748 lines
30 KiB
Python
748 lines
30 KiB
Python
import numpy as np
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import gradio as gr
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import math
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from modules.ui_components import InputAccordion
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import modules.scripts as scripts
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class SoftInpaintingSettings:
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def __init__(self,
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mask_blend_power,
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mask_blend_scale,
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inpaint_detail_preservation,
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composite_mask_influence,
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composite_difference_threshold,
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composite_difference_contrast):
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self.mask_blend_power = mask_blend_power
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self.mask_blend_scale = mask_blend_scale
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self.inpaint_detail_preservation = inpaint_detail_preservation
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self.composite_mask_influence = composite_mask_influence
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self.composite_difference_threshold = composite_difference_threshold
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self.composite_difference_contrast = composite_difference_contrast
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def add_generation_params(self, dest):
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dest[enabled_gen_param_label] = True
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dest[gen_param_labels.mask_blend_power] = self.mask_blend_power
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dest[gen_param_labels.mask_blend_scale] = self.mask_blend_scale
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dest[gen_param_labels.inpaint_detail_preservation] = self.inpaint_detail_preservation
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dest[gen_param_labels.composite_mask_influence] = self.composite_mask_influence
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dest[gen_param_labels.composite_difference_threshold] = self.composite_difference_threshold
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dest[gen_param_labels.composite_difference_contrast] = self.composite_difference_contrast
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# ------------------- Methods -------------------
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def processing_uses_inpainting(p):
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# TODO: Figure out a better way to determine if inpainting is being used by p
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if getattr(p, "image_mask", None) is not None:
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return True
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if getattr(p, "mask", None) is not None:
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return True
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if getattr(p, "nmask", None) is not None:
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return True
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return False
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def latent_blend(settings, a, b, t):
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"""
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Interpolates two latent image representations according to the parameter t,
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where the interpolated vectors' magnitudes are also interpolated separately.
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The "detail_preservation" factor biases the magnitude interpolation towards
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the larger of the two magnitudes.
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"""
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import torch
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# NOTE: We use inplace operations wherever possible.
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# [4][w][h] to [1][4][w][h]
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t2 = t.unsqueeze(0)
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# [4][w][h] to [1][1][w][h] - the [4] seem redundant.
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t3 = t[0].unsqueeze(0).unsqueeze(0)
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one_minus_t2 = 1 - t2
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one_minus_t3 = 1 - t3
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# Linearly interpolate the image vectors.
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a_scaled = a * one_minus_t2
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b_scaled = b * t2
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image_interp = a_scaled
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image_interp.add_(b_scaled)
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result_type = image_interp.dtype
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del a_scaled, b_scaled, t2, one_minus_t2
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# Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
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# 64-bit operations are used here to allow large exponents.
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current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
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# Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
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a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
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settings.inpaint_detail_preservation) * one_minus_t3
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b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
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settings.inpaint_detail_preservation) * t3
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desired_magnitude = a_magnitude
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desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation)
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del a_magnitude, b_magnitude, t3, one_minus_t3
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# Change the linearly interpolated image vectors' magnitudes to the value we want.
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# This is the last 64-bit operation.
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image_interp_scaling_factor = desired_magnitude
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image_interp_scaling_factor.div_(current_magnitude)
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image_interp_scaling_factor = image_interp_scaling_factor.to(result_type)
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image_interp_scaled = image_interp
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image_interp_scaled.mul_(image_interp_scaling_factor)
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del current_magnitude
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del desired_magnitude
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del image_interp
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del image_interp_scaling_factor
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del result_type
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return image_interp_scaled
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def get_modified_nmask(settings, nmask, sigma):
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"""
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Converts a negative mask representing the transparency of the original latent vectors being overlaid
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to a mask that is scaled according to the denoising strength for this step.
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Where:
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0 = fully opaque, infinite density, fully masked
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1 = fully transparent, zero density, fully unmasked
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We bring this transparency to a power, as this allows one to simulate N number of blending operations
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where N can be any positive real value. Using this one can control the balance of influence between
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the denoiser and the original latents according to the sigma value.
