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Get area-based regional prompting working.

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This commit is contained in:
Ryan Dick 2024-02-24 18:28:27 -05:00
parent bc869cad62
commit ef8177ba4b
3 changed files with 25 additions and 11 deletions
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6
invokeai/app/invocations/primitives.py
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@ -423,6 +423,7 @@ class ColorInvocation(BaseInvocation):
# region Conditioning
class DenoisingArea(BaseModel):
top_y: int = Field(description="TODO")
left_x: int = Field(description="TODO")
@ -439,7 +440,10 @@ class ConditioningField(BaseModel):
# description="The mask associated with this conditioning tensor. Excluded regions should be set to 0, included "
# "regions should be set to 1.",
# )
denoising_area: DenoisingArea = Field(description="The area to apply this conditioning to. Coordinates are in latent space.")
denoising_area: Optional[DenoisingArea] = Field(
description="The area to apply this conditioning to. Coordinates are in latent space.",
default=None,
)
mask_strength: float = Field(
default=1.0,
description="The strength of the mask. Only has an effect if mask_name is set. The strength is relative to "

2
invokeai/backend/stable_diffusion/diffusion/conditioning_data.py
View File

@ -45,7 +45,7 @@ class TextConditioningInfoWithArea:
def __init__(
self,
text_conditioning_info: Union[BasicConditioningInfo, SDXLConditioningInfo],
area: DenoisingArea,
area: Optional[DenoisingArea],
mask_strength: float,
):
self.text_conditioning_info = text_conditioning_info

28
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -5,11 +5,10 @@ from contextlib import contextmanager
from typing import Any, Callable, Optional, Union
import torch
import torchvision
from diffusers import UNet2DConditionModel
from typing_extensions import TypeAlias
from invokeai.app.invocations.primitives import DenoisingArea
from invokeai.app.invocations.primitives import DenoisingArea
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
BasicConditioningInfo,
@ -256,13 +255,21 @@ class InvokeAIDiffuserComponent:
# )
else:
area = text_conditioning.area
# TODO(ryand): Use LATENT_SCALE_FACTOR instead of hard-coding to 8.
latent_area = DenoisingArea(
height=max(1, area.height // 8),
width=max(1, area.width // 8),
top_y=area.top_y // 8,
left_x=area.left_x // 8,
)
if area is None:
latent_area = DenoisingArea(
height=latent_shape[2],
width=latent_shape[3],
top_y=0,
left_x=0,
)
else:
# TODO(ryand): Use LATENT_SCALE_FACTOR instead of hard-coding to 8.
latent_area = DenoisingArea(
height=max(1, area.height // 8),
width=max(1, area.width // 8),
top_y=area.top_y // 8,
left_x=area.left_x // 8,
)
area_sample = sample[
:,
:,
@ -288,16 +295,19 @@ class InvokeAIDiffuserComponent:
cond_next_xs[
:,
i,
:,
latent_area.top_y : latent_area.top_y + latent_area.height,
latent_area.left_x : latent_area.left_x + latent_area.width,
] = conditioned_next_x * text_conditioning.mask_strength
uncond_next_xs[
:,
i,
:,
latent_area.top_y : latent_area.top_y + latent_area.height,
latent_area.left_x : latent_area.left_x + latent_area.width,
] = unconditioned_next_x * text_conditioning.mask_strength
cond_count[
:,
:,
latent_area.top_y : latent_area.top_y + latent_area.height,
latent_area.left_x : latent_area.left_x + latent_area.width,