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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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263 lines
11 KiB
Python
263 lines
11 KiB
Python
class SoftInpaintingSettings:
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def __init__(self, mask_blend_power, mask_blend_scale, inpaint_detail_preservation):
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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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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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# ------------------- Methods -------------------
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def latent_blend(soft_inpainting, 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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one_minus_t = 1 - t
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# Linearly interpolate the image vectors.
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a_scaled = a * one_minus_t
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b_scaled = b * t
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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
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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).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).to(torch.float64).pow_(soft_inpainting.inpaint_detail_preservation) * one_minus_t
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b_magnitude = torch.norm(b, p=2, dim=1).to(torch.float64).pow_(soft_inpainting.inpaint_detail_preservation) * t
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desired_magnitude = a_magnitude
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desired_magnitude.add_(b_magnitude).pow_(1 / soft_inpainting.inpaint_detail_preservation)
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del a_magnitude, b_magnitude, one_minus_t
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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(soft_inpainting, nmask, sigma):
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"""
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Converts a negative mask representing the transparency of the original latent vectors being overlayed
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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 ** soft_inpainting.mask_blend_power) * soft_inpainting.mask_blend_scale)
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def generate_adaptive_masks(
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latent_orig,
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latent_processed,
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overlay_images,
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masks_for_overlay,
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width, height,
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paste_to):
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import torch
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import numpy as np
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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 = p.nmask[0].float().cpu()
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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) ** (p.mask_blend_scale / 2))
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# mask_scalar = mask_scalar / (1.00001-mask_scalar)
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# mask_scalar = mask_scalar.numpy()
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latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
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kernel, kernel_center = images.get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
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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 = images.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 = images.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 = 1 # * mask_scalar
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# converted_mask = converted_mask / half_weighted_distance
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converted_mask = 1 / (1 + converted_mask ** 2)
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converted_mask = images.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[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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# ------------------- Constants -------------------
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default = SoftInpaintingSettings(1, 0.5, 4)
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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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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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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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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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# ------------------- UI -------------------
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def gradio_ui():
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import gradio as gr
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from modules.ui_components import InputAccordion
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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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""")
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result = SoftInpaintingSettings(
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gr.Slider(label=ui_labels.mask_blend_power,
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info=ui_info.mask_blend_power,
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minimum=0,
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maximum=8,
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step=0.1,
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value=default.mask_blend_power,
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elem_id=el_ids.mask_blend_power),
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gr.Slider(label=ui_labels.mask_blend_scale,
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info=ui_info.mask_blend_scale,
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minimum=0,
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maximum=8,
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step=0.05,
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value=default.mask_blend_scale,
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elem_id=el_ids.mask_blend_scale),
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gr.Slider(label=ui_labels.inpaint_detail_preservation,
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info=ui_info.inpaint_detail_preservation,
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minimum=1,
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maximum=32,
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step=0.5,
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value=default.inpaint_detail_preservation,
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elem_id=el_ids.inpaint_detail_preservation))
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with gr.Accordion("Help", open=False):
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gr.Markdown(
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f"""
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### {ui_labels.mask_blend_power}
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The blending strength of original content is scaled proportionally with the decreasing noise level values at each step (sigmas).
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This ensures that the influence of the denoiser and original content preservation is roughly balanced at each step.
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This balance can be shifted using this parameter, controlling whether earlier or later steps have stronger preservation.
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- **Below 1**: Stronger preservation near the end (with low sigma)
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- **1**: Balanced (proportional to sigma)
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- **Above 1**: Stronger preservation in the beginning (with high sigma)
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""")
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gr.Markdown(
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f"""
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### {ui_labels.mask_blend_scale}
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Skews whether partially masked image regions should be more likely to preserve the original content or favor inpainted content.
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This may need to be adjusted depending on the {ui_labels.mask_blend_power}, CFG Scale, prompt and Denoising strength.
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- **Low values**: Favors generated content.
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- **High values**: Favors original content.
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""")
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gr.Markdown(
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f"""
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### {ui_labels.inpaint_detail_preservation}
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This parameter controls how the original latent vectors and denoised latent vectors are interpolated.
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With higher values, the magnitude of the resulting blended vector will be closer to the maximum of the two interpolated vectors.
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This can prevent the loss of contrast that occurs with linear interpolation.
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- **Low values**: Softer blending, details may fade.
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- **High values**: Stronger contrast, may over-saturate colors.
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""")
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return (
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[
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soft_inpainting_enabled,
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result.mask_blend_power,
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result.mask_blend_scale,
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result.inpaint_detail_preservation
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],
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[
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(soft_inpainting_enabled, enabled_gen_param_label),
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(result.mask_blend_power, gen_param_labels.mask_blend_power),
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(result.mask_blend_scale, gen_param_labels.mask_blend_scale),
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(result.inpaint_detail_preservation, gen_param_labels.inpaint_detail_preservation)
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]
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)
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