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https://github.com/invoke-ai/InvokeAI
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feat(nodes): add w/h to latents outputs
This reduces the number of nodes needed when working with latents (ie fewer plain integer value nodes) Also correct a few mistakes in the fields
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@ -40,17 +40,40 @@ class LatentsField(BaseModel):
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class LatentsOutput(BaseInvocationOutput):
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"""Base class for invocations that output latents"""
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#fmt: off
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type: Literal["latent_output"] = "latent_output"
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latents: LatentsField = Field(default=None, description="The output latents")
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type: Literal["latents_output"] = "latents_output"
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# Inputs
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latents: LatentsField = Field(default=None, description="The output latents")
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width: int = Field(description="The width of the latents in pixels")
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height: int = Field(description="The height of the latents in pixels")
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#fmt: on
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def build_latents_output(latents_name: str, latents: torch.Tensor):
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return LatentsOutput(
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latents=LatentsField(latents_name=latents_name),
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width=latents.size()[3] * 8,
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height=latents.size()[2] * 8,
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)
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class NoiseOutput(BaseInvocationOutput):
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"""Invocation noise output"""
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#fmt: off
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type: Literal["noise_output"] = "noise_output"
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type: Literal["noise_output"] = "noise_output"
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# Inputs
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noise: LatentsField = Field(default=None, description="The output noise")
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width: int = Field(description="The width of the noise in pixels")
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height: int = Field(description="The height of the noise in pixels")
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#fmt: on
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def build_noise_output(latents_name: str, latents: torch.Tensor):
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return NoiseOutput(
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noise=LatentsField(latents_name=latents_name),
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width=latents.size()[3] * 8,
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height=latents.size()[2] * 8,
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)
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# TODO: this seems like a hack
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scheduler_map = dict(
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@ -130,9 +153,7 @@ class NoiseInvocation(BaseInvocation):
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name = f'{context.graph_execution_state_id}__{self.id}'
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context.services.latents.set(name, noise)
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return NoiseOutput(
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noise=LatentsField(latents_name=name)
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)
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return build_noise_output(latents_name=name, latents=noise)
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# Text to image
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@ -248,9 +269,7 @@ class TextToLatentsInvocation(BaseInvocation):
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name = f'{context.graph_execution_state_id}__{self.id}'
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context.services.latents.set(name, result_latents)
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return LatentsOutput(
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latents=LatentsField(latents_name=name)
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)
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return build_latents_output(latents_name=name, latents=result_latents)
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class LatentsToLatentsInvocation(TextToLatentsInvocation):
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@ -313,9 +332,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
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name = f'{context.graph_execution_state_id}__{self.id}'
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context.services.latents.set(name, result_latents)
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return LatentsOutput(
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latents=LatentsField(latents_name=name)
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)
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return build_latents_output(latents_name=name, latents=result_latents)
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# Latent to image
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@ -379,11 +396,11 @@ class ResizeLatentsInvocation(BaseInvocation):
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type: Literal["lresize"] = "lresize"
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# Inputs
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latents: Optional[LatentsField] = Field(description="The latents to resize")
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width: int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
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height: int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
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mode: Optional[LATENTS_INTERPOLATION_MODE] = Field(default="bilinear", description="The interpolation mode")
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antialias: Optional[bool] = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
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latents: Optional[LatentsField] = Field(description="The latents to resize")
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width: int = Field(ge=64, multiple_of=8, description="The width to resize to (px)")
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height: int = Field(ge=64, multiple_of=8, description="The height to resize to (px)")
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mode: LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
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antialias: bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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latents = context.services.latents.get(self.latents.latents_name)
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@ -400,7 +417,7 @@ class ResizeLatentsInvocation(BaseInvocation):
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name = f"{context.graph_execution_state_id}__{self.id}"
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context.services.latents.set(name, resized_latents)
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return LatentsOutput(latents=LatentsField(latents_name=name))
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return build_latents_output(latents_name=name, latents=resized_latents)
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class ScaleLatentsInvocation(BaseInvocation):
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@ -409,10 +426,10 @@ class ScaleLatentsInvocation(BaseInvocation):
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type: Literal["lscale"] = "lscale"
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# Inputs
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latents: Optional[LatentsField] = Field(description="The latents to scale")
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scale_factor: float = Field(gt=0, description="The factor by which to scale the latents")
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mode: Optional[LATENTS_INTERPOLATION_MODE] = Field(default="bilinear", description="The interpolation mode")
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antialias: Optional[bool] = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
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latents: Optional[LatentsField] = Field(description="The latents to scale")
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scale_factor: float = Field(gt=0, description="The factor by which to scale the latents")
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mode: LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
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antialias: bool = Field(default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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latents = context.services.latents.get(self.latents.latents_name)
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@ -430,7 +447,7 @@ class ScaleLatentsInvocation(BaseInvocation):
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name = f"{context.graph_execution_state_id}__{self.id}"
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context.services.latents.set(name, resized_latents)
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return LatentsOutput(latents=LatentsField(latents_name=name))
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return build_latents_output(latents_name=name, latents=resized_latents)
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class ImageToLatentsInvocation(BaseInvocation):
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@ -474,4 +491,4 @@ class ImageToLatentsInvocation(BaseInvocation):
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name = f"{context.graph_execution_state_id}__{self.id}"
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context.services.latents.set(name, latents)
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return LatentsOutput(latents=LatentsField(latents_name=name))
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return build_latents_output(latents_name=name, latents=latents)
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