mirror of
https://github.com/invoke-ai/InvokeAI
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325 lines
12 KiB
Python
325 lines
12 KiB
Python
'''
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invokeai.backend.generator.inpaint descends from ldm.invoke.generator
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'''
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from __future__ import annotations
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import math
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import PIL
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import cv2
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import numpy as np
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import torch
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from PIL import Image, ImageFilter, ImageOps, ImageChops
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from .diffusers_pipeline import image_resized_to_grid_as_tensor, StableDiffusionGeneratorPipeline, \
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ConditioningData
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from .img2img import Img2Img
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from ldm.invoke.patchmatch import PatchMatch
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from ldm.util import debug_image
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def infill_methods()->list[str]:
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methods = [
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"tile",
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"solid",
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]
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if PatchMatch.patchmatch_available():
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methods.insert(0, 'patchmatch')
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return methods
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class Inpaint(Img2Img):
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def __init__(self, model, precision):
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self.inpaint_height = 0
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self.inpaint_width = 0
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self.enable_image_debugging = False
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self.init_latent = None
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self.pil_image = None
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self.pil_mask = None
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self.mask_blur_radius = 0
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self.infill_method = None
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super().__init__(model, precision)
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# Outpaint support code
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def get_tile_images(self, image: np.ndarray, width=8, height=8):
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_nrows, _ncols, depth = image.shape
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_strides = image.strides
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nrows, _m = divmod(_nrows, height)
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ncols, _n = divmod(_ncols, width)
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if _m != 0 or _n != 0:
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return None
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return np.lib.stride_tricks.as_strided(
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np.ravel(image),
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shape=(nrows, ncols, height, width, depth),
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strides=(height * _strides[0], width * _strides[1], *_strides),
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writeable=False
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)
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def infill_patchmatch(self, im: Image.Image) -> Image:
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if im.mode != 'RGBA':
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return im
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# Skip patchmatch if patchmatch isn't available
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if not PatchMatch.patchmatch_available():
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return im
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# Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
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im_patched_np = PatchMatch.inpaint(im.convert('RGB'), ImageOps.invert(im.split()[-1]), patch_size = 3)
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im_patched = Image.fromarray(im_patched_np, mode = 'RGB')
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return im_patched
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def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: int = None) -> Image:
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# Only fill if there's an alpha layer
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if im.mode != 'RGBA':
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return im
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a = np.asarray(im, dtype=np.uint8)
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tile_size = (tile_size, tile_size)
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# Get the image as tiles of a specified size
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tiles = self.get_tile_images(a,*tile_size).copy()
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# Get the mask as tiles
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tiles_mask = tiles[:,:,:,:,3]
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# Find any mask tiles with any fully transparent pixels (we will be replacing these later)
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tmask_shape = tiles_mask.shape
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tiles_mask = tiles_mask.reshape(math.prod(tiles_mask.shape))
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n,ny = (math.prod(tmask_shape[0:2])), math.prod(tmask_shape[2:])
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tiles_mask = (tiles_mask > 0)
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tiles_mask = tiles_mask.reshape((n,ny)).all(axis = 1)
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# Get RGB tiles in single array and filter by the mask
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tshape = tiles.shape
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tiles_all = tiles.reshape((math.prod(tiles.shape[0:2]), * tiles.shape[2:]))
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filtered_tiles = tiles_all[tiles_mask]
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if len(filtered_tiles) == 0:
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return im
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# Find all invalid tiles and replace with a random valid tile
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replace_count = (tiles_mask == False).sum()
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rng = np.random.default_rng(seed = seed)
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tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count),:,:,:]
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# Convert back to an image
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tiles_all = tiles_all.reshape(tshape)
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tiles_all = tiles_all.swapaxes(1,2)
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st = tiles_all.reshape((math.prod(tiles_all.shape[0:2]), math.prod(tiles_all.shape[2:4]), tiles_all.shape[4]))
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si = Image.fromarray(st, mode='RGBA')
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return si
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def mask_edge(self, mask: Image, edge_size: int, edge_blur: int) -> Image:
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npimg = np.asarray(mask, dtype=np.uint8)
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# Detect any partially transparent regions
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npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
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# Detect hard edges
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npedge = cv2.Canny(npimg, threshold1=100, threshold2=200)
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# Combine
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npmask = npgradient + npedge
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# Expand
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npmask = cv2.dilate(npmask, np.ones((3,3), np.uint8), iterations = int(edge_size / 2))
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new_mask = Image.fromarray(npmask)
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if edge_blur > 0:
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new_mask = new_mask.filter(ImageFilter.BoxBlur(edge_blur))
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return ImageOps.invert(new_mask)
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def seam_paint(self, im: Image.Image, seam_size: int, seam_blur: int, prompt, sampler, steps, cfg_scale, ddim_eta,
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conditioning, strength, noise, infill_method, step_callback) -> Image.Image:
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hard_mask = self.pil_image.split()[-1].copy()
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mask = self.mask_edge(hard_mask, seam_size, seam_blur)
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make_image = self.get_make_image(
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prompt,
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sampler,
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steps,
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cfg_scale,
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ddim_eta,
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conditioning,
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init_image = im.copy().convert('RGBA'),
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mask_image = mask,
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strength = strength,
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mask_blur_radius = 0,
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seam_size = 0,
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step_callback = step_callback,
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inpaint_width = im.width,
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inpaint_height = im.height,
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infill_method = infill_method
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)
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seam_noise = self.get_noise(im.width, im.height)
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result = make_image(seam_noise)
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return result
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@torch.no_grad()
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def get_make_image(self,prompt,sampler,steps,cfg_scale,ddim_eta,
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conditioning,
