Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Move CreateDenoiseMaskInvocation to its own file. No functional changes.

Browse Source
This commit is contained in:
Ryan Dick 2024-06-05 14:59:45 -04:00
parent 638bf33483
commit dec0ffd47c
2 changed files with 82 additions and 70 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

80
invokeai/app/invocations/create_denoise_mask.py Normal file
View File

@ -0,0 +1,80 @@
from typing import Optional
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.fields import FieldDescriptions, ImageField, Input, InputField
from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
from invokeai.app.invocations.latent import DEFAULT_PRECISION
from invokeai.app.invocations.model import VAEField
from invokeai.app.invocations.primitives import DenoiseMaskOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.stable_diffusion.diffusers_pipeline import image_resized_to_grid_as_tensor
@invocation(
"create_denoise_mask",
title="Create Denoise Mask",
tags=["mask", "denoise"],
category="latents",
version="1.0.2",
)
class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
fp32: bool = InputField(
default=DEFAULT_PRECISION == "float32",
description=FieldDescriptions.fp32,
ui_order=4,
)
def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
if mask_image.mode != "L":
mask_image = mask_image.convert("L")
mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
if mask_tensor.dim() == 3:
mask_tensor = mask_tensor.unsqueeze(0)
# if shape is not None:
# mask_tensor = tv_resize(mask_tensor, shape, T.InterpolationMode.BILINEAR)
return mask_tensor
@torch.no_grad()
def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
if self.image is not None:
image = context.images.get_pil(self.image.image_name)
image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
if image_tensor.dim() == 3:
image_tensor = image_tensor.unsqueeze(0)
else:
image_tensor = None
mask = self.prep_mask_tensor(
context.images.get_pil(self.mask.image_name),
)
if image_tensor is not None:
vae_info = context.models.load(self.vae.vae)
img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
# TODO:
masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
masked_latents_name = context.tensors.save(tensor=masked_latents)
else:
masked_latents_name = None
mask_name = context.tensors.save(tensor=mask)
return DenoiseMaskOutput.build(
mask_name=mask_name,
masked_latents_name=masked_latents_name,
gradient=False,
)

72
invokeai/app/invocations/latent.py
View File

@ -12,7 +12,6 @@ from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
from diffusers.schedulers.scheduling_dpmsolver_sde import DPMSolverSDEScheduler
from diffusers.schedulers.scheduling_tcd import TCDScheduler
from diffusers.schedulers.scheduling_utils import SchedulerMixin as Scheduler
from PIL import Image
from pydantic import field_validator
from torchvision.transforms.functional import resize as tv_resize
from transformers import CLIPVisionModelWithProjection
@ -22,7 +21,6 @@ from invokeai.app.invocations.fields import (
ConditioningField,
DenoiseMaskField,
FieldDescriptions,
ImageField,
Input,
InputField,
LatentsField,
@ -30,7 +28,7 @@ from invokeai.app.invocations.fields import (
UIType,
)
from invokeai.app.invocations.ip_adapter import IPAdapterField
from invokeai.app.invocations.primitives import DenoiseMaskOutput, LatentsOutput
from invokeai.app.invocations.primitives import LatentsOutput
from invokeai.app.invocations.t2i_adapter import T2IAdapterField
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.controlnet_utils import prepare_control_image
@ -55,13 +53,12 @@ from ...backend.stable_diffusion.diffusers_pipeline import (
ControlNetData,
StableDiffusionGeneratorPipeline,
T2IAdapterData,
image_resized_to_grid_as_tensor,
)
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import TorchDevice
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
from .controlnet_image_processors import ControlField
from .model import ModelIdentifierField, UNetField, VAEField
from .model import ModelIdentifierField, UNetField
DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()
@ -91,71 +88,6 @@ class SchedulerInvocation(BaseInvocation):
return SchedulerOutput(scheduler=self.scheduler)
@invocation(
"create_denoise_mask",
title="Create Denoise Mask",
tags=["mask", "denoise"],
category="latents",
version="1.0.2",
)
class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
vae: VAEField = InputField(description=FieldDescriptions.vae, input=Input.Connection, ui_order=0)
image: Optional[ImageField] = InputField(default=None, description="Image which will be masked", ui_order=1)
mask: ImageField = InputField(description="The mask to use when pasting", ui_order=2)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=3)
fp32: bool = InputField(
default=DEFAULT_PRECISION == "float32",
description=FieldDescriptions.fp32,
ui_order=4,
)
def prep_mask_tensor(self, mask_image: Image.Image) -> torch.Tensor:
if mask_image.mode != "L":
mask_image = mask_image.convert("L")
mask_tensor: torch.Tensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
if mask_tensor.dim() == 3:
mask_tensor = mask_tensor.unsqueeze(0)
# if shape is not None:
# mask_tensor = tv_resize(mask_tensor, shape, T.InterpolationMode.BILINEAR)
return mask_tensor
@torch.no_grad()
def invoke(self, context: InvocationContext) -> DenoiseMaskOutput:
if self.image is not None:
image = context.images.get_pil(self.image.image_name)
image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
if image_tensor.dim() == 3:
image_tensor = image_tensor.unsqueeze(0)
else:
image_tensor = None
mask = self.prep_mask_tensor(
context.images.get_pil(self.mask.image_name),
)
if image_tensor is not None:
vae_info = context.models.load(self.vae.vae)
img_mask = tv_resize(mask, image_tensor.shape[-2:], T.InterpolationMode.BILINEAR, antialias=False)
masked_image = image_tensor * torch.where(img_mask < 0.5, 0.0, 1.0)
# TODO:
masked_latents = ImageToLatentsInvocation.vae_encode(vae_info, self.fp32, self.tiled, masked_image.clone())
masked_latents_name = context.tensors.save(tensor=masked_latents)
else:
masked_latents_name = None
mask_name = context.tensors.save(tensor=mask)
return DenoiseMaskOutput.build(
mask_name=mask_name,
masked_latents_name=masked_latents_name,
gradient=False,
)
def get_scheduler(
context: InvocationContext,
scheduler_info: ModelIdentifierField,