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NOTE: "mask" is not used
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"""
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import torch
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return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale)
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def apply_adaptive_masks(
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settings: SoftInpaintingSettings,
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nmask,
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latent_orig,
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latent_processed,
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overlay_images,
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width, height,
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paste_to):
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import torch
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import modules.processing as proc
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import modules.images as images
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from PIL import Image, ImageOps, ImageFilter
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# TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control.
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latent_mask = nmask[0].float()
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# convert the original mask into a form we use to scale distances for thresholding
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mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2))
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mask_scalar = (0.5 * (1 - settings.composite_mask_influence)
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+ mask_scalar * settings.composite_mask_influence)
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mask_scalar = mask_scalar / (1.00001 - mask_scalar)
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mask_scalar = mask_scalar.cpu().numpy()
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latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
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kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
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masks_for_overlay = []
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for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)):
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converted_mask = distance_map.float().cpu().numpy()
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converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
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percentile_min=0.9, percentile_max=1, min_width=1)
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converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center,
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percentile_min=0.25, percentile_max=0.75, min_width=1)
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# The distance at which opacity of original decreases to 50%
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half_weighted_distance = settings.composite_difference_threshold * mask_scalar
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converted_mask = converted_mask / half_weighted_distance
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converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast)
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converted_mask = smootherstep(converted_mask)
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converted_mask = 1 - converted_mask
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converted_mask = 255. * converted_mask
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converted_mask = converted_mask.astype(np.uint8)
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converted_mask = Image.fromarray(converted_mask)
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converted_mask = images.resize_image(2, converted_mask, width, height)
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converted_mask = proc.create_binary_mask(converted_mask, round=False)
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# Remove aliasing artifacts using a gaussian blur.
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converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
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# Expand the mask to fit the whole image if needed.
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if paste_to is not None:
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converted_mask = proc.uncrop(converted_mask,
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(overlay_image.width, overlay_image.height),
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paste_to)
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masks_for_overlay.append(converted_mask)
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image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
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image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
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mask=ImageOps.invert(converted_mask.convert('L')))
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overlay_images[i] = image_masked.convert('RGBA')
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return masks_for_overlay
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def apply_masks(
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settings,
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nmask,
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overlay_images,
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width, height,
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paste_to):
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import torch
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import modules.processing as proc
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import modules.images as images
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from PIL import Image, ImageOps, ImageFilter
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converted_mask = nmask[0].float()
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converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2)
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converted_mask = 255. * converted_mask
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converted_mask = converted_mask.cpu().numpy().astype(np.uint8)
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converted_mask = Image.fromarray(converted_mask)
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converted_mask = images.resize_image(2, converted_mask, width, height)
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converted_mask = proc.create_binary_mask(converted_mask, round=False)
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# Remove aliasing artifacts using a gaussian blur.
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converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
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# Expand the mask to fit the whole image if needed.
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if paste_to is not None:
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converted_mask = proc.uncrop(converted_mask,
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(width, height),
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paste_to)
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masks_for_overlay = []
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for i, overlay_image in enumerate(overlay_images):
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masks_for_overlay[i] = converted_mask
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image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
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image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
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mask=ImageOps.invert(converted_mask.convert('L')))
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overlay_images[i] = image_masked.convert('RGBA')
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return masks_for_overlay
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def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0):
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"""
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Generalization convolution filter capable of applying
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weighted mean, median, maximum, and minimum filters
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parametrically using an arbitrary kernel.
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Args:
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img (nparray):
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The image, a 2-D array of floats, to which the filter is being applied.
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kernel (nparray):
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The kernel, a 2-D array of floats.
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kernel_center (nparray):
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The kernel center coordinate, a 1-D array with two elements.
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percentile_min (float):
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The lower bound of the histogram window used by the filter,
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from 0 to 1.
