mirror of
https://github.com/invoke-ai/InvokeAI
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104 lines
3.7 KiB
Python
104 lines
3.7 KiB
Python
from typing import Literal
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import torch
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from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
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from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
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from invokeai.app.invocations.fields import (
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FieldDescriptions,
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Input,
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InputField,
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LatentsField,
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)
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from invokeai.app.invocations.primitives import LatentsOutput
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from invokeai.app.services.shared.invocation_context import InvocationContext
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from invokeai.backend.util.devices import TorchDevice
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LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
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@invocation(
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"lresize",
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title="Resize Latents",
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tags=["latents", "resize"],
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category="latents",
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version="1.0.2",
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)
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class ResizeLatentsInvocation(BaseInvocation):
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"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
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latents: LatentsField = InputField(
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description=FieldDescriptions.latents,
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input=Input.Connection,
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)
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width: int = InputField(
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ge=64,
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multiple_of=LATENT_SCALE_FACTOR,
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description=FieldDescriptions.width,
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)
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height: int = InputField(
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ge=64,
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multiple_of=LATENT_SCALE_FACTOR,
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description=FieldDescriptions.width,
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)
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mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
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antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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latents = context.tensors.load(self.latents.latents_name)
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device = TorchDevice.choose_torch_device()
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resized_latents = torch.nn.functional.interpolate(
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latents.to(device),
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size=(self.height // LATENT_SCALE_FACTOR, self.width // LATENT_SCALE_FACTOR),
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mode=self.mode,
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antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
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)
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# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
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resized_latents = resized_latents.to("cpu")
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TorchDevice.empty_cache()
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name = context.tensors.save(tensor=resized_latents)
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return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
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@invocation(
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"lscale",
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title="Scale Latents",
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tags=["latents", "resize"],
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category="latents",
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version="1.0.2",
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)
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class ScaleLatentsInvocation(BaseInvocation):
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"""Scales latents by a given factor."""
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latents: LatentsField = InputField(
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description=FieldDescriptions.latents,
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input=Input.Connection,
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)
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scale_factor: float = InputField(gt=0, description=FieldDescriptions.scale_factor)
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mode: LATENTS_INTERPOLATION_MODE = InputField(default="bilinear", description=FieldDescriptions.interp_mode)
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antialias: bool = InputField(default=False, description=FieldDescriptions.torch_antialias)
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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latents = context.tensors.load(self.latents.latents_name)
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device = TorchDevice.choose_torch_device()
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# resizing
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resized_latents = torch.nn.functional.interpolate(
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latents.to(device),
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scale_factor=self.scale_factor,
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mode=self.mode,
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antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
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)
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# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
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resized_latents = resized_latents.to("cpu")
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TorchDevice.empty_cache()
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name = context.tensors.save(tensor=resized_latents)
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return LatentsOutput.build(latents_name=name, latents=resized_latents, seed=self.latents.seed)
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