="The image to crop")
x: int = InputField(default=0, description="The left x coordinate of the crop rectangle")
y: int = InputField(default=0, description="The top y coordinate of the crop rectangle")
@ -113,15 +98,10 @@ class ImageCropInvocation(BaseInvocation):
)
@title("Paste Image")
@tags("image", "paste")
@invocation("img_paste", title="Paste Image", tags=["image", "paste"], category="image")
class ImagePasteInvocation(BaseInvocation):
"""Pastes an image into another image."""
# Metadata
type: Literal["img_paste"] = "img_paste"
# Inputs
base_image: ImageField = InputField(description="The base image")
image: ImageField = InputField(description="The image to paste")
mask: Optional[ImageField] = InputField(
@ -166,15 +146,10 @@ class ImagePasteInvocation(BaseInvocation):
)
@title("Mask from Alpha")
@tags("image", "mask")
@invocation("tomask", title="Mask from Alpha", tags=["image", "mask"], category="image")
class MaskFromAlphaInvocation(BaseInvocation):
"""Extracts the alpha channel of an image as a mask."""
# Metadata
type: Literal["tomask"] = "tomask"
# Inputs
image: ImageField = InputField(description="The image to create the mask from")
invert: bool = InputField(default=False, description="Whether or not to invert the mask")
@ -202,15 +177,10 @@ class MaskFromAlphaInvocation(BaseInvocation):
)
@title("Multiply Images")
@tags("image", "multiply")
@invocation("img_mul", title="Multiply Images", tags=["image", "multiply"], category="image")
class ImageMultiplyInvocation(BaseInvocation):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
# Metadata
type: Literal["img_mul"] = "img_mul"
# Inputs
image1: ImageField = InputField(description="The first image to multiply")
image2: ImageField = InputField(description="The second image to multiply")
@ -240,15 +210,10 @@ class ImageMultiplyInvocation(BaseInvocation):
IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
@title("Extract Image Channel")
@tags("image", "channel")
@invocation("img_chan", title="Extract Image Channel", tags=["image", "channel"], category="image")
class ImageChannelInvocation(BaseInvocation):
"""Gets a channel from an image."""
# Metadata
type: Literal["img_chan"] = "img_chan"
# Inputs
image: ImageField = InputField(description="The image to get the channel from")
channel: IMAGE_CHANNELS = InputField(default="A", description="The channel to get")
@ -277,15 +242,10 @@ class ImageChannelInvocation(BaseInvocation):
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
@title("Convert Image Mode")
@tags("image", "convert")
@invocation("img_conv", title="Convert Image Mode", tags=["image", "convert"], category="image")
class ImageConvertInvocation(BaseInvocation):
"""Converts an image to a different mode."""
# Metadata
type: Literal["img_conv"] = "img_conv"
# Inputs
image: ImageField = InputField(description="The image to convert")
mode: IMAGE_MODES = InputField(default="L", description="The mode to convert to")
@ -311,15 +271,10 @@ class ImageConvertInvocation(BaseInvocation):
)
@title("Blur Image")
@tags("image", "blur")
@invocation("img_blur", title="Blur Image", tags=["image", "blur"], category="image")
class ImageBlurInvocation(BaseInvocation):
"""Blurs an image"""
# Metadata
type: Literal["img_blur"] = "img_blur"
# Inputs
image: ImageField = InputField(description="The image to blur")
radius: float = InputField(default=8.0, ge=0, description="The blur radius")
# Metadata
@ -370,15 +325,10 @@ PIL_RESAMPLING_MAP = {
}
@title("Resize Image")
@tags("image", "resize")
@invocation("img_resize", title="Resize Image", tags=["image", "resize"], category="image")
class ImageResizeInvocation(BaseInvocation):
"""Resizes an image to specific dimensions"""
# Metadata
type: Literal["img_resize"] = "img_resize"
# Inputs
image: ImageField = InputField(description="The image to resize")
width: int = InputField(default=512, ge=64, multiple_of=8, description="The width to resize to (px)")
height: int = InputField(default=512, ge=64, multiple_of=8, description="The height to resize to (px)")
@ -415,15 +365,10 @@ class ImageResizeInvocation(BaseInvocation):
)
@title("Scale Image")
@tags("image", "scale")
@invocation("img_scale", title="Scale Image", tags=["image", "scale"], category="image")
class ImageScaleInvocation(BaseInvocation):
"""Scales an image by a factor"""
# Metadata
type: Literal["img_scale"] = "img_scale"
# Inputs
image: ImageField = InputField(description="The image to scale")
scale_factor: float = InputField(
default=2.0,
@ -461,15 +406,10 @@ class ImageScaleInvocation(BaseInvocation):
)
@title("Lerp Image")
@tags("image", "lerp")
@invocation("img_lerp", title="Lerp Image", tags=["image", "lerp"], category="image")
class ImageLerpInvocation(BaseInvocation):
"""Linear interpolation of all pixels of an image"""
# Metadata
type: Literal["img_lerp"] = "img_lerp"
# Inputs
image: ImageField = InputField(description="The image to lerp")
min: int = InputField(default=0, ge=0, le=255, description="The minimum output value")
max: int = InputField(default=255, ge=0, le=255, description="The maximum output value")
@ -499,15 +439,10 @@ class ImageLerpInvocation(BaseInvocation):
)
@title("Inverse Lerp Image")
@tags("image", "ilerp")
@invocation("img_ilerp", title="Inverse Lerp Image", tags=["image", "ilerp"], category="image")
class ImageInverseLerpInvocation(BaseInvocation):
"""Inverse linear interpolation of all pixels of an image"""
# Metadata
type: Literal["img_ilerp"] = "img_ilerp"
# Inputs
image: ImageField = InputField(description="The image to lerp")
min: int = InputField(default=0, ge=0, le=255, description="The minimum input value")
max: int = InputField(default=255, ge=0, le=255, description="The maximum input value")
@ -537,15 +472,10 @@ class ImageInverseLerpInvocation(BaseInvocation):
)
@title("Blur NSFW Image")
@tags("image", "nsfw")
@invocation("img_nsfw", title="Blur NSFW Image", tags=["image", "nsfw"], category="image")
class ImageNSFWBlurInvocation(BaseInvocation):
"""Add blur to NSFW-flagged images"""
# Metadata
type: Literal["img_nsfw"] = "img_nsfw"
# Inputs
image: ImageField = InputField(description="The image to check")
metadata: Optional[CoreMetadata] = InputField(
default=None, description=FieldDescriptions.core_metadata, ui_hidden=True
@ -587,15 +517,10 @@ class ImageNSFWBlurInvocation(BaseInvocation):
return caution.resize((caution.width // 2, caution.height // 2))
@title("Add Invisible Watermark")
@tags("image", "watermark")
@invocation("img_watermark", title="Add Invisible Watermark", tags=["image", "watermark"], category="image")
class ImageWatermarkInvocation(BaseInvocation):
"""Add an invisible watermark to an image"""
# Metadata
type: Literal["img_watermark"] = "img_watermark"
# Inputs
image: ImageField = InputField(description="The image to check")
text: str = InputField(default="InvokeAI", description="Watermark text")
metadata: Optional[CoreMetadata] = InputField(
@ -623,14 +548,10 @@ class ImageWatermarkInvocation(BaseInvocation):
)
@title("Mask Edge")
@tags("image", "mask", "inpaint")
@invocation("mask_edge", title="Mask Edge", tags=["image", "mask", "inpaint"], category="image")
class MaskEdgeInvocation(BaseInvocation):
"""Applies an edge mask to an image"""
type: Literal["mask_edge"] = "mask_edge"
# Inputs
image: ImageField = InputField(description="The image to apply the mask to")
edge_size: int = InputField(description="The size of the edge")
edge_blur: int = InputField(description="The amount of blur on the edge")
@ -672,14 +593,10 @@ class MaskEdgeInvocation(BaseInvocation):
)
@title("Combine Mask")
@tags("image", "mask", "multiply")
@invocation("mask_combine", title="Combine Masks", tags=["image", "mask", "multiply"], category="image")
class MaskCombineInvocation(BaseInvocation):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
type: Literal["mask_combine"] = "mask_combine"
# Inputs
mask1: ImageField = InputField(description="The first mask to combine")
mask2: ImageField = InputField(description="The second image to combine")
@ -706,17 +623,13 @@ class MaskCombineInvocation(BaseInvocation):
)
@title("Color Correct")
@tags("image", "color")
@invocation("color_correct", title="Color Correct", tags=["image", "color"], category="image")
class ColorCorrectInvocation(BaseInvocation):
"""
Shifts the colors of a target image to match the reference image, optionally
using a mask to only color-correct certain regions of the target image.
"""
type: Literal["color_correct"] = "color_correct"
# Inputs
image: ImageField = InputField(description="The image to color-correct")
reference: ImageField = InputField(description="Reference image for color-correction")
mask: Optional[ImageField] = InputField(default=None, description="Mask to use when applying color-correction")
@ -815,14 +728,10 @@ class ColorCorrectInvocation(BaseInvocation):
)
@title("Image Hue Adjustment")
@tags("image", "hue", "hsl")
@invocation("img_hue_adjust", title="Adjust Image Hue", tags=["image", "hue"], category="image")
class ImageHueAdjustmentInvocation(BaseInvocation):
"""Adjusts the Hue of an image."""
type: Literal["img_hue_adjust"] = "img_hue_adjust"
# Inputs
image: ImageField = InputField(description="The image to adjust")
hue: int = InputField(default=0, description="The degrees by which to rotate the hue, 0-360")
@ -860,14 +769,15 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
)
@title("Image Luminosity Adjustment")
@tags("image", "luminosity", "hsl")
@invocation(
"img_luminosity_adjust",
title="Adjust Image Luminosity",
tags=["image", "luminosity", "hsl"],
category="image",
)
class ImageLuminosityAdjustmentInvocation(BaseInvocation):
"""Adjusts the Luminosity (Value) of an image."""
type: Literal["img_luminosity_adjust"] = "img_luminosity_adjust"
# Inputs
image: ImageField = InputField(description="The image to adjust")
luminosity: float = InputField(
default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)"
@ -911,14 +821,15 @@ class ImageLuminosityAdjustmentInvocation(BaseInvocation):
)
@title("Image Saturation Adjustment")
@tags("image", "saturation", "hsl")
@invocation(
"img_saturation_adjust",
title="Adjust Image Saturation",
tags=["image", "saturation", "hsl"],
category="image",
)
class ImageSaturationAdjustmentInvocation(BaseInvocation):
"""Adjusts the Saturation of an image."""
type: Literal["img_saturation_adjust"] = "img_saturation_adjust"
# Inputs
image: ImageField = InputField(description="The image to adjust")
saturation: float = InputField(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation")

