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85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
import torch
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from pydantic import field_validator
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from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
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from invokeai.app.invocations.constants import LATENT_SCALE_FACTOR
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from invokeai.app.invocations.fields import FieldDescriptions, InputField, LatentsField, OutputField
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from invokeai.app.invocations.latent_noise import (
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LatentNoiseType,
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generate_noise_tensor,
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)
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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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from invokeai.backend.util.devices import TorchDevice
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@invocation_output("noise_output")
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class NoiseOutput(BaseInvocationOutput):
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"""Invocation noise output"""
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noise: LatentsField = OutputField(description=FieldDescriptions.noise)
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width: int = OutputField(description=FieldDescriptions.width)
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height: int = OutputField(description=FieldDescriptions.height)
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@classmethod
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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.shape[-1] * LATENT_SCALE_FACTOR,
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height=latents.shape[-2] * LATENT_SCALE_FACTOR,
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)
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@invocation(
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"noise",
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title="Create Latent Noise",
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tags=["latents", "noise"],
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category="latents",
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version="1.1.0",
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)
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class NoiseInvocation(BaseInvocation):
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"""Generates latent noise for supported denoiser architectures."""
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noise_type: LatentNoiseType = InputField(default="SD", description="Architecture-specific noise type.")
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seed: int = InputField(
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default=0,
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ge=0,
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le=SEED_MAX,
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description=FieldDescriptions.seed,
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)
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width: int = InputField(
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default=512,
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multiple_of=LATENT_SCALE_FACTOR,
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gt=0,
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description=FieldDescriptions.width,
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)
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height: int = InputField(
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default=512,
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multiple_of=LATENT_SCALE_FACTOR,
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gt=0,
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description=FieldDescriptions.height,
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)
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use_cpu: bool = InputField(
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default=True,
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description="Use CPU for noise generation (for reproducible results across platforms)",
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)
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@field_validator("seed", mode="before")
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def modulo_seed(cls, v):
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"""Return the seed modulo (SEED_MAX + 1) to ensure it is within the valid range."""
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return v % (SEED_MAX + 1)
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def invoke(self, context: InvocationContext) -> NoiseOutput:
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noise = generate_noise_tensor(
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noise_type=self.noise_type,
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width=self.width,
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height=self.height,
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device=TorchDevice.choose_torch_device(),
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seed=self.seed,
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dtype=TorchDevice.choose_torch_dtype(),
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use_cpu=self.use_cpu,
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)
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name = context.tensors.save(tensor=noise)
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return NoiseOutput.build(latents_name=name, latents=noise, seed=self.seed)
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