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docs: update INVOCATIONS.md
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@ -9,11 +9,15 @@ complex functionality.
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## Invocations Directory
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InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These can be used as examples to create your own nodes.
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InvokeAI Nodes can be found in the `invokeai/app/invocations` directory. These
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can be used as examples to create your own nodes.
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New nodes should be added to a subfolder in `nodes` direction found at the root level of the InvokeAI installation location. Nodes added to this folder will be able to be used upon application startup.
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New nodes should be added to a subfolder in `nodes` direction found at the root
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level of the InvokeAI installation location. Nodes added to this folder will be
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able to be used upon application startup.
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Example `nodes` subfolder structure:
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Example `nodes` subfolder structure:
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```py
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├── __init__.py # Invoke-managed custom node loader
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│
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@ -30,14 +34,14 @@ Example `nodes` subfolder structure:
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└── fancy_node.py
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```
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Each node folder must have an `__init__.py` file that imports its nodes. Only nodes imported in the `__init__.py` file are loaded.
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See the README in the nodes folder for more examples:
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Each node folder must have an `__init__.py` file that imports its nodes. Only
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nodes imported in the `__init__.py` file are loaded. See the README in the nodes
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folder for more examples:
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```py
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from .cool_node import CoolInvocation
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```
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## Creating A New Invocation
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In order to understand the process of creating a new Invocation, let us actually
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@ -131,7 +135,6 @@ from invokeai.app.invocations.primitives import ImageField
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class ResizeInvocation(BaseInvocation):
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'''Resizes an image'''
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# Inputs
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image: ImageField = InputField(description="The input image")
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width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
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height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
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@ -167,12 +170,11 @@ from invokeai.app.invocations.primitives import ImageField
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class ResizeInvocation(BaseInvocation):
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'''Resizes an image'''
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# Inputs
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image: ImageField = InputField(description="The input image")
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width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
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height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
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def invoke(self, context: InvocationContext):
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def invoke(self, context):
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pass
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```
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@ -197,12 +199,11 @@ from invokeai.app.invocations.image import ImageOutput
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class ResizeInvocation(BaseInvocation):
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'''Resizes an image'''
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# Inputs
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image: ImageField = InputField(description="The input image")
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width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
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height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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def invoke(self, context) -> ImageOutput:
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pass
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```
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@ -228,31 +229,18 @@ class ResizeInvocation(BaseInvocation):
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width: int = InputField(default=512, ge=64, le=2048, description="Width of the new image")
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height: int = InputField(default=512, ge=64, le=2048, description="Height of the new image")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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# Load the image using InvokeAI's predefined Image Service. Returns the PIL image.
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image = context.services.images.get_pil_image(self.image.image_name)
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def invoke(self, context) -> ImageOutput:
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# Load the input image as a PIL image
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image = context.images.get_pil(self.image.image_name)
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# Resizing the image
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# Resize the image
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resized_image = image.resize((self.width, self.height))
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# Save the image using InvokeAI's predefined Image Service. Returns the prepared PIL image.
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output_image = context.services.images.create(
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image=resized_image,
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image_origin=ResourceOrigin.INTERNAL,
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image_category=ImageCategory.GENERAL,
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node_id=self.id,
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session_id=context.graph_execution_state_id,
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is_intermediate=self.is_intermediate,
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)
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# Save the image
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image_dto = context.images.save(image=resized_image)
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# Returning the Image
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return ImageOutput(
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image=ImageField(
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image_name=output_image.image_name,
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),
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width=output_image.width,
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height=output_image.height,
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)
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# Return an ImageOutput
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return ImageOutput.build(image_dto)
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```
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**Note:** Do not be overwhelmed by the `ImageOutput` process. InvokeAI has a
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@ -343,27 +331,25 @@ class ImageColorStringOutput(BaseInvocationOutput):
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That's all there is to it.
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<!-- 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.
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### Custom Input Fields
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Now that you know how to create your own Invocations, let us dive into slightly
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more advanced topics.
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While creating your own Invocations, you might run into a scenario where the
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existing input types in InvokeAI do not meet your requirements. In such cases,
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you can create your own input types.
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existing fields in InvokeAI do not meet your requirements. In such cases, you
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can create your own fields.
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Let us create one as an example. Let us say we want to create a color input
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field that represents a color code. But before we start on that here are some
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general good practices to keep in mind.
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**Good Practices**
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### Best Practices
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- There is no naming convention for input fields but we highly recommend that
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you name it something appropriate like `ColorField`.
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- It is not mandatory but it is heavily recommended to add a relevant
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`docstring` to describe your input field.
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`docstring` to describe your field.
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- Keep your field in the same file as the Invocation that it is made for or in
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another file where it is relevant.
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@ -378,10 +364,13 @@ class ColorField(BaseModel):
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pass
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```
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Perfect. Now let us create our custom inputs for our field. This is exactly
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similar how you created input fields for your Invocation. All the same rules
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apply. Let us create four fields representing the _red(r)_, _blue(b)_,
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_green(g)_ and _alpha(a)_ channel of the color.
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Perfect. Now let us create the properties for our field. This is similar to how
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you created input fields for your Invocation. All the same rules apply. Let us
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create four fields representing the _red(r)_, _blue(b)_, _green(g)_ and
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_alpha(a)_ channel of the color.
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> Technically, the properties are _also_ called fields - but in this case, it
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> refers to a `pydantic` field.
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```python
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class ColorField(BaseModel):
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@ -396,25 +385,11 @@ That's it. We now have a new input field type that we can use in our Invocations
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like this.
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```python
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color: ColorField = Field(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
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color: ColorField = InputField(default=ColorField(r=0, g=0, b=0, a=0), description='Background color of an image')
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```
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### Custom Components For Frontend
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### Using the custom field
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Every backend input type should have a corresponding frontend component so the
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UI knows what to render when you use a particular field type.
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When you start the UI, your custom field will be automatically recognized.
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If you are using existing field types, we already have components for those. So
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you don't have to worry about creating anything new. But this might not always
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be the case. Sometimes you might want to create new field types and have the
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frontend UI deal with it in a different way.
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This is where we venture into the world of React and Javascript and create our
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own new components for our Invocations. Do not fear the world of JS. It's
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actually pretty straightforward.
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Let us create a new component for our custom color field we created above. When
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we use a color field, let us say we want the UI to display a color picker for
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the user to pick from rather than entering values. That is what we will build
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now.
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-->
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Custom fields only support connection inputs in the Workflow Editor.
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