26
invokeai/app/invocations/infill.py
View File

@ -12,7 +12,7 @@ from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
def infill_methods() -> list[str]:
@ -116,14 +116,10 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
return si
@title("Solid Color Infill")
@tags("image", "inpaint")
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint")
class InfillColorInvocation(BaseInvocation):
"""Infills transparent areas of an image with a solid color"""
type: Literal["infill_rgba"] = "infill_rgba"
# Inputs
image: ImageField = InputField(description="The image to infill")
color: ColorField = InputField(
default=ColorField(r=127, g=127, b=127, a=255),
@ -155,14 +151,10 @@ class InfillColorInvocation(BaseInvocation):
)
@title("Tile Infill")
@tags("image", "inpaint")
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint")
class InfillTileInvocation(BaseInvocation):
"""Infills transparent areas of an image with tiles of the image"""
type: Literal["infill_tile"] = "infill_tile"
# Input
image: ImageField = InputField(description="The image to infill")
tile_size: int = InputField(default=32, ge=1, description="The tile size (px)")
seed: int = InputField(
@ -195,14 +187,10 @@ class InfillTileInvocation(BaseInvocation):
)
@title("PatchMatch Infill")
@tags("image", "inpaint")
@invocation("infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint")
class InfillPatchMatchInvocation(BaseInvocation):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
type: Literal["infill_patchmatch"] = "infill_patchmatch"
# Inputs
image: ImageField = InputField(description="The image to infill")
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -230,14 +218,10 @@ class InfillPatchMatchInvocation(BaseInvocation):
)
@title("LaMa Infill")
@tags("image", "inpaint")
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint")
class LaMaInfillInvocation(BaseInvocation):
"""Infills transparent areas of an image using the LaMa model"""
type: Literal["infill_lama"] = "infill_lama"
# Inputs
image: ImageField = InputField(description="The image to infill")
def invoke(self, context: InvocationContext) -> ImageOutput:

