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