mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
Move BlendLatentsInvocation to its own file. No functional changes.
This commit is contained in:
Ryan Dick
parent
ed03d281e6
commit
595096bdcf
98
invokeai/app/invocations/blend_latents.py
Normal file
98
invokeai/app/invocations/blend_latents.py
Normal file
@ -0,0 +1,98 @@
|
||||
from typing import Any, Union
|
||||
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
import torch
|
||||
|
||||
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
|
||||
from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField, LatentsField
|
||||
from invokeai.app.invocations.primitives import LatentsOutput
|
||||
from invokeai.app.services.shared.invocation_context import InvocationContext
|
||||
from invokeai.backend.util.devices import TorchDevice
|
||||
|
||||
|
||||
@invocation(
|
||||
"lblend",
|
||||
title="Blend Latents",
|
||||
tags=["latents", "blend"],
|
||||
category="latents",
|
||||
version="1.0.3",
|
||||
)
|
||||
class BlendLatentsInvocation(BaseInvocation):
|
||||
"""Blend two latents using a given alpha. Latents must have same size."""
|
||||
|
||||
latents_a: LatentsField = InputField(
|
||||
description=FieldDescriptions.latents,
|
||||
input=Input.Connection,
|
||||
)
|
||||
latents_b: LatentsField = InputField(
|
||||
description=FieldDescriptions.latents,
|
||||
input=Input.Connection,
|
||||
)
|
||||
alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
latents_a = context.tensors.load(self.latents_a.latents_name)
|
||||
latents_b = context.tensors.load(self.latents_b.latents_name)
|
||||
|
||||
if latents_a.shape != latents_b.shape:
|
||||
raise Exception("Latents to blend must be the same size.")
|
||||
|
||||
device = TorchDevice.choose_torch_device()
|
||||
|
||||
def slerp(
|
||||
t: Union[float, npt.NDArray[Any]], # FIXME: maybe use np.float32 here?
|
||||
v0: Union[torch.Tensor, npt.NDArray[Any]],
|
||||
v1: Union[torch.Tensor, npt.NDArray[Any]],
|
||||
DOT_THRESHOLD: float = 0.9995,
|
||||
) -> Union[torch.Tensor, npt.NDArray[Any]]:
|
||||
"""
|
||||
Spherical linear interpolation
|
||||
Args:
|
||||
t (float/np.ndarray): Float value between 0.0 and 1.0
|
||||
v0 (np.ndarray): Starting vector
|
||||
v1 (np.ndarray): Final vector
|
||||
DOT_THRESHOLD (float): Threshold for considering the two vectors as
|
||||
colineal. Not recommended to alter this.
|
||||
Returns:
|
||||
v2 (np.ndarray): Interpolation vector between v0 and v1
|
||||
"""
|
||||
inputs_are_torch = False
|
||||
if not isinstance(v0, np.ndarray):
|
||||
inputs_are_torch = True
|
||||
v0 = v0.detach().cpu().numpy()
|
||||
if not isinstance(v1, np.ndarray):
|
||||
inputs_are_torch = True
|
||||
v1 = v1.detach().cpu().numpy()
|
||||
|
||||
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
|
||||
if np.abs(dot) > DOT_THRESHOLD:
|
||||
v2 = (1 - t) * v0 + t * v1
|
||||
else:
|
||||
theta_0 = np.arccos(dot)
|
||||
sin_theta_0 = np.sin(theta_0)
|
||||
theta_t = theta_0 * t
|
||||
sin_theta_t = np.sin(theta_t)
|
||||
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
|
||||
s1 = sin_theta_t / sin_theta_0
|
||||
v2 = s0 * v0 + s1 * v1
|
||||
|
||||
if inputs_are_torch:
|
||||
v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
|
||||
return v2_torch
|
||||
else:
|
||||
assert isinstance(v2, np.ndarray)
|
||||
return v2
|
||||
|
||||
# blend
|
||||
bl = slerp(self.alpha, latents_a, latents_b)
|
||||
assert isinstance(bl, torch.Tensor)
|
||||
blended_latents: torch.Tensor = bl # for type checking convenience
|
||||
|
||||
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
|
||||
blended_latents = blended_latents.to("cpu")
|
||||
|
||||
TorchDevice.empty_cache()
|
||||
|
||||
name = context.tensors.save(tensor=blended_latents)
|
||||
return LatentsOutput.build(latents_name=name, latents=blended_latents, seed=self.latents_a.seed)
|
||||
@ -6,7 +6,6 @@ from typing import Any, Dict, Iterator, List, Literal, Optional, Tuple, Union
|
||||
|
||||
import einops
|
||||
import numpy as np
|
||||
import numpy.typing as npt
|
||||
import torch
|
||||
import torchvision
|
||||
import torchvision.transforms as T
|
||||
@ -1304,90 +1303,3 @@ class ImageToLatentsInvocation(BaseInvocation):
|
||||
assert isinstance(vae, torch.nn.Module)
|
||||
latents: torch.FloatTensor = vae.encode(image_tensor).latents
|
||||
return latents
|
||||
|
||||
|
||||
@invocation(
|
||||
"lblend",
|
||||
title="Blend Latents",
|
||||
tags=["latents", "blend"],
|
||||
category="latents",
|
||||
version="1.0.3",
|
||||
)
|
||||
class BlendLatentsInvocation(BaseInvocation):
|
||||
"""Blend two latents using a given alpha. Latents must have same size."""