58
invokeai/app/invocations/latent.py
View File

@ -47,7 +47,15 @@ from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import Post
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import choose_precision, choose_torch_device
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, FieldDescriptions, Input, InputField, InvocationContext, UIType, tags, title
from .baseinvocation import (
BaseInvocation,
FieldDescriptions,
Input,
InputField,
InvocationContext,
UIType,
invocation,
)
from .compel import ConditioningField
from .controlnet_image_processors import ControlField
from .model import ModelInfo, UNetField, VaeField
@ -58,15 +66,10 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
@title("Create Denoise Mask")
@tags("mask", "denoise")
@invocation("create_denoise_mask", title="Create Denoise Mask", tags=["mask", "denoise"], category="latents")
class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
# Metadata
type: Literal["create_denoise_mask"] = "create_denoise_mask"
# Inputs
vae: VaeField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
@ -158,14 +161,15 @@ def get_scheduler(
return scheduler
@title("Denoise Latents")
@tags("latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l")
@invocation(
"denoise_latents",
title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents",
)
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
type: Literal["denoise_latents"] = "denoise_latents"
# Inputs
positive_conditioning: ConditioningField = InputField(
description=FieldDescriptions.positive_cond, input=Input.Connection, ui_order=0
)
@ -512,14 +516,10 @@ class DenoiseLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=result_latents, seed=seed)
@title("Latents to Image")
@tags("latents", "image", "vae", "l2i")
@invocation("l2i", title="Latents to Image", tags=["latents", "image", "vae", "l2i"], category="latents")
class LatentsToImageInvocation(BaseInvocation):
"""Generates an image from latents."""
type: Literal["l2i"] = "l2i"
# Inputs
latents: LatentsField = InputField(
description=FieldDescriptions.latents,
input=Input.Connection,
@ -613,14 +613,10 @@ class LatentsToImageInvocation(BaseInvocation):
LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
@title("Resize Latents")
@tags("latents", "resize")
@invocation("lresize", title="Resize Latents", tags=["latents", "resize"], category="latents")
class ResizeLatentsInvocation(BaseInvocation):
"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
type: Literal["lresize"] = "lresize"
# Inputs
latents: LatentsField = InputField(
description=FieldDescriptions.latents,
input=Input.Connection,
@ -661,14 +657,10 @@ class ResizeLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@title("Scale Latents")
@tags("latents", "resize")
@invocation("lscale", title="Scale Latents", tags=["latents", "resize"], category="latents")
class ScaleLatentsInvocation(BaseInvocation):
"""Scales latents by a given factor."""
type: Literal["lscale"] = "lscale"
# Inputs
latents: LatentsField = InputField(
description=FieldDescriptions.latents,
input=Input.Connection,
@ -701,14 +693,10 @@ class ScaleLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=resized_latents, seed=self.latents.seed)
@title("Image to Latents")
@tags("latents", "image", "vae", "i2l")
@invocation("i2l", title="Image to Latents", tags=["latents", "image", "vae", "i2l"], category="latents")
class ImageToLatentsInvocation(BaseInvocation):
"""Encodes an image into latents."""
type: Literal["i2l"] = "i2l"
# Inputs
image: ImageField = InputField(
description="The image to encode",
)
@ -785,14 +773,10 @@ class ImageToLatentsInvocation(BaseInvocation):
return build_latents_output(latents_name=name, latents=latents, seed=None)
@title("Blend Latents")
@tags("latents", "blend")
@invocation("lblend", title="Blend Latents", tags=["latents", "blend"], category="latents")
class BlendLatentsInvocation(BaseInvocation):
"""Blend two latents using a given alpha. Latents must have same size."""
type: Literal["lblend"] = "lblend"
# Inputs
latents_a: LatentsField = InputField(
description=FieldDescriptions.latents,
input=Input.Connection,

34
invokeai/app/invocations/math.py
View File

@ -1,22 +1,16 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from typing import Literal
import numpy as np
from invokeai.app.invocations.primitives import IntegerOutput
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, tags, title
from .baseinvocation import BaseInvocation, FieldDescriptions, InputField, InvocationContext, invocation
@title("Add Integers")
@tags("math")
@invocation("add", title="Add Integers", tags=["math", "add"], category="math")
class AddInvocation(BaseInvocation):
"""Adds two numbers"""
type: Literal["add"] = "add"
# Inputs
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
@ -24,14 +18,10 @@ class AddInvocation(BaseInvocation):
return IntegerOutput(value=self.a + self.b)
@title("Subtract Integers")
@tags("math")
@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math")
class SubtractInvocation(BaseInvocation):
"""Subtracts two numbers"""
type: Literal["sub"] = "sub"
# Inputs
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
@ -39,14 +29,10 @@ class SubtractInvocation(BaseInvocation):
return IntegerOutput(value=self.a - self.b)
@title("Multiply Integers")
@tags("math")
@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math")
class MultiplyInvocation(BaseInvocation):
"""Multiplies two numbers"""
type: Literal["mul"] = "mul"
# Inputs
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
@ -54,14 +40,10 @@ class MultiplyInvocation(BaseInvocation):
return IntegerOutput(value=self.a * self.b)
@title("Divide Integers")
@tags("math")
@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math")
class DivideInvocation(BaseInvocation):
"""Divides two numbers"""
type: Literal["div"] = "div"
# Inputs
a: int = InputField(default=0, description=FieldDescriptions.num_1)
b: int = InputField(default=0, description=FieldDescriptions.num_2)
@ -69,14 +51,10 @@ class DivideInvocation(BaseInvocation):
return IntegerOutput(value=int(self.a / self.b))
@title("Random Integer")
@tags("math")
@invocation("rand_int", title="Random Integer", tags=["math", "random"], category="math")
class RandomIntInvocation(BaseInvocation):
"""Outputs a single random integer."""
type: Literal["rand_int"] = "rand_int"
# Inputs
low: int = InputField(default=0, description="The inclusive low value")
high: int = InputField(default=np.iinfo(np.int32).max, description="The exclusive high value")

14
invokeai/app/invocations/metadata.py
View File

@ -1,4 +1,4 @@
from typing import Literal, Optional
from typing import Optional
from pydantic import Field
@ -8,8 +8,8 @@ from invokeai.app.invocations.baseinvocation import (
InputField,
InvocationContext,
OutputField,
tags,
title,
invocation,
invocation_output,
)
from invokeai.app.invocations.controlnet_image_processors import ControlField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
@ -91,21 +91,17 @@ class ImageMetadata(BaseModelExcludeNull):
graph: Optional[dict] = Field(default=None, description="The graph that created the image")
@invocation_output("metadata_accumulator_output")
class MetadataAccumulatorOutput(BaseInvocationOutput):
"""The output of the MetadataAccumulator node"""
type: Literal["metadata_accumulator_output"] = "metadata_accumulator_output"
metadata: CoreMetadata = OutputField(description="The core metadata for the image")
@title("Metadata Accumulator")
@tags("metadata")
@invocation("metadata_accumulator", title="Metadata Accumulator", tags=["metadata"], category="metadata")
class MetadataAccumulatorInvocation(BaseInvocation):
"""Outputs a Core Metadata Object"""
type: Literal["metadata_accumulator"] = "metadata_accumulator"
generation_mode: str = InputField(
description="The generation mode that output this image",
)