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percentile_max (float):
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The upper bound of the histogram window used by the filter,
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from 0 to 1.
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min_width (float):
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The minimum size of the histogram window bounds, in weight units.
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Must be greater than 0.
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Returns:
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(nparray): A filtered copy of the input image "img", a 2-D array of floats.
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"""
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# Converts an index tuple into a vector.
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def vec(x):
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return np.array(x)
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kernel_min = -kernel_center
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kernel_max = vec(kernel.shape) - kernel_center
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def weighted_histogram_filter_single(idx):
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idx = vec(idx)
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min_index = np.maximum(0, idx + kernel_min)
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max_index = np.minimum(vec(img.shape), idx + kernel_max)
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window_shape = max_index - min_index
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class WeightedElement:
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"""
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An element of the histogram, its weight
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and bounds.
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"""
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def __init__(self, value, weight):
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self.value: float = value
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self.weight: float = weight
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self.window_min: float = 0.0
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self.window_max: float = 1.0
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# Collect the values in the image as WeightedElements,
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# weighted by their corresponding kernel values.
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values = []
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for window_tup in np.ndindex(tuple(window_shape)):
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window_index = vec(window_tup)
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image_index = window_index + min_index
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centered_kernel_index = image_index - idx
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kernel_index = centered_kernel_index + kernel_center
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element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)])
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values.append(element)
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def sort_key(x: WeightedElement):
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return x.value
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values.sort(key=sort_key)
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# Calculate the height of the stack (sum)
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# and each sample's range they occupy in the stack
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sum = 0
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for i in range(len(values)):
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values[i].window_min = sum
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sum += values[i].weight
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values[i].window_max = sum
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# Calculate what range of this stack ("window")
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# we want to get the weighted average across.
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window_min = sum * percentile_min
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window_max = sum * percentile_max
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window_width = window_max - window_min
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# Ensure the window is within the stack and at least a certain size.
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if window_width < min_width:
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window_center = (window_min + window_max) / 2
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window_min = window_center - min_width / 2
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window_max = window_center + min_width / 2
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if window_max > sum:
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window_max = sum
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window_min = sum - min_width
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if window_min < 0:
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window_min = 0
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window_max = min_width
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value = 0
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value_weight = 0
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# Get the weighted average of all the samples
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# that overlap with the window, weighted
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# by the size of their overlap.
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for i in range(len(values)):
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if window_min >= values[i].window_max:
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continue
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if window_max <= values[i].window_min:
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break
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s = max(window_min, values[i].window_min)
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e = min(window_max, values[i].window_max)
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w = e - s
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value += values[i].value * w
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value_weight += w
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return value / value_weight if value_weight != 0 else 0
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img_out = img.copy()
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# Apply the kernel operation over each pixel.
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for index in np.ndindex(img.shape):
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img_out[index] = weighted_histogram_filter_single(index)
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return img_out
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def smoothstep(x):
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"""
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The smoothstep function, input should be clamped to 0-1 range.
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Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
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"""
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return x * x * (3 - 2 * x)
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def smootherstep(x):
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"""
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The smootherstep function, input should be clamped to 0-1 range.
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Turns a diagonal line (f(x) = x) into a sigmoid-like curve.
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"""
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return x * x * x * (x * (6 * x - 15) + 10)
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def get_gaussian_kernel(stddev_radius=1.0, max_radius=2):
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"""
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Creates a Gaussian kernel with thresholded edges.
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Args:
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stddev_radius (float):
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Standard deviation of the gaussian kernel, in pixels.
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max_radius (int):
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The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2.
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The kernel is thresholded so that any values one pixel beyond this radius
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is weighted at 0.
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Returns:
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(nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2))
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"""
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# Evaluates a 0-1 normalized gaussian function for a given square distance from the mean.
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def gaussian(sqr_mag):
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return math.exp(-sqr_mag / (stddev_radius * stddev_radius))
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# Helper function for converting a tuple to an array.