|
||||
|
||||
latents_a: LatentsField = InputField(
|
||||
description=FieldDescriptions.latents,
|
||||
input=Input.Connection,
|
||||
)
|
||||
latents_b: LatentsField = InputField(
|
||||
description=FieldDescriptions.latents,
|
||||
input=Input.Connection,
|
||||
)
|
||||
alpha: float = InputField(default=0.5, description=FieldDescriptions.blend_alpha)
|
||||
|
||||
def invoke(self, context: InvocationContext) -> LatentsOutput:
|
||||
latents_a = context.tensors.load(self.latents_a.latents_name)
|
||||
latents_b = context.tensors.load(self.latents_b.latents_name)
|
||||
|
||||
if latents_a.shape != latents_b.shape:
|
||||
raise Exception("Latents to blend must be the same size.")
|
||||
|
||||
device = TorchDevice.choose_torch_device()
|
||||
|
||||
def slerp(
|
||||
t: Union[float, npt.NDArray[Any]], # FIXME: maybe use np.float32 here?
|
||||
v0: Union[torch.Tensor, npt.NDArray[Any]],
|
||||
v1: Union[torch.Tensor, npt.NDArray[Any]],
|
||||
DOT_THRESHOLD: float = 0.9995,
|
||||
) -> Union[torch.Tensor, npt.NDArray[Any]]:
|
||||
"""
|
||||
Spherical linear interpolation
|
||||
Args:
|
||||
t (float/np.ndarray): Float value between 0.0 and 1.0
|
||||
v0 (np.ndarray): Starting vector
|
||||
v1 (np.ndarray): Final vector
|
||||
DOT_THRESHOLD (float): Threshold for considering the two vectors as
|
||||
colineal. Not recommended to alter this.
|
||||
Returns:
|
||||
v2 (np.ndarray): Interpolation vector between v0 and v1
|
||||
"""
|
||||
inputs_are_torch = False
|
||||
if not isinstance(v0, np.ndarray):
|
||||
inputs_are_torch = True
|
||||
v0 = v0.detach().cpu().numpy()
|
||||
if not isinstance(v1, np.ndarray):
|
||||
inputs_are_torch = True
|
||||
v1 = v1.detach().cpu().numpy()
|
||||
|
||||
dot = np.sum(v0 * v1 / (np.linalg.norm(v0) * np.linalg.norm(v1)))
|
||||
if np.abs(dot) > DOT_THRESHOLD:
|
||||
v2 = (1 - t) * v0 + t * v1
|
||||
else:
|
||||
theta_0 = np.arccos(dot)
|
||||
sin_theta_0 = np.sin(theta_0)
|
||||
theta_t = theta_0 * t
|
||||
sin_theta_t = np.sin(theta_t)
|
||||
s0 = np.sin(theta_0 - theta_t) / sin_theta_0
|
||||
s1 = sin_theta_t / sin_theta_0
|
||||
v2 = s0 * v0 + s1 * v1
|
||||
|
||||
if inputs_are_torch:
|
||||
v2_torch: torch.Tensor = torch.from_numpy(v2).to(device)
|
||||
return v2_torch
|
||||
else:
|
||||
assert isinstance(v2, np.ndarray)
|
||||
return v2
|
||||
|
||||
# blend
|
||||
bl = slerp(self.alpha, latents_a, latents_b)
|
||||
assert isinstance(bl, torch.Tensor)
|
||||
blended_latents: torch.Tensor = bl # for type checking convenience
|
||||
|
||||
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
|
||||
blended_latents = blended_latents.to("cpu")
|
||||
|
||||
TorchDevice.empty_cache()
|
||||
|
||||
name = context.tensors.save(tensor=blended_latents)
|
||||
return LatentsOutput.build(latents_name=name, latents=blended_latents, seed=self.latents_a.seed)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user