58
invokeai/app/invocations/model.py
View File

@ -1,5 +1,5 @@
import copy
from typing import List, Literal, Optional
from typing import List, Optional
from pydantic import BaseModel, Field
@ -13,8 +13,8 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIType,
tags,
title,
invocation,
invocation_output,
)
@ -49,11 +49,10 @@ class VaeField(BaseModel):
seamless_axes: List[str] = Field(default_factory=list, description='Axes("x" and "y") to which apply seamless')
@invocation_output("model_loader_output")
class ModelLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
type: Literal["model_loader_output"] = "model_loader_output"
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP")
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
@ -74,14 +73,10 @@ class LoRAModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model")
@title("Main Model")
@tags("model")
@invocation("main_model_loader", title="Main Model", tags=["model"], category="model")
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
type: Literal["main_model_loader"] = "main_model_loader"
# Inputs
model: MainModelField = InputField(description=FieldDescriptions.main_model, input=Input.Direct)
# TODO: precision?
@ -170,25 +165,18 @@ class MainModelLoaderInvocation(BaseInvocation):
)
@invocation_output("lora_loader_output")
class LoraLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
# fmt: off
type: Literal["lora_loader_output"] = "lora_loader_output"
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
# fmt: on
@title("LoRA")
@tags("lora", "model")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model")
class LoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
type: Literal["lora_loader"] = "lora_loader"
# Inputs
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = InputField(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = InputField(
@ -247,25 +235,19 @@ class LoraLoaderInvocation(BaseInvocation):
return output
@invocation_output("sdxl_lora_loader_output")
class SDXLLoraLoaderOutput(BaseInvocationOutput):
"""SDXL LoRA Loader Output"""
# fmt: off
type: Literal["sdxl_lora_loader_output"] = "sdxl_lora_loader_output"
unet: Optional[UNetField] = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
clip: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 1")
clip2: Optional[ClipField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP 2")
# fmt: on
@title("SDXL LoRA")
@tags("sdxl", "lora", "model")
@invocation("sdxl_lora_loader", title="SDXL LoRA", tags=["lora", "model"], category="model")
class SDXLLoraLoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
type: Literal["sdxl_lora_loader"] = "sdxl_lora_loader"
lora: LoRAModelField = InputField(description=FieldDescriptions.lora_model, input=Input.Direct, title="LoRA")
weight: float = Field(default=0.75, description=FieldDescriptions.lora_weight)
unet: Optional[UNetField] = Field(
@ -349,23 +331,17 @@ class VAEModelField(BaseModel):
base_model: BaseModelType = Field(description="Base model")
@invocation_output("vae_loader_output")
class VaeLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
"""VAE output"""
type: Literal["vae_loader_output"] = "vae_loader_output"
# Outputs
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
@title("VAE")
@tags("vae", "model")
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model")
class VaeLoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput"""
type: Literal["vae_loader"] = "vae_loader"
# Inputs
vae_model: VAEModelField = InputField(
description=FieldDescriptions.vae_model, input=Input.Direct, ui_type=UIType.VaeModel, title="VAE"
)
@ -392,24 +368,18 @@ class VaeLoaderInvocation(BaseInvocation):
)
@invocation_output("seamless_output")
class SeamlessModeOutput(BaseInvocationOutput):
"""Modified Seamless Model output"""
type: Literal["seamless_output"] = "seamless_output"
# Outputs
unet: Optional[UNetField] = OutputField(description=FieldDescriptions.unet, title="UNet")
vae: Optional[VaeField] = OutputField(description=FieldDescriptions.vae, title="VAE")
@title("Seamless")
@tags("seamless", "model")
@invocation("seamless", title="Seamless", tags=["seamless", "model"], category="model")
class SeamlessModeInvocation(BaseInvocation):
"""Applies the seamless transformation to the Model UNet and VAE."""
type: Literal["seamless"] = "seamless"
# Inputs
unet: Optional[UNetField] = InputField(
default=None, description=FieldDescriptions.unet, input=Input.Connection, title="UNet"
)

15
invokeai/app/invocations/noise.py
View File

@ -1,6 +1,5 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654) & the InvokeAI Team
from typing import Literal
import torch
from pydantic import validator
@ -16,8 +15,8 @@ from .baseinvocation import (
InputField,
InvocationContext,
OutputField,
tags,
title,
invocation,
invocation_output,
)
"""
@ -62,12 +61,10 @@ Nodes
"""
@invocation_output("noise_output")
class NoiseOutput(BaseInvocationOutput):
"""Invocation noise output"""
type: Literal["noise_output"] = "noise_output"
# Inputs
noise: LatentsField = OutputField(default=None, description=FieldDescriptions.noise)
width: int = OutputField(description=FieldDescriptions.width)
height: int = OutputField(description=FieldDescriptions.height)
@ -81,14 +78,10 @@ def build_noise_output(latents_name: str, latents: torch.Tensor, seed: int):
)
@title("Noise")
@tags("latents", "noise")
@invocation("noise", title="Noise", tags=["latents", "noise"], category="latents")
class NoiseInvocation(BaseInvocation):
"""Generates latent noise."""
type: Literal["noise"] = "noise"
# Inputs
seed: int = InputField(
ge=0,
le=SEED_MAX,

42
invokeai/app/invocations/onnx.py
View File

@ -31,8 +31,8 @@ from .baseinvocation import (
OutputField,
UIComponent,
UIType,
tags,
title,
invocation,
invocation_output,
)
from .controlnet_image_processors import ControlField
from .latent import SAMPLER_NAME_VALUES, LatentsField, LatentsOutput, build_latents_output, get_scheduler
@ -56,11 +56,8 @@ ORT_TO_NP_TYPE = {
PRECISION_VALUES = Literal[tuple(list(ORT_TO_NP_TYPE.keys()))]
@title("ONNX Prompt (Raw)")
@tags("onnx", "prompt")
@invocation("prompt_onnx", title="ONNX Prompt (Raw)", tags=["prompt", "onnx"], category="conditioning")
class ONNXPromptInvocation(BaseInvocation):
type: Literal["prompt_onnx"] = "prompt_onnx"
prompt: str = InputField(default="", description=FieldDescriptions.raw_prompt, ui_component=UIComponent.Textarea)
clip: ClipField = InputField(description=FieldDescriptions.clip, input=Input.Connection)
@ -141,14 +138,15 @@ class ONNXPromptInvocation(BaseInvocation):
# Text to image
@title("ONNX Text to Latents")
@tags("latents", "inference", "txt2img", "onnx")
@invocation(
"t2l_onnx",
title="ONNX Text to Latents",
tags=["latents", "inference", "txt2img", "onnx"],
category="latents",
)
class ONNXTextToLatentsInvocation(BaseInvocation):
"""Generates latents from conditionings."""
type: Literal["t2l_onnx"] = "t2l_onnx"
# Inputs
positive_conditioning: ConditioningField = InputField(
description=FieldDescriptions.positive_cond,
input=Input.Connection,
@ -316,14 +314,15 @@ class ONNXTextToLatentsInvocation(BaseInvocation):
# Latent to image
@title("ONNX Latents to Image")
@tags("latents", "image", "vae", "onnx")
@invocation(
"l2i_onnx",
title="ONNX Latents to Image",
tags=["latents", "image", "vae", "onnx"],
category="image",
)
class ONNXLatentsToImageInvocation(BaseInvocation):
"""Generates an image from latents."""
type: Literal["l2i_onnx"] = "l2i_onnx"
# Inputs
latents: LatentsField = InputField(
description=FieldDescriptions.denoised_latents,
input=Input.Connection,
@ -386,17 +385,14 @@ class ONNXLatentsToImageInvocation(BaseInvocation):
)
@invocation_output("model_loader_output_onnx")
class ONNXModelLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
# fmt: off
type: Literal["model_loader_output_onnx"] = "model_loader_output_onnx"
unet: UNetField = OutputField(default=None, description=FieldDescriptions.unet, title="UNet")
clip: ClipField = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
vae_decoder: VaeField = OutputField(default=None, description=FieldDescriptions.vae, title="VAE Decoder")
vae_encoder: VaeField = OutputField(default=None, description=FieldDescriptions.vae, title="VAE Encoder")
# fmt: on
class OnnxModelField(BaseModel):
@ -407,14 +403,10 @@ class OnnxModelField(BaseModel):
model_type: ModelType = Field(description="Model Type")
@title("ONNX Main Model")
@tags("onnx", "model")
@invocation("onnx_model_loader", title="ONNX Main Model", tags=["onnx", "model"], category="model")
class OnnxModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
type: Literal["onnx_model_loader"] = "onnx_model_loader"
# Inputs
model: OnnxModelField = InputField(
description=FieldDescriptions.onnx_main_model, input=Input.Direct, ui_type=UIType.ONNXModel
)

