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feat(nodes): use LATENT_SCALE_FACTOR const in tensor output builders
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@ -5,6 +5,7 @@ import torch
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from pydantic import field_validator
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from invokeai.app.invocations.fields import FieldDescriptions, InputField, LatentsField, OutputField
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from invokeai.app.invocations.latent import LATENT_SCALE_FACTOR
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.app.util.misc import SEED_MAX
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@ -70,8 +71,8 @@ class NoiseOutput(BaseInvocationOutput):
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def build(cls, latents_name: str, latents: torch.Tensor, seed: int) -> "NoiseOutput":
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return cls(
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noise=LatentsField(latents_name=latents_name, seed=seed),
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width=latents.size()[3] * 8,
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height=latents.size()[2] * 8,
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width=latents.size()[3] * LATENT_SCALE_FACTOR,
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height=latents.size()[2] * LATENT_SCALE_FACTOR,
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)
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@ -16,6 +16,7 @@ from invokeai.app.invocations.fields import (
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OutputField,
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UIComponent,
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)
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from invokeai.app.invocations.latent import LATENT_SCALE_FACTOR
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from invokeai.app.services.images.images_common import ImageDTO
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from invokeai.app.services.shared.invocation_context import InvocationContext
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@ -321,8 +322,8 @@ class LatentsOutput(BaseInvocationOutput):
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def build(cls, latents_name: str, latents: torch.Tensor, seed: Optional[int] = None) -> "LatentsOutput":
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return cls(
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latents=LatentsField(latents_name=latents_name, seed=seed),
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width=latents.size()[3] * 8,
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height=latents.size()[2] * 8,
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width=latents.size()[3] * LATENT_SCALE_FACTOR,
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height=latents.size()[2] * LATENT_SCALE_FACTOR,
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)
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