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Merge branch 'main' into refactor/rename-get-logger

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Millun Atluri
2023-09-07 23:19:59 +10:00
committed by GitHub
parent 58aa159a50 a98c37b7a3
commit b5e1ba34b3
45 changed files with 1422 additions and 414 deletions
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12
invokeai/app/api/routers/app_info.py
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@ -1,19 +1,19 @@
import typing
from enum import Enum
from pathlib import Path
from fastapi import Body
from fastapi.routing import APIRouter
from pathlib import Path
from pydantic import BaseModel, Field
from invokeai.app.invocations.upscale import ESRGAN_MODELS
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.backend.image_util.patchmatch import PatchMatch
from invokeai.backend.image_util.safety_checker import SafetyChecker
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
from invokeai.app.invocations.upscale import ESRGAN_MODELS
from invokeai.backend.util.logging import logging
from invokeai.version import __version__
from ..dependencies import ApiDependencies
from invokeai.backend.util.logging import logging
class LogLevel(int, Enum):
@ -55,7 +55,7 @@ async def get_version() -> AppVersion:
@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
async def get_config() -> AppConfig:
infill_methods = ["tile", "lama"]
infill_methods = ["tile", "lama", "cv2"]
if PatchMatch.patchmatch_available():
infill_methods.append("patchmatch")

161
invokeai/app/invocations/image.py
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@ -563,7 +563,7 @@ class MaskEdgeInvocation(BaseInvocation):
)
def invoke(self, context: InvocationContext) -> ImageOutput:
mask = context.services.images.get_pil_image(self.image.image_name)
mask = context.services.images.get_pil_image(self.image.image_name).convert("L")
npimg = numpy.asarray(mask, dtype=numpy.uint8)
npgradient = numpy.uint8(255 * (1.0 - numpy.floor(numpy.abs(0.5 - numpy.float32(npimg) / 255.0) * 2.0)))
@ -700,8 +700,13 @@ class ColorCorrectInvocation(BaseInvocation):
# Blur the mask out (into init image) by specified amount
if self.mask_blur_radius > 0:
nm = numpy.asarray(pil_init_mask, dtype=numpy.uint8)
inverted_nm = 255 - nm
dilation_size = int(round(self.mask_blur_radius) + 20)
dilating_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (dilation_size, dilation_size))
inverted_dilated_nm = cv2.dilate(inverted_nm, dilating_kernel)
dilated_nm = 255 - inverted_dilated_nm
nmd = cv2.erode(
nm,
dilated_nm,
kernel=numpy.ones((3, 3), dtype=numpy.uint8),
iterations=int(self.mask_blur_radius / 2),
)
@ -773,39 +778,95 @@ class ImageHueAdjustmentInvocation(BaseInvocation):
)
COLOR_CHANNELS = Literal[
"Red (RGBA)",
"Green (RGBA)",
"Blue (RGBA)",
"Alpha (RGBA)",
"Cyan (CMYK)",
"Magenta (CMYK)",
"Yellow (CMYK)",
"Black (CMYK)",
"Hue (HSV)",
"Saturation (HSV)",
"Value (HSV)",
"Luminosity (LAB)",
"A (LAB)",
"B (LAB)",
"Y (YCbCr)",
"Cb (YCbCr)",
"Cr (YCbCr)",
]
CHANNEL_FORMATS = {
"Red (RGBA)": ("RGBA", 0),
"Green (RGBA)": ("RGBA", 1),
"Blue (RGBA)": ("RGBA", 2),
"Alpha (RGBA)": ("RGBA", 3),
"Cyan (CMYK)": ("CMYK", 0),
"Magenta (CMYK)": ("CMYK", 1),
"Yellow (CMYK)": ("CMYK", 2),
"Black (CMYK)": ("CMYK", 3),
"Hue (HSV)": ("HSV", 0),
"Saturation (HSV)": ("HSV", 1),
"Value (HSV)": ("HSV", 2),
"Luminosity (LAB)": ("LAB", 0),
"A (LAB)": ("LAB", 1),
"B (LAB)": ("LAB", 2),
"Y (YCbCr)": ("YCbCr", 0),
"Cb (YCbCr)": ("YCbCr", 1),
"Cr (YCbCr)": ("YCbCr", 2),
}
@invocation(
"img_luminosity_adjust",
title="Adjust Image Luminosity",
tags=["image", "luminosity", "hsl"],
"img_channel_offset",
title="Offset Image Channel",
tags=[
"image",
"offset",
"red",
"green",
"blue",
"alpha",
"cyan",
"magenta",
"yellow",
"black",
"hue",
"saturation",
"luminosity",
"value",
],
category="image",
version="1.0.0",
)
class ImageLuminosityAdjustmentInvocation(BaseInvocation):
"""Adjusts the Luminosity (Value) of an image."""
class ImageChannelOffsetInvocation(BaseInvocation):
"""Add or subtract a value from a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
luminosity: float = InputField(
default=1.0, ge=0, le=1, description="The factor by which to adjust the luminosity (value)"
)
channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
offset: int = InputField(default=0, ge=-255, le=255, description="The amount to adjust the channel by")
def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.services.images.get_pil_image(self.image.image_name)
# Convert PIL image to OpenCV format (numpy array), note color channel
# ordering is changed from RGB to BGR
image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
# extract the channel and mode from the input and reference tuple
mode = CHANNEL_FORMATS[self.channel][0]
channel_number = CHANNEL_FORMATS[self.channel][1]
# Convert image to HSV color space
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Convert PIL image to new format
converted_image = numpy.array(pil_image.convert(mode)).astype(int)
image_channel = converted_image[:, :, channel_number]
# Adjust the luminosity (value)
hsv_image[:, :, 2] = numpy.clip(hsv_image[:, :, 2] * self.luminosity, 0, 255)
# Adjust the value, clipping to 0..255
image_channel = numpy.clip(image_channel + self.offset, 0, 255)
# Convert image back to BGR color space
image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
# Put the channel back into the image
converted_image[:, :, channel_number] = image_channel
# Convert back to PIL format and to original color mode
pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
# Convert back to RGBA format and output
pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,
@ -827,36 +888,60 @@ class ImageLuminosityAdjustmentInvocation(BaseInvocation):
@invocation(
"img_saturation_adjust",
title="Adjust Image Saturation",
tags=["image", "saturation", "hsl"],
"img_channel_multiply",
title="Multiply Image Channel",
tags=[
"image",
"invert",
"scale",
"multiply",
"red",
"green",
"blue",
"alpha",
"cyan",
"magenta",
"yellow",
"black",
"hue",
"saturation",
"luminosity",
"value",
],
category="image",
version="1.0.0",
)
class ImageSaturationAdjustmentInvocation(BaseInvocation):
"""Adjusts the Saturation of an image."""
class ImageChannelMultiplyInvocation(BaseInvocation):
"""Scale a specific color channel of an image."""
image: ImageField = InputField(description="The image to adjust")
saturation: float = InputField(default=1.0, ge=0, le=1, description="The factor by which to adjust the saturation")
channel: COLOR_CHANNELS = InputField(description="Which channel to adjust")
scale: float = InputField(default=1.0, ge=0.0, description="The amount to scale the channel by.")
invert_channel: bool = InputField(default=False, description="Invert the channel after scaling")
def invoke(self, context: InvocationContext) -> ImageOutput:
pil_image = context.services.images.get_pil_image(self.image.image_name)
# Convert PIL image to OpenCV format (numpy array), note color channel
# ordering is changed from RGB to BGR
image = numpy.array(pil_image.convert("RGB"))[:, :, ::-1]
# extract the channel and mode from the input and reference tuple
mode = CHANNEL_FORMATS[self.channel][0]
channel_number = CHANNEL_FORMATS[self.channel][1]
# Convert image to HSV color space
hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# Convert PIL image to new format
converted_image = numpy.array(pil_image.convert(mode)).astype(float)
image_channel = converted_image[:, :, channel_number]
# Adjust the saturation
hsv_image[:, :, 1] = numpy.clip(hsv_image[:, :, 1] * self.saturation, 0, 255)
# Adjust the value, clipping to 0..255
image_channel = numpy.clip(image_channel * self.scale, 0, 255)
# Convert image back to BGR color space
image = cv2.cvtColor(hsv_image, cv2.COLOR_HSV2BGR)
# Invert the channel if requested
if self.invert_channel:
image_channel = 255 - image_channel
# Convert back to PIL format and to original color mode
pil_image = Image.fromarray(image[:, :, ::-1], "RGB").convert("RGBA")
# Put the channel back into the image
converted_image[:, :, channel_number] = image_channel
# Convert back to RGBA format and output
pil_image = Image.fromarray(converted_image.astype(numpy.uint8), mode=mode).convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,

63
invokeai/app/invocations/infill.py
View File

@ -8,19 +8,17 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.primitives import ColorField, ImageField, ImageOutput
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
from invokeai.backend.image_util.patchmatch import PatchMatch
from ..models.image import ImageCategory, ResourceOrigin
from .baseinvocation import BaseInvocation, InputField, InvocationContext, invocation
from .image import PIL_RESAMPLING_MAP, PIL_RESAMPLING_MODES
def infill_methods() -> list[str]:
methods = [
"tile",
"solid",
"lama",
]
methods = ["tile", "solid", "lama", "cv2"]
if PatchMatch.patchmatch_available():
methods.insert(0, "patchmatch")
return methods
@ -49,6 +47,10 @@ def infill_patchmatch(im: Image.Image) -> Image.Image:
return im_patched
def infill_cv2(im: Image.Image) -> Image.Image:
return cv2_inpaint(im)
def get_tile_images(image: np.ndarray, width=8, height=8):
_nrows, _ncols, depth = image.shape
_strides = image.strides
@ -194,15 +196,35 @@ class InfillPatchMatchInvocation(BaseInvocation):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
image: ImageField = InputField(description="The image to infill")
downscale: float = InputField(default=2.0, gt=0, description="Run patchmatch on downscaled image to speedup infill")
resample_mode: PIL_RESAMPLING_MODES = InputField(default="bicubic", description="The resampling mode")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image = context.services.images.get_pil_image(self.image.image_name).convert("RGBA")
resample_mode = PIL_RESAMPLING_MAP[self.resample_mode]
infill_image = image.copy()
width = int(image.width / self.downscale)
height = int(image.height / self.downscale)
infill_image = infill_image.resize(
(width, height),
resample=resample_mode,
)
if PatchMatch.patchmatch_available():
infilled = infill_patchmatch(image.copy())
infilled = infill_patchmatch(infill_image)
else:
raise ValueError("PatchMatch is not available on this system")
infilled = infilled.resize(
(image.width, image.height),
resample=resample_mode,
)
infilled.paste(image, (0, 0), mask=image.split()[-1])
# image.paste(infilled, (0, 0), mask=image.split()[-1])
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
@ -245,3 +267,30 @@ class LaMaInfillInvocation(BaseInvocation):
width=image_dto.width,
height=image_dto.height,
)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint")
class CV2InfillInvocation(BaseInvocation):
"""Infills transparent areas of an image using OpenCV Inpainting"""
image: ImageField = InputField(description="The image to infill")
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
infilled = infill_cv2(image.copy())
image_dto = context.services.images.create(
image=infilled,
image_origin=ResourceOrigin.INTERNAL,
image_category=ImageCategory.GENERAL,
node_id=self.id,
session_id=context.graph_execution_state_id,
is_intermediate=self.is_intermediate,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)

4
invokeai/app/services/graph.py
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@ -182,7 +182,7 @@ class IterateInvocationOutput(BaseInvocationOutput):
# TODO: Fill this out and move to invocations
@invocation("iterate")
@invocation("iterate", version="1.0.0")
class IterateInvocation(BaseInvocation):
"""Iterates over a list of items"""
@ -203,7 +203,7 @@ class CollectInvocationOutput(BaseInvocationOutput):
)
@invocation("collect")
@invocation("collect", version="1.0.0")
class CollectInvocation(BaseInvocation):
"""Collects values into a collection"""