14
invokeai/app/invocations/param_easing.py
View File

@ -42,17 +42,13 @@ from matplotlib.ticker import MaxNLocator
from invokeai.app.invocations.primitives import FloatCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
@title("Float Range")
@tags("math", "range")
@invocation("float_range", title="Float Range", tags=["math", "range"], category="math")
class FloatLinearRangeInvocation(BaseInvocation):
"""Creates a range"""
type: Literal["float_range"] = "float_range"
# Inputs
start: float = InputField(default=5, description="The first value of the range")
stop: float = InputField(default=10, description="The last value of the range")
steps: int = InputField(default=30, description="number of values to interpolate over (including start and stop)")
@ -100,14 +96,10 @@ EASING_FUNCTION_KEYS = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
# actually I think for now could just use CollectionOutput (which is list[Any]
@title("Step Param Easing")
@tags("step", "easing")
@invocation("step_param_easing", title="Step Param Easing", tags=["step", "easing"], category="step")
class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps"""
type: Literal["step_param_easing"] = "step_param_easing"
# Inputs
easing: EASING_FUNCTION_KEYS = InputField(default="Linear", description="The easing function to use")
num_steps: int = InputField(default=20, description="number of denoising steps")
start_value: float = InputField(default=0.0, description="easing starting value")

