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

Fix all comparisons against the DEFAULT_PRECISION constant. DEFAULT_PRECISION is a torch.dtype. Previously, it was compared to a str in a number of places where it would always resolve to False. This is a bugfix that results in a change to the default behavior. In practice, this will not change the behavior for many users, because it only causes a change in behavior if a users has configured float32 as their default precision.

Browse Source
This commit is contained in:
Ryan Dick 2024-06-06 09:16:45 -04:00
parent 78852228cd
commit 76c97ec411
6 changed files with 11 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
invokeai/app/invocations/constants.py
View File

@ -1,6 +1,7 @@
from typing import Literal
from invokeai.backend.stable_diffusion.schedulers import SCHEDULER_MAP
from invokeai.backend.util.devices import TorchDevice
LATENT_SCALE_FACTOR = 8
"""
@ -15,3 +16,5 @@ SCHEDULER_NAME_VALUES = Literal[tuple(SCHEDULER_MAP.keys())]
IMAGE_MODES = Literal["L", "RGB", "RGBA", "CMYK", "YCbCr", "LAB", "HSV", "I", "F"]
"""A literal type for PIL image modes supported by Invoke"""
DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()

4
invokeai/app/invocations/create_denoise_mask.py
View File

@ -6,9 +6,9 @@ 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.constants import DEFAULT_PRECISION
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
@ -30,7 +30,7 @@ class CreateDenoiseMaskInvocation(BaseInvocation):
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",
default=DEFAULT_PRECISION == torch.float32,
description=FieldDescriptions.fp32,
ui_order=4,
)

4
invokeai/app/invocations/create_gradient_mask.py
View File

@ -7,6 +7,7 @@ from PIL import Image, ImageFilter
from torchvision.transforms.functional import resize as tv_resize
from invokeai.app.invocations.baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
from invokeai.app.invocations.constants import DEFAULT_PRECISION
from invokeai.app.invocations.fields import (
DenoiseMaskField,
FieldDescriptions,
@ -16,7 +17,6 @@ from invokeai.app.invocations.fields import (
OutputField,
)
from invokeai.app.invocations.image_to_latents import ImageToLatentsInvocation
from invokeai.app.invocations.latent import DEFAULT_PRECISION
from invokeai.app.invocations.model import UNetField, VAEField
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.model_manager import LoadedModel
@ -74,7 +74,7 @@ class CreateGradientMaskInvocation(BaseInvocation):
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled, ui_order=8)
fp32: bool = InputField(
default=DEFAULT_PRECISION == "float32",
default=DEFAULT_PRECISION == torch.float32,
description=FieldDescriptions.fp32,
ui_order=9,
)

4
invokeai/app/invocations/image_to_latents.py
View File

@ -12,13 +12,13 @@ from diffusers.models.autoencoders.autoencoder_kl import AutoencoderKL
from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.constants import DEFAULT_PRECISION
from invokeai.app.invocations.fields import (
FieldDescriptions,
ImageField,
Input,
InputField,
)
from invokeai.app.invocations.latent import DEFAULT_PRECISION
from invokeai.app.invocations.model import VAEField
from invokeai.app.invocations.primitives import LatentsOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
@ -44,7 +44,7 @@ class ImageToLatentsInvocation(BaseInvocation):
input=Input.Connection,
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
@staticmethod
def vae_encode(vae_info: LoadedModel, upcast: bool, tiled: bool, image_tensor: torch.Tensor) -> torch.Tensor:

2
invokeai/app/invocations/latent.py
View File

@ -59,8 +59,6 @@ from .baseinvocation import BaseInvocation, invocation
from .controlnet_image_processors import ControlField
from .model import ModelIdentifierField, UNetField
DEFAULT_PRECISION = TorchDevice.choose_torch_dtype()
def get_scheduler(
context: InvocationContext,

4
invokeai/app/invocations/latents_to_image.py
View File

@ -11,6 +11,7 @@ from diffusers.models.autoencoders.autoencoder_tiny import AutoencoderTiny
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
from invokeai.app.invocations.constants import DEFAULT_PRECISION
from invokeai.app.invocations.fields import (
FieldDescriptions,
Input,
@ -19,7 +20,6 @@ from invokeai.app.invocations.fields import (
WithBoard,
WithMetadata,
)
from invokeai.app.invocations.latent import DEFAULT_PRECISION
from invokeai.app.invocations.model import VAEField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
@ -46,7 +46,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
input=Input.Connection,
)
tiled: bool = InputField(default=False, description=FieldDescriptions.tiled)
fp32: bool = InputField(default=DEFAULT_PRECISION == "float32", description=FieldDescriptions.fp32)
fp32: bool = InputField(default=DEFAULT_PRECISION == torch.float32, description=FieldDescriptions.fp32)
@torch.no_grad()
def invoke(self, context: InvocationContext) -> ImageOutput: