mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
259 lines
8.4 KiB
Python
259 lines
8.4 KiB
Python
# Copyright (c) 2022 Kyle Schouviller (https://github.com/kyle0654) and the InvokeAI Team
|
|
|
|
import math
|
|
from typing import Literal, Optional, get_args
|
|
|
|
import numpy as np
|
|
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.lama import LaMA
|
|
from invokeai.backend.image_util.patchmatch import PatchMatch
|
|
|
|
from ..models.image import ImageCategory, ResourceOrigin
|
|
from .baseinvocation import BaseInvocation, InputField, InvocationContext, tags, title
|
|
|
|
|
|
def infill_methods() -> list[str]:
|
|
methods = [
|
|
"tile",
|
|
"solid",
|
|
"lama",
|
|
]
|
|
if PatchMatch.patchmatch_available():
|
|
methods.insert(0, "patchmatch")
|
|
return methods
|
|
|
|
|
|
INFILL_METHODS = Literal[tuple(infill_methods())]
|
|
DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
|
|
|
|
|
|
def infill_lama(im: Image.Image) -> Image.Image:
|
|
lama = LaMA()
|
|
return lama(im)
|
|
|
|
|
|
def infill_patchmatch(im: Image.Image) -> Image.Image:
|
|
if im.mode != "RGBA":
|
|
return im
|
|
|
|
# Skip patchmatch if patchmatch isn't available
|
|
if not PatchMatch.patchmatch_available():
|
|
return im
|
|
|
|
# Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
|
|
im_patched_np = PatchMatch.inpaint(im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3)
|
|
im_patched = Image.fromarray(im_patched_np, mode="RGB")
|
|
return im_patched
|
|
|
|
|
|
def get_tile_images(image: np.ndarray, width=8, height=8):
|
|
_nrows, _ncols, depth = image.shape
|
|
_strides = image.strides
|
|
|
|
nrows, _m = divmod(_nrows, height)
|
|
ncols, _n = divmod(_ncols, width)
|
|
if _m != 0 or _n != 0:
|
|
return None
|
|
|
|
return np.lib.stride_tricks.as_strided(
|
|
np.ravel(image),
|
|
shape=(nrows, ncols, height, width, depth),
|
|
strides=(height * _strides[0], width * _strides[1], *_strides),
|
|
writeable=False,
|
|
)
|
|
|
|
|
|
def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int] = None) -> Image.Image:
|
|
# Only fill if there's an alpha layer
|
|
if im.mode != "RGBA":
|
|
return im
|
|
|
|
a = np.asarray(im, dtype=np.uint8)
|
|
|
|
tile_size_tuple = (tile_size, tile_size)
|
|
|
|
# Get the image as tiles of a specified size
|
|
tiles = get_tile_images(a, *tile_size_tuple).copy()
|
|
|
|
# Get the mask as tiles
|
|
tiles_mask = tiles[:, :, :, :, 3]
|
|
|
|
# Find any mask tiles with any fully transparent pixels (we will be replacing these later)
|
|
tmask_shape = tiles_mask.shape
|
|
tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
|
|
n, ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
|
|
tiles_mask = tiles_mask > 0
|
|
tiles_mask = tiles_mask.reshape((n, ny)).all(axis=1)
|
|
|
|
# Get RGB tiles in single array and filter by the mask
|
|
tshape = tiles.shape
|
|
tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), *tiles.shape[2:]))
|
|
filtered_tiles = tiles_all[tiles_mask]
|
|
|
|
if len(filtered_tiles) == 0:
|
|
return im
|
|
|
|
# Find all invalid tiles and replace with a random valid tile
|
|
replace_count = (tiles_mask == False).sum()
|
|
rng = np.random.default_rng(seed=seed)
|
|
tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count), :, :, :]
|
|
|
|
# Convert back to an image
|
|
tiles_all = tiles_all.reshape(tshape)
|
|
tiles_all = tiles_all.swapaxes(1, 2)
|
|
st = tiles_all.reshape(
|
|
(
|
|
math.prod(tiles_all.shape[0:2]),
|
|
math.prod(tiles_all.shape[2:4]),
|
|
tiles_all.shape[4],
|
|
)
|
|
)
|
|
si = Image.fromarray(st, mode="RGBA")
|
|
|
|
return si
|
|
|
|
|
|
@title("Solid Color Infill")
|
|
@tags("image", "inpaint")
|
|
class InfillColorInvocation(BaseInvocation):
|
|
"""Infills transparent areas of an image with a solid color"""
|
|
|
|
type: Literal["infill_rgba"] = "infill_rgba"
|
|
|
|
# Inputs
|
|
image: ImageField = InputField(description="The image to infill")
|
|
color: ColorField = InputField(
|
|
default=ColorField(r=127, g=127, b=127, a=255),
|
|
description="The color to use to infill",
|
|
)
|
|
|
|
def invoke(self, context: InvocationContext) -> ImageOutput:
|
|
image = context.services.images.get_pil_image(self.image.image_name)
|
|
|
|
solid_bg = Image.new("RGBA", image.size, self.color.tuple())
|
|
infilled = Image.alpha_composite(solid_bg, image.convert("RGBA"))
|
|
|
|
infilled.paste(image, (0, 0), image.split()[-1])
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
@title("Tile Infill")
|
|
@tags("image", "inpaint")
|
|
class InfillTileInvocation(BaseInvocation):
|
|
"""Infills transparent areas of an image with tiles of the image"""
|
|
|
|
type: Literal["infill_tile"] = "infill_tile"
|
|
|
|
# Input
|
|
image: ImageField = InputField(description="The image to infill")
|
|
tile_size: int = InputField(default=32, ge=1, description="The tile size (px)")
|
|
seed: int = InputField(
|
|
ge=0,
|
|
le=SEED_MAX,
|
|
description="The seed to use for tile generation (omit for random)",
|
|
default_factory=get_random_seed,
|
|
)
|
|
|
|
def invoke(self, context: InvocationContext) -> ImageOutput:
|
|
image = context.services.images.get_pil_image(self.image.image_name)
|
|
|
|
infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
|
|
infilled.paste(image, (0, 0), image.split()[-1])
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
@title("PatchMatch Infill")
|
|
@tags("image", "inpaint")
|
|
class InfillPatchMatchInvocation(BaseInvocation):
|
|
"""Infills transparent areas of an image using the PatchMatch algorithm"""
|
|
|
|
type: Literal["infill_patchmatch"] = "infill_patchmatch"
|
|
|
|
# Inputs
|
|
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)
|
|
|
|
if PatchMatch.patchmatch_available():
|
|
infilled = infill_patchmatch(image.copy())
|
|
else:
|
|
raise ValueError("PatchMatch is not available on this system")
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
@title("LaMa Infill")
|
|
@tags("image", "inpaint")
|
|
class LaMaInfillInvocation(BaseInvocation):
|
|
"""Infills transparent areas of an image using the LaMa model"""
|
|
|
|
type: Literal["infill_lama"] = "infill_lama"
|
|
|
|
# Inputs
|
|
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_lama(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,
|
|
)
|