187
invokeai/app/invocations/primitives.py
View File

@ -1,6 +1,6 @@
# Copyright (c) 2023 Kyle Schouviller (https://github.com/kyle0654)
from typing import Literal, Optional, Tuple
from typing import Optional, Tuple
import torch
from pydantic import BaseModel, Field
@ -15,8 +15,8 @@ from .baseinvocation import (
OutputField,
UIComponent,
UIType,
tags,
title,
invocation,
invocation_output,
)
"""
@ -29,44 +29,39 @@ Primitives: Boolean, Integer, Float, String, Image, Latents, Conditioning, Color
# region Boolean
@invocation_output("boolean_output")
class BooleanOutput(BaseInvocationOutput):
"""Base class for nodes that output a single boolean"""
type: Literal["boolean_output"] = "boolean_output"
value: bool = OutputField(description="The output boolean")
@invocation_output("boolean_collection_output")
class BooleanCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of booleans"""
type: Literal["boolean_collection_output"] = "boolean_collection_output"
# Outputs
collection: list[bool] = OutputField(description="The output boolean collection", ui_type=UIType.BooleanCollection)
@title("Boolean Primitive")
@tags("primitives", "boolean")
@invocation("boolean", title="Boolean Primitive", tags=["primitives", "boolean"], category="primitives")
class BooleanInvocation(BaseInvocation):
"""A boolean primitive value"""
type: Literal["boolean"] = "boolean"
# Inputs
value: bool = InputField(default=False, description="The boolean value")
def invoke(self, context: InvocationContext) -> BooleanOutput:
return BooleanOutput(value=self.value)
@title("Boolean Primitive Collection")
@tags("primitives", "boolean", "collection")
@invocation(
"boolean_collection",
title="Boolean Collection Primitive",
tags=["primitives", "boolean", "collection"],
category="primitives",
)
class BooleanCollectionInvocation(BaseInvocation):
"""A collection of boolean primitive values"""
type: Literal["boolean_collection"] = "boolean_collection"
# Inputs
collection: list[bool] = InputField(
default_factory=list, description="The collection of boolean values", ui_type=UIType.BooleanCollection
)
@ -80,44 +75,39 @@ class BooleanCollectionInvocation(BaseInvocation):
# region Integer
@invocation_output("integer_output")
class IntegerOutput(BaseInvocationOutput):
"""Base class for nodes that output a single integer"""
type: Literal["integer_output"] = "integer_output"
value: int = OutputField(description="The output integer")
@invocation_output("integer_collection_output")
class IntegerCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of integers"""
type: Literal["integer_collection_output"] = "integer_collection_output"
# Outputs
collection: list[int] = OutputField(description="The int collection", ui_type=UIType.IntegerCollection)
@title("Integer Primitive")
@tags("primitives", "integer")
@invocation("integer", title="Integer Primitive", tags=["primitives", "integer"], category="primitives")
class IntegerInvocation(BaseInvocation):
"""An integer primitive value"""
type: Literal["integer"] = "integer"
# Inputs
value: int = InputField(default=0, description="The integer value")
def invoke(self, context: InvocationContext) -> IntegerOutput:
return IntegerOutput(value=self.value)
@title("Integer Primitive Collection")
@tags("primitives", "integer", "collection")
@invocation(
"integer_collection",
title="Integer Collection Primitive",
tags=["primitives", "integer", "collection"],
category="primitives",
)
class IntegerCollectionInvocation(BaseInvocation):
"""A collection of integer primitive values"""
type: Literal["integer_collection"] = "integer_collection"
# Inputs
collection: list[int] = InputField(
default=0, description="The collection of integer values", ui_type=UIType.IntegerCollection
)
@ -131,44 +121,39 @@ class IntegerCollectionInvocation(BaseInvocation):
# region Float
@invocation_output("float_output")
class FloatOutput(BaseInvocationOutput):
"""Base class for nodes that output a single float"""
type: Literal["float_output"] = "float_output"
value: float = OutputField(description="The output float")
@invocation_output("float_collection_output")
class FloatCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of floats"""
type: Literal["float_collection_output"] = "float_collection_output"
# Outputs
collection: list[float] = OutputField(description="The float collection", ui_type=UIType.FloatCollection)
@title("Float Primitive")
@tags("primitives", "float")
@invocation("float", title="Float Primitive", tags=["primitives", "float"], category="primitives")
class FloatInvocation(BaseInvocation):
"""A float primitive value"""
type: Literal["float"] = "float"
# Inputs
value: float = InputField(default=0.0, description="The float value")
def invoke(self, context: InvocationContext) -> FloatOutput:
return FloatOutput(value=self.value)
@title("Float Primitive Collection")
@tags("primitives", "float", "collection")
@invocation(
"float_collection",
title="Float Collection Primitive",
tags=["primitives", "float", "collection"],
category="primitives",
)
class FloatCollectionInvocation(BaseInvocation):
"""A collection of float primitive values"""
type: Literal["float_collection"] = "float_collection"
# Inputs
collection: list[float] = InputField(
default_factory=list, description="The collection of float values", ui_type=UIType.FloatCollection
)
@ -182,44 +167,39 @@ class FloatCollectionInvocation(BaseInvocation):
# region String
@invocation_output("string_output")
class StringOutput(BaseInvocationOutput):
"""Base class for nodes that output a single string"""
type: Literal["string_output"] = "string_output"
value: str = OutputField(description="The output string")
@invocation_output("string_collection_output")
class StringCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of strings"""
type: Literal["string_collection_output"] = "string_collection_output"
# Outputs
collection: list[str] = OutputField(description="The output strings", ui_type=UIType.StringCollection)
@title("String Primitive")
@tags("primitives", "string")
@invocation("string", title="String Primitive", tags=["primitives", "string"], category="primitives")
class StringInvocation(BaseInvocation):
"""A string primitive value"""
type: Literal["string"] = "string"
# Inputs
value: str = InputField(default="", description="The string value", ui_component=UIComponent.Textarea)
def invoke(self, context: InvocationContext) -> StringOutput:
return StringOutput(value=self.value)
@title("String Primitive Collection")
@tags("primitives", "string", "collection")
@invocation(
"string_collection",
title="String Collection Primitive",
tags=["primitives", "string", "collection"],
category="primitives",
)
class StringCollectionInvocation(BaseInvocation):
"""A collection of string primitive values"""
type: Literal["string_collection"] = "string_collection"
# Inputs
collection: list[str] = InputField(
default_factory=list, description="The collection of string values", ui_type=UIType.StringCollection
)
@ -239,33 +219,26 @@ class ImageField(BaseModel):
image_name: str = Field(description="The name of the image")
@invocation_output("image_output")
class ImageOutput(BaseInvocationOutput):
"""Base class for nodes that output a single image"""
type: Literal["image_output"] = "image_output"
image: ImageField = OutputField(description="The output image")
width: int = OutputField(description="The width of the image in pixels")
height: int = OutputField(description="The height of the image in pixels")
@invocation_output("image_collection_output")
class ImageCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of images"""
type: Literal["image_collection_output"] = "image_collection_output"
# Outputs
collection: list[ImageField] = OutputField(description="The output images", ui_type=UIType.ImageCollection)
@title("Image Primitive")
@tags("primitives", "image")
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives")
class ImageInvocation(BaseInvocation):
"""An image primitive value"""
# Metadata
type: Literal["image"] = "image"
# Inputs
image: ImageField = InputField(description="The image to load")
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -278,14 +251,15 @@ class ImageInvocation(BaseInvocation):
)
@title("Image Primitive Collection")
@tags("primitives", "image", "collection")
@invocation(
"image_collection",
title="Image Collection Primitive",
tags=["primitives", "image", "collection"],
category="primitives",
)
class ImageCollectionInvocation(BaseInvocation):
"""A collection of image primitive values"""
type: Literal["image_collection"] = "image_collection"
# Inputs
collection: list[ImageField] = InputField(
default=0, description="The collection of image values", ui_type=UIType.ImageCollection
)
@ -306,10 +280,10 @@ class DenoiseMaskField(BaseModel):
masked_latents_name: Optional[str] = Field(description="The name of the masked image latents")
@invocation_output("denoise_mask_output")
class DenoiseMaskOutput(BaseInvocationOutput):
"""Base class for nodes that output a single image"""
type: Literal["denoise_mask_output"] = "denoise_mask_output"
denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
@ -325,11 +299,10 @@ class LatentsField(BaseModel):
seed: Optional[int] = Field(default=None, description="Seed used to generate this latents")
@invocation_output("latents_output")
class LatentsOutput(BaseInvocationOutput):
"""Base class for nodes that output a single latents tensor"""
type: Literal["latents_output"] = "latents_output"
latents: LatentsField = OutputField(
description=FieldDescriptions.latents,
)
@ -337,25 +310,20 @@ class LatentsOutput(BaseInvocationOutput):
height: int = OutputField(description=FieldDescriptions.height)
@invocation_output("latents_collection_output")
class LatentsCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of latents tensors"""
type: Literal["latents_collection_output"] = "latents_collection_output"
collection: list[LatentsField] = OutputField(
description=FieldDescriptions.latents,
ui_type=UIType.LatentsCollection,
)
@title("Latents Primitive")
@tags("primitives", "latents")
@invocation("latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives")
class LatentsInvocation(BaseInvocation):
"""A latents tensor primitive value"""
type: Literal["latents"] = "latents"
# Inputs
latents: LatentsField = InputField(description="The latents tensor", input=Input.Connection)
def invoke(self, context: InvocationContext) -> LatentsOutput:
@ -364,14 +332,15 @@ class LatentsInvocation(BaseInvocation):
return build_latents_output(self.latents.latents_name, latents)
@title("Latents Primitive Collection")
@tags("primitives", "latents", "collection")
@invocation(
"latents_collection",
title="Latents Collection Primitive",
tags=["primitives", "latents", "collection"],
category="primitives",
)
class LatentsCollectionInvocation(BaseInvocation):
"""A collection of latents tensor primitive values"""
type: Literal["latents_collection"] = "latents_collection"
# Inputs
collection: list[LatentsField] = InputField(
description="The collection of latents tensors", ui_type=UIType.LatentsCollection
)
@ -405,30 +374,24 @@ class ColorField(BaseModel):
return (self.r, self.g, self.b, self.a)
@invocation_output("color_output")
class ColorOutput(BaseInvocationOutput):
"""Base class for nodes that output a single color"""
type: Literal["color_output"] = "color_output"
color: ColorField = OutputField(description="The output color")
@invocation_output("color_collection_output")
class ColorCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of colors"""
type: Literal["color_collection_output"] = "color_collection_output"
# Outputs
collection: list[ColorField] = OutputField(description="The output colors", ui_type=UIType.ColorCollection)
@title("Color Primitive")
@tags("primitives", "color")
@invocation("color", title="Color Primitive", tags=["primitives", "color"], category="primitives")
class ColorInvocation(BaseInvocation):
"""A color primitive value"""
type: Literal["color"] = "color"
# Inputs
color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=255), description="The color value")
def invoke(self, context: InvocationContext) -> ColorOutput:
@ -446,47 +409,47 @@ class ConditioningField(BaseModel):
conditioning_name: str = Field(description="The name of conditioning tensor")
@invocation_output("conditioning_output")
class ConditioningOutput(BaseInvocationOutput):
"""Base class for nodes that output a single conditioning tensor"""
type: Literal["conditioning_output"] = "conditioning_output"
conditioning: ConditioningField = OutputField(description=FieldDescriptions.cond)
@invocation_output("conditioning_collection_output")
class ConditioningCollectionOutput(BaseInvocationOutput):
"""Base class for nodes that output a collection of conditioning tensors"""
type: Literal["conditioning_collection_output"] = "conditioning_collection_output"
# Outputs
collection: list[ConditioningField] = OutputField(
description="The output conditioning tensors",
ui_type=UIType.ConditioningCollection,
)
@title("Conditioning Primitive")
@tags("primitives", "conditioning")
@invocation(
"conditioning",
title="Conditioning Primitive",
tags=["primitives", "conditioning"],
category="primitives",
)
class ConditioningInvocation(BaseInvocation):
"""A conditioning tensor primitive value"""
type: Literal["conditioning"] = "conditioning"
conditioning: ConditioningField = InputField(description=FieldDescriptions.cond, input=Input.Connection)
def invoke(self, context: InvocationContext) -> ConditioningOutput:
return ConditioningOutput(conditioning=self.conditioning)
@title("Conditioning Primitive Collection")
@tags("primitives", "conditioning", "collection")
@invocation(
"conditioning_collection",
title="Conditioning Collection Primitive",
tags=["primitives", "conditioning", "collection"],
category="primitives",
)
class ConditioningCollectionInvocation(BaseInvocation):
"""A collection of conditioning tensor primitive values"""
type: Literal["conditioning_collection"] = "conditioning_collection"
# Inputs
collection: list[ConditioningField] = InputField(
default=0, description="The collection of conditioning tensors", ui_type=UIType.ConditioningCollection
)