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init_image: PIL.Image.Image | torch.FloatTensor,
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mask_image: PIL.Image.Image | torch.FloatTensor,
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strength: float,
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mask_blur_radius: int = 8,
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# Seam settings - when 0, doesn't fill seam
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seam_size: int = 0,
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seam_blur: int = 0,
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seam_strength: float = 0.7,
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seam_steps: int = 10,
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tile_size: int = 32,
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step_callback=None,
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inpaint_replace=False, enable_image_debugging=False,
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infill_method = None,
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inpaint_width=None,
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inpaint_height=None,
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inpaint_fill:tuple(int)=(0x7F, 0x7F, 0x7F, 0xFF),
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attention_maps_callback=None,
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**kwargs):
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"""
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Returns a function returning an image derived from the prompt and
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the initial image + mask. Return value depends on the seed at
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the time you call it. kwargs are 'init_latent' and 'strength'
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"""
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self.enable_image_debugging = enable_image_debugging
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infill_method = infill_method or infill_methods()[0]
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self.infill_method = infill_method
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self.inpaint_width = inpaint_width
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self.inpaint_height = inpaint_height
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if isinstance(init_image, PIL.Image.Image):
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self.pil_image = init_image.copy()
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# Do infill
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if infill_method == 'patchmatch' and PatchMatch.patchmatch_available():
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init_filled = self.infill_patchmatch(self.pil_image.copy())
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elif infill_method == 'tile':
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init_filled = self.tile_fill_missing(
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self.pil_image.copy(),
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seed = self.seed,
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tile_size = tile_size
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)
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elif infill_method == 'solid':
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solid_bg = PIL.Image.new("RGBA", init_image.size, inpaint_fill)
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init_filled = PIL.Image.alpha_composite(solid_bg, init_image)
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else:
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raise ValueError(f"Non-supported infill type {infill_method}", infill_method)
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init_filled.paste(init_image, (0,0), init_image.split()[-1])
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# Resize if requested for inpainting
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if inpaint_width and inpaint_height:
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init_filled = init_filled.resize((inpaint_width, inpaint_height))
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debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
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# Create init tensor
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init_image = image_resized_to_grid_as_tensor(init_filled.convert('RGB'))
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if isinstance(mask_image, PIL.Image.Image):
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self.pil_mask = mask_image.copy()
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debug_image(mask_image, "mask_image BEFORE multiply with pil_image", debug_status=self.enable_image_debugging)
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init_alpha = self.pil_image.getchannel("A")
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if mask_image.mode != "L":
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# FIXME: why do we get passed an RGB image here? We can only use single-channel.
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mask_image = mask_image.convert("L")
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mask_image = ImageChops.multiply(mask_image, init_alpha)
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self.pil_mask = mask_image
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# Resize if requested for inpainting
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if inpaint_width and inpaint_height:
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mask_image = mask_image.resize((inpaint_width, inpaint_height))
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debug_image(mask_image, "mask_image AFTER multiply with pil_image", debug_status=self.enable_image_debugging)
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mask: torch.FloatTensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
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else:
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mask: torch.FloatTensor = mask_image
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self.mask_blur_radius = mask_blur_radius
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# noinspection PyTypeChecker
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pipeline: StableDiffusionGeneratorPipeline = self.model
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pipeline.scheduler = sampler
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# todo: support cross-attention control
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uc, c, _ = conditioning
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conditioning_data = (ConditioningData(uc, c, cfg_scale)
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.add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta))
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def make_image(x_T):
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pipeline_output = pipeline.inpaint_from_embeddings(
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init_image=init_image,
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mask=1 - mask, # expects white means "paint here."
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strength=strength,
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num_inference_steps=steps,
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conditioning_data=conditioning_data,
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noise_func=self.get_noise_like,
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callback=step_callback,
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)
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if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
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attention_maps_callback(pipeline_output.attention_map_saver)
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result = self.postprocess_size_and_mask(pipeline.numpy_to_pil(pipeline_output.images)[0])
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# Seam paint if this is our first pass (seam_size set to 0 during seam painting)
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if seam_size > 0:
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old_image = self.pil_image or init_image
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old_mask = self.pil_mask or mask_image
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result = self.seam_paint(result, seam_size, seam_blur, prompt, sampler, seam_steps, cfg_scale, ddim_eta,
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conditioning, seam_strength, x_T, infill_method, step_callback)
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# Restore original settings
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self.get_make_image(prompt,sampler,steps,cfg_scale,ddim_eta,
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conditioning,
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old_image,
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old_mask,
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strength,
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mask_blur_radius, seam_size, seam_blur, seam_strength,
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seam_steps, tile_size, step_callback,
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inpaint_replace, enable_image_debugging,
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inpaint_width = inpaint_width,
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inpaint_height = inpaint_height,
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infill_method = infill_method,
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**kwargs)
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return result
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return make_image
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def sample_to_image(self, samples)->Image.Image:
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gen_result = super().sample_to_image(samples).convert('RGB')
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return self.postprocess_size_and_mask(gen_result)
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def postprocess_size_and_mask(self, gen_result: Image.Image) -> Image.Image:
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debug_image(gen_result, "gen_result", debug_status=self.enable_image_debugging)
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# Resize if necessary
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if self.inpaint_width and self.inpaint_height:
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gen_result = gen_result.resize(self.pil_image.size)
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if self.pil_image is None or self.pil_mask is None:
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return gen_result
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corrected_result = self.repaste_and_color_correct(gen_result, self.pil_image, self.pil_mask, self.mask_blur_radius)
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debug_image(corrected_result, "corrected_result", debug_status=self.enable_image_debugging)
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return corrected_result
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