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def vec(x):
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return np.array(x)
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"""
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Since a gaussian is unbounded, we need to limit ourselves
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to a finite range.
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We taper the ends off at the end of that range so they equal zero
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while preserving the maximum value of 1 at the mean.
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"""
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zero_radius = max_radius + 1.0
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gauss_zero = gaussian(zero_radius * zero_radius)
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gauss_kernel_scale = 1 / (1 - gauss_zero)
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def gaussian_kernel_func(coordinate):
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x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0
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x = gaussian(x)
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x -= gauss_zero
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x *= gauss_kernel_scale
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x = max(0.0, x)
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return x
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size = max_radius * 2 + 1
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kernel_center = max_radius
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kernel = np.zeros((size, size))
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for index in np.ndindex(kernel.shape):
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kernel[index] = gaussian_kernel_func(vec(index) - kernel_center)
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return kernel, kernel_center
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# ------------------- Constants -------------------
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default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2)
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enabled_ui_label = "Soft inpainting"
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enabled_gen_param_label = "Soft inpainting enabled"
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enabled_el_id = "soft_inpainting_enabled"
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ui_labels = SoftInpaintingSettings(
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"Schedule bias",
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"Preservation strength",
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"Transition contrast boost",
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"Mask influence",
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"Difference threshold",
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"Difference contrast")
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ui_info = SoftInpaintingSettings(
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"Shifts when preservation of original content occurs during denoising.",
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"How strongly partially masked content should be preserved.",
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"Amplifies the contrast that may be lost in partially masked regions.",
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"How strongly the original mask should bias the difference threshold.",
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"How much an image region can change before the original pixels are not blended in anymore.",
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"How sharp the transition should be between blended and not blended.")
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gen_param_labels = SoftInpaintingSettings(
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"Soft inpainting schedule bias",
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"Soft inpainting preservation strength",
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"Soft inpainting transition contrast boost",
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"Soft inpainting mask influence",
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"Soft inpainting difference threshold",
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"Soft inpainting difference contrast")
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el_ids = SoftInpaintingSettings(
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"mask_blend_power",
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"mask_blend_scale",
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"inpaint_detail_preservation",
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"composite_mask_influence",
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"composite_difference_threshold",
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"composite_difference_contrast")
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# ------------------- Script -------------------
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class Script(scripts.Script):
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def __init__(self):
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self.section = "inpaint"
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self.masks_for_overlay = None
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self.overlay_images = None
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def title(self):
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return "Soft Inpainting"
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def show(self, is_img2img):
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return scripts.AlwaysVisible if is_img2img else False
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def ui(self, is_img2img):
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if not is_img2img:
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return
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with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled:
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with gr.Group():
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gr.Markdown(
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"""
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Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity.
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**High _Mask blur_** values are recommended!
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|
""")
|
|
|
|
power = \
|
|
gr.Slider(label=ui_labels.mask_blend_power,
|
|
info=ui_info.mask_blend_power,
|
|
minimum=0,
|
|
maximum=8,
|
|
step=0.1,
|
|
value=default.mask_blend_power,
|
|
elem_id=el_ids.mask_blend_power)
|
|
scale = \
|
|
gr.Slider(label=ui_labels.mask_blend_scale,
|
|
info=ui_info.mask_blend_scale,
|
|
minimum=0,
|
|
maximum=8,
|
|
step=0.05,
|
|
value=default.mask_blend_scale,
|
|
elem_id=el_ids.mask_blend_scale)
|
|
detail = \
|
|
gr.Slider(label=ui_labels.inpaint_detail_preservation,
|
|
info=ui_info.inpaint_detail_preservation,
|
|
minimum=1,
|
|
maximum=32,
|
|
step=0.5,
|
|
value=default.inpaint_detail_preservation,
|
|
elem_id=el_ids.inpaint_detail_preservation)
|
|
|
|
gr.Markdown(
|
|
"""
|
|
### Pixel Composite Settings
|
|
""")
|
|
|
|
mask_inf = \
|
|
gr.Slider(label=ui_labels.composite_mask_influence,
|
|
info=ui_info.composite_mask_influence,
|
|
minimum=0,
|
|
maximum=1,
|
|
step=0.05,
|
|
value=default.composite_mask_influence,
|
|
elem_id=el_ids.composite_mask_influence)
|
|
|
|
dif_thresh = \
|
|
gr.Slider(label=ui_labels.composite_difference_threshold,
|
|
info=ui_info.composite_difference_threshold,
|
|
minimum=0,
|
|
maximum=8,
|
|
step=0.25,
|
|
value=default.composite_difference_threshold,
|
|
elem_id=el_ids.composite_difference_threshold)
|
|
|
|
dif_contr = \
|
|
gr.Slider(label=ui_labels.composite_difference_contrast,
|
|
info=ui_info.composite_difference_contrast,
|
|
minimum=0,
|
|
maximum=8,
|
|
step=0.25,
|
|
value=default.composite_difference_contrast,
|
|
elem_id=el_ids.composite_difference_contrast)
|
|
|
|
with gr.Accordion("Help", open=False):
|
|
gr.Markdown(
|
|
f"""
|
|
### {ui_labels.mask_blend_power}
|
|
|
|
The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas).
|
|
This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step.
|
|
This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation.
|
|
|
|
- **Below 1**: Stronger preservation near the end (with low sigma)
|
|
- **1**: Balanced (proportional to sigma)
|
|
- **Above 1**: Stronger preservation in the beginning (with high sigma)
|
|
""")
|
|
gr.Markdown(
|
|
f"""
|
|
### {ui_labels.mask_blend_scale}
|
|
|
|
Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content.
|
|
This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength.
|
|
|
|
- **Low values**: Favors generated content.
|
|
- **High values**: Favors original content.
|
|
""")
|
|
gr.Markdown(
|
|
f"""
|
|
### {ui_labels.inpaint_detail_preservation}
|
|
|
|
This parameter controls how the original latent vectors and denoised latent vectors are interpolated.
|
|
With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors.
|
|
This can prevent the loss of contrast that occurs with linear interpolation.
|
|
|
|
- **Low values**: Softer blending, details may fade.
|
|
- **High values**: Stronger contrast, may over-saturate colors.
|
|
""")
|
|
|
|
gr.Markdown(
|
|
"""
|
|
## Pixel Composite Settings
|
|
|
|
Masks are generated based on how much a part of the image changed after denoising.
|
|
These masks are used to blend the original and final images together.
|
|
If the difference is low, the original pixels are used instead of the pixels returned by the inpainting process.
|
|
""")
|
|
|
|
gr.Markdown(
|
|
f"""
|
|
### {ui_labels.composite_mask_influence}
|
|
|
|
This parameter controls how much the mask should bias this sensitivity to difference.
|
|
|
|
- **0**: Ignore the mask, only consider differences in image content.
|
|
- **1**: Follow the mask closely despite image content changes.
|
|
""")
|
|
|
|
gr.Markdown(
|
|
f"""
|
|
### {ui_labels.composite_difference_threshold}
|
|
|
|
This value represents the difference at which the original pixels will have less than 50% opacity.
|
|
|
|
- **Low values**: Two images patches must be almost the same in order to retain original pixels.
|
|
- **High values**: Two images patches can be very different and still retain original pixels.
|
|
""")
|
|
|
|
gr.Markdown(
|
|
f"""
|
|
### {ui_labels.composite_difference_contrast}
|
|
|
|
This value represents the contrast between the opacity of the original and inpainted content.
|
|
|
|
- **Low values**: The blend will be more gradual and have longer transitions, but may cause ghosting.
|
|
- **High values**: Ghosting will be less common, but transitions may be very sudden.
|
|
""")
|
|
|
|
self.infotext_fields = [(soft_inpainting_enabled, enabled_gen_param_label),
|
|
(power, gen_param_labels.mask_blend_power),
|
|
(scale, gen_param_labels.mask_blend_scale),
|
|
(detail, gen_param_labels.inpaint_detail_preservation),
|
|
(mask_inf, gen_param_labels.composite_mask_influence),
|
|
(dif_thresh, gen_param_labels.composite_difference_threshold),
|
|
(dif_contr, gen_param_labels.composite_difference_contrast)]
|
|
|
|
self.paste_field_names = []
|
|
for _, field_name in self.infotext_fields:
|
|
self.paste_field_names.append(field_name)
|
|
|
|
return [soft_inpainting_enabled,
|
|
power,
|
|
scale,
|
|
detail,
|
|
mask_inf,
|
|
dif_thresh,
|
|
dif_contr]
|
|
|
|
def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr):
|
|
if not enabled:
|
|
return
|
|
|
|
if not processing_uses_inpainting(p):
|
|
return
|
|
|
|
# Shut off the rounding it normally does.
|
|
p.mask_round = False
|
|
|
|
settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
|
|
|
|
# p.extra_generation_params["Mask rounding"] = False
|
|
settings.add_generation_params(p.extra_generation_params)
|
|
|
|
def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf,
|
|
dif_thresh, dif_contr):
|
|
if not enabled:
|
|
return
|
|
|
|
if not processing_uses_inpainting(p):
|
|
return
|
|
|
|
if mba.is_final_blend:
|
|
mba.blended_latent = mba.current_latent
|
|
return
|
|
|
|
settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
|
|
|
|
# todo: Why is sigma 2D? Both values are the same.
|
|
mba.blended_latent = latent_blend(settings,
|
|
mba.init_latent,
|
|
mba.current_latent,
|
|
get_modified_nmask(settings, mba.nmask, mba.sigma[0]))
|
|
|
|
def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf,
|
|
dif_thresh, dif_contr):
|
|
if not enabled:
|
|
return
|
|
|
|
if not processing_uses_inpainting(p):
|
|
return
|
|
|
|
nmask = getattr(p, "nmask", None)
|
|
if nmask is None:
|
|
return
|
|
|
|
from modules import images
|
|
from modules.shared import opts
|
|
|
|
settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr)
|
|
|
|
# since the original code puts holes in the existing overlay images,
|
|
# we have to rebuild them.
|
|
self.overlay_images = []
|
|
for img in p.init_images:
|
|
|
|
image = images.flatten(img, opts.img2img_background_color)
|
|
|
|
if p.paste_to is None and p.resize_mode != 3:
|
|
image = images.resize_image(p.resize_mode, image, p.width, p.height)
|
|
|
|
self.overlay_images.append(image.convert('RGBA'))
|
|
|
|
if len(p.init_images) == 1:
|
|
self.overlay_images = self.overlay_images * p.batch_size
|
|
|
|
if getattr(ps.samples, 'already_decoded', False):
|
|
self.masks_for_overlay = apply_masks(settings=settings,
|
|
nmask=nmask,
|
|
overlay_images=self.overlay_images,
|
|
width=p.width,
|
|
height=p.height,
|
|
paste_to=p.paste_to)
|
|
else:
|
|
self.masks_for_overlay = apply_adaptive_masks(settings=settings,
|
|
nmask=nmask,
|
|
latent_orig=p.init_latent,
|
|
latent_processed=ps.samples,
|
|
overlay_images=self.overlay_images,
|
|
width=p.width,
|
|
height=p.height,
|
|
paste_to=p.paste_to)
|
|
|
|
def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale,
|
|
detail_preservation, mask_inf, dif_thresh, dif_contr):
|
|
if not enabled:
|
|
return
|
|
|
|
if not processing_uses_inpainting(p):
|
|
return
|
|
|
|
if self.masks_for_overlay is None:
|
|
return
|
|
|
|
if self.overlay_images is None:
|
|
return
|
|
|
|
ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index]
|
|
ppmo.overlay_image = self.overlay_images[ppmo.index]
|