16
invokeai/app/invocations/prompt.py
View File

@ -1,5 +1,5 @@
from os.path import exists
from typing import Literal, Optional, Union
from typing import Optional, Union
import numpy as np
from dynamicprompts.generators import CombinatorialPromptGenerator, RandomPromptGenerator
@ -7,17 +7,13 @@ from pydantic import validator
from invokeai.app.invocations.primitives import StringCollectionOutput
from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, tags, title
from .baseinvocation import BaseInvocation, InputField, InvocationContext, UIComponent, invocation
@title("Dynamic Prompt")
@tags("prompt", "collection")
@invocation("dynamic_prompt", title="Dynamic Prompt", tags=["prompt", "collection"], category="prompt")
class DynamicPromptInvocation(BaseInvocation):
"""Parses a prompt using adieyal/dynamicprompts' random or combinatorial generator"""
type: Literal["dynamic_prompt"] = "dynamic_prompt"
# Inputs
prompt: str = InputField(description="The prompt to parse with dynamicprompts", ui_component=UIComponent.Textarea)
max_prompts: int = InputField(default=1, description="The number of prompts to generate")
combinatorial: bool = InputField(default=False, description="Whether to use the combinatorial generator")
@ -33,14 +29,10 @@ class DynamicPromptInvocation(BaseInvocation):
return StringCollectionOutput(collection=prompts)
@title("Prompts from File")
@tags("prompt", "file")
@invocation("prompt_from_file", title="Prompts from File", tags=["prompt", "file"], category="prompt")
class PromptsFromFileInvocation(BaseInvocation):
"""Loads prompts from a text file"""
type: Literal["prompt_from_file"] = "prompt_from_file"
# Inputs
file_path: str = InputField(description="Path to prompt text file")
pre_prompt: Optional[str] = InputField(
default=None, description="String to prepend to each prompt", ui_component=UIComponent.Textarea

29
invokeai/app/invocations/sdxl.py
View File

@ -1,5 +1,3 @@
from typing import Literal
from ...backend.model_management import ModelType, SubModelType
from .baseinvocation import (
BaseInvocation,
@ -10,41 +8,35 @@ from .baseinvocation import (
InvocationContext,
OutputField,
UIType,
tags,
title,
invocation,
invocation_output,
)
from .model import ClipField, MainModelField, ModelInfo, UNetField, VaeField
@invocation_output("sdxl_model_loader_output")
class SDXLModelLoaderOutput(BaseInvocationOutput):
"""SDXL base model loader output"""
type: Literal["sdxl_model_loader_output"] = "sdxl_model_loader_output"
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
clip: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 1")
clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation_output("sdxl_refiner_model_loader_output")
class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
"""SDXL refiner model loader output"""
type: Literal["sdxl_refiner_model_loader_output"] = "sdxl_refiner_model_loader_output"
unet: UNetField = OutputField(description=FieldDescriptions.unet, title="UNet")
clip2: ClipField = OutputField(description=FieldDescriptions.clip, title="CLIP 2")
vae: VaeField = OutputField(description=FieldDescriptions.vae, title="VAE")
@title("SDXL Main Model")
@tags("model", "sdxl")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model")
class SDXLModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl base model, outputting its submodels."""
type: Literal["sdxl_model_loader"] = "sdxl_model_loader"
# Inputs
model: MainModelField = InputField(
description=FieldDescriptions.sdxl_main_model, input=Input.Direct, ui_type=UIType.SDXLMainModel
)
@ -122,14 +114,15 @@ class SDXLModelLoaderInvocation(BaseInvocation):
)
@title("SDXL Refiner Model")
@tags("model", "sdxl", "refiner")
@invocation(
"sdxl_refiner_model_loader",
title="SDXL Refiner Model",
tags=["model", "sdxl", "refiner"],
category="model",
)
class SDXLRefinerModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl refiner model, outputting its submodels."""
type: Literal["sdxl_refiner_model_loader"] = "sdxl_refiner_model_loader"
# Inputs
model: MainModelField = InputField(
description=FieldDescriptions.sdxl_refiner_model,
input=Input.Direct,

