mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
99 lines
3.7 KiB
Python
99 lines
3.7 KiB
Python
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from typing import Any, Union
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import numpy as np
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import numpy.typing as npt
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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.fields import FieldDescriptions, Input, InputField, LatentsField
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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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@invocation(
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"lblend",
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title="Blend Latents",
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tags=["latents", "blend"],
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category="latents",
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version="1.0.3",
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)
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class BlendLatentsInvocation(BaseInvocation):
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"""Blend two latents using a given alpha. Latents must have same size."""
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latents_a: LatentsField = InputField(
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description=FieldDescriptions.latents,
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input=Input.Connection,
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)
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latents_b: LatentsField = InputField(
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description=FieldDescriptions.latents,
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input=Input.Connection,
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)
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alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
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def invoke(self, context: InvocationContext) -> LatentsOutput:
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latents_a = context.tensors.load(self.latents_a.latents_name)
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latents_b = context.tensors.load(self.latents_b.latents_name)
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if latents_a.shape != latents_b.shape:
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raise Exception("Latents to blend must be the same size.")
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device = TorchDevice.choose_torch_device()
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def slerp(
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t: Union[float, npt.NDArray[Any]], # FIXME: maybe use np.float32 here?
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v0: Union[torch.Tensor, npt.NDArray[Any]],
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v1: Union[torch.Tensor, npt.NDArray[Any]],
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DOT_THRESHOLD: float = 0.9995,
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) -> Union[torch.Tensor, npt.NDArray[Any]]:
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"""
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Spherical linear interpolation
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Args:
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t (float/np.ndarray): Float value between 0.0 and 1.0
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v0 (np.ndarray): Starting vector
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v1 (np.ndarray): Final vector
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DOT_THRESHOLD (float): Threshold for considering the two vectors as
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colineal. Not recommended to alter this.
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Returns:
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v2 (np.ndarray): Interpolation vector between v0 and v1
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"""
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inputs_are_torch = False
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if not isinstance(v0, np.ndarray):
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inputs_are_torch = True
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v0 = v0.detach().cpu().numpy()
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if not isinstance(v1, np.ndarray):
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inputs_are_torch = True
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v1 = v1.detach().cpu().numpy()
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dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
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if np.abs(dot) > DOT_THRESHOLD:
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v2 = (1 - t) * v0 + t * v1
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else:
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theta_0 = np.arccos(dot)
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sin_theta_0 = np.sin(theta_0)
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theta_t = theta_0 * t
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sin_theta_t = np.sin(theta_t)
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s0 = np.sin(theta_0 - theta_t) / sin_theta_0
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s1 = sin_theta_t / sin_theta_0
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v2 = s0 * v0 + s1 * v1
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if inputs_are_torch:
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v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
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return v2_torch
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else:
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assert isinstance(v2, np.ndarray)
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return v2
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# blend
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bl = slerp(self.alpha, latents_a, latents_b)
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assert isinstance(bl, torch.Tensor)
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blended_latents: torch.Tensor = bl # for type checking convenience
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# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
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blended_latents = blended_latents.to("cpu")
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TorchDevice.empty_cache()
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name = context.tensors.save(tensor=blended_latents)
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return LatentsOutput.build(latents_name=name, latents=blended_latents, seed=self.latents_a.seed)
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