8
invokeai/app/invocations/upscale.py
View File

@ -11,7 +11,7 @@ from invokeai.app.invocations.primitives import ImageField, ImageOutput
from invokeai.app.models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, title, tags
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
# TODO: Populate this from disk?
# TODO: Use model manager to load?
@ -23,14 +23,10 @@ ESRGAN_MODELS = Literal[
]
@title("Upscale (RealESRGAN)")
@tags("esrgan", "upscale")
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan")
class ESRGANInvocation(BaseInvocation):
"""Upscales an image using RealESRGAN."""
type: Literal["esrgan"] = "esrgan"
# Inputs
image: ImageField = InputField(description="The input image")
model_name: ESRGAN_MODELS = InputField(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")

30
invokeai/app/services/graph.py
View File

@ -3,7 +3,7 @@
import copy
import itertools
import uuid
from typing import Annotated, Any, Literal, Optional, Union, get_args, get_origin, get_type_hints
from typing import Annotated, Any, Optional, Union, get_args, get_origin, get_type_hints
import networkx as nx
from pydantic import BaseModel, root_validator, validator
@ -14,11 +14,13 @@ from ..invocations import * # noqa: F401 F403
from ..invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
invocation,
Input,
InputField,
InvocationContext,
OutputField,
UIType,
invocation_output,
)
# in 3.10 this would be "from types import NoneType"
@ -148,24 +150,16 @@ class NodeAlreadyExecutedError(Exception):
# TODO: Create and use an Empty output?
@invocation_output("graph_output")
class GraphInvocationOutput(BaseInvocationOutput):
type: Literal["graph_output"] = "graph_output"
class Config:
schema_extra = {
"required": [
"type",
"image",
]
}
pass
# TODO: Fill this out and move to invocations
@invocation("graph")
class GraphInvocation(BaseInvocation):
"""Execute a graph"""
type: Literal["graph"] = "graph"
# TODO: figure out how to create a default here
graph: "Graph" = Field(description="The graph to run", default=None)
@ -174,22 +168,20 @@ class GraphInvocation(BaseInvocation):
return GraphInvocationOutput()
@invocation_output("iterate_output")
class IterateInvocationOutput(BaseInvocationOutput):
"""Used to connect iteration outputs. Will be expanded to a specific output."""
type: Literal["iterate_output"] = "iterate_output"
item: Any = OutputField(
description="The item being iterated over", title="Collection Item", ui_type=UIType.CollectionItem
)
# TODO: Fill this out and move to invocations
@invocation("iterate")
class IterateInvocation(BaseInvocation):
"""Iterates over a list of items"""
type: Literal["iterate"] = "iterate"
collection: list[Any] = InputField(
description="The list of items to iterate over", default_factory=list, ui_type=UIType.Collection
)
@ -200,19 +192,17 @@ class IterateInvocation(BaseInvocation):
return IterateInvocationOutput(item=self.collection[self.index])
@invocation_output("collect_output")
class CollectInvocationOutput(BaseInvocationOutput):
type: Literal["collect_output"] = "collect_output"
collection: list[Any] = OutputField(
description="The collection of input items", title="Collection", ui_type=UIType.Collection
)
@invocation("collect")
class CollectInvocation(BaseInvocation):
"""Collects values into a collection"""
type: Literal["collect"] = "collect"
item: Any = InputField(
description="The item to collect (all inputs must be of the same type)",
ui_type=UIType.CollectionItem,

1
invokeai/frontend/web/src/features/nodes/types/types.ts
View File

@ -678,6 +678,7 @@ export type TypeHints = {
export type InvocationSchemaExtra = {
output: OpenAPIV3.ReferenceObject; // the output of the invocation
title: string;
category?: string;
tags?: string[];
properties: Omit<
NonNullable<OpenAPIV3.SchemaObject['properties']> &

1914
invokeai/frontend/web/src/services/api/schema.d.ts vendored
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File diff suppressed because one or more lines are too long

38
tests/nodes/test_nodes.py
View File

@ -1,65 +1,63 @@
from typing import Any, Callable, Literal, Union
from typing import Any, Callable, Union
from pydantic import Field
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationContext
from invokeai.app.invocations.baseinvocation import (
BaseInvocation,
BaseInvocationOutput,
InvocationContext,
invocation,
invocation_output,
)
from invokeai.app.invocations.image import ImageField
# Define test invocations before importing anything that uses invocations
@invocation_output("test_list_output")
class ListPassThroughInvocationOutput(BaseInvocationOutput):
type: Literal["test_list_output"] = "test_list_output"
collection: list[ImageField] = Field(default_factory=list)
@invocation("test_list")
class ListPassThroughInvocation(BaseInvocation):
type: Literal["test_list"] = "test_list"
collection: list[ImageField] = Field(default_factory=list)
def invoke(self, context: InvocationContext) -> ListPassThroughInvocationOutput:
return ListPassThroughInvocationOutput(collection=self.collection)
@invocation_output("test_prompt_output")
class PromptTestInvocationOutput(BaseInvocationOutput):
type: Literal["test_prompt_output"] = "test_prompt_output"
prompt: str = Field(default="")
@invocation("test_prompt")
class PromptTestInvocation(BaseInvocation):
type: Literal["test_prompt"] = "test_prompt"
prompt: str = Field(default="")
def invoke(self, context: InvocationContext) -> PromptTestInvocationOutput:
return PromptTestInvocationOutput(prompt=self.prompt)
@invocation("test_error")
class ErrorInvocation(BaseInvocation):
type: Literal["test_error"] = "test_error"
def invoke(self, context: InvocationContext) -> PromptTestInvocationOutput:
raise Exception("This invocation is supposed to fail")
@invocation_output("test_image_output")
class ImageTestInvocationOutput(BaseInvocationOutput):
type: Literal["test_image_output"] = "test_image_output"
image: ImageField = Field()
@invocation("test_text_to_image")
class TextToImageTestInvocation(BaseInvocation):
type: Literal["test_text_to_image"] = "test_text_to_image"
prompt: str = Field(default="")
def invoke(self, context: InvocationContext) -> ImageTestInvocationOutput:
return ImageTestInvocationOutput(image=ImageField(image_name=self.id))
@invocation("test_image_to_image")
class ImageToImageTestInvocation(BaseInvocation):
type: Literal["test_image_to_image"] = "test_image_to_image"
prompt: str = Field(default="")
image: Union[ImageField, None] = Field(default=None)
@ -67,13 +65,13 @@ class ImageToImageTestInvocation(BaseInvocation):
return ImageTestInvocationOutput(image=ImageField(image_name=self.id))
@invocation_output("test_prompt_collection_output")
class PromptCollectionTestInvocationOutput(BaseInvocationOutput):
type: Literal["test_prompt_collection_output"] = "test_prompt_collection_output"
collection: list[str] = Field(default_factory=list)
@invocation("test_prompt_collection")
class PromptCollectionTestInvocation(BaseInvocation):
type: Literal["test_prompt_collection"] = "test_prompt_collection"
collection: list[str] = Field()
def invoke(self, context: InvocationContext) -> PromptCollectionTestInvocationOutput: