mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
401 lines
17 KiB
Python
401 lines
17 KiB
Python
|
import gradio as gr
|
||
|
|
from modules.ui_components import InputAccordion
|
||
|
|
import modules.scripts as scripts
|
||
|
|
|
||
|
|
|
||
|
|
class SoftInpaintingSettings:
|
||
|
|
def __init__(self, mask_blend_power, mask_blend_scale, inpaint_detail_preservation):
|
||
|
|
self.mask_blend_power = mask_blend_power
|
||
|
|
self.mask_blend_scale = mask_blend_scale
|
||
|
|
self.inpaint_detail_preservation = inpaint_detail_preservation
|
||
|
|
|
||
|
|
def add_generation_params(self, dest):
|
||
|
|
dest[enabled_gen_param_label] = True
|
||
|
|
dest[gen_param_labels.mask_blend_power] = self.mask_blend_power
|
||
|
|
dest[gen_param_labels.mask_blend_scale] = self.mask_blend_scale
|
||
|
|
dest[gen_param_labels.inpaint_detail_preservation] = self.inpaint_detail_preservation
|
||
|
|
|
||
|
|
|
||
|
|
# ------------------- Methods -------------------
|
||
|
|
|
||
|
|
|
||
|
|
def latent_blend(soft_inpainting, a, b, t):
|
||
|
|
"""
|
||
|
|
Interpolates two latent image representations according to the parameter t,
|
||
|
|
where the interpolated vectors' magnitudes are also interpolated separately.
|
||
|
|
The "detail_preservation" factor biases the magnitude interpolation towards
|
||
|
|
the larger of the two magnitudes.
|
||
|
|
"""
|
||
|
|
import torch
|
||
|
|
|
||
|
|
# NOTE: We use inplace operations wherever possible.
|
||
|
|
|
||
|
|
# [4][w][h] to [1][4][w][h]
|
||
|
|
t2 = t.unsqueeze(0)
|
||
|
|
# [4][w][h] to [1][1][w][h] - the [4] seem redundant.
|
||
|
|
t3 = t[0].unsqueeze(0).unsqueeze(0)
|
||
|
|
|
||
|
|
one_minus_t2 = 1 - t2
|
||
|
|
one_minus_t3 = 1 - t3
|
||
|
|
|
||
|
|
# Linearly interpolate the image vectors.
|
||
|
|
a_scaled = a * one_minus_t2
|
||
|
|
b_scaled = b * t2
|
||
|
|
image_interp = a_scaled
|
||
|
|
image_interp.add_(b_scaled)
|
||
|
|
result_type = image_interp.dtype
|
||
|
|
del a_scaled, b_scaled, t2, one_minus_t2
|
||
|
|
|
||
|
|
# Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
|
||
|
|
# 64-bit operations are used here to allow large exponents.
|
||
|
|
current_magnitude = torch.norm(image_interp, p=2, dim=1, keepdim=True).to(torch.float64).add_(0.00001)
|
||
|
|
|
||
|
|
# Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
|
||
|
|
a_magnitude = torch.norm(a, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
|
||
|
|
soft_inpainting.inpaint_detail_preservation) * one_minus_t3
|
||
|
|
b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_(
|
||
|
|
soft_inpainting.inpaint_detail_preservation) * t3
|
||
|
|
desired_magnitude = a_magnitude
|
||
|
|
desired_magnitude.add_(b_magnitude).pow_(1 / soft_inpainting.inpaint_detail_preservation)
|
||
|
|
del a_magnitude, b_magnitude, t3, one_minus_t3
|
||
|
|
|
||
|
|
# Change the linearly interpolated image vectors' magnitudes to the value we want.
|
||
|
|
# This is the last 64-bit operation.
|
||
|
|
image_interp_scaling_factor = desired_magnitude
|
||
|
|
image_interp_scaling_factor.div_(current_magnitude)
|
||
|
|
image_interp_scaling_factor = image_interp_scaling_factor.to(result_type)
|
||
|
|
image_interp_scaled = image_interp
|
||
|
|
image_interp_scaled.mul_(image_interp_scaling_factor)
|
||
|
|
del current_magnitude
|
||
|
|
del desired_magnitude
|
||
|
|
del image_interp
|
||
|
|
del image_interp_scaling_factor
|
||
|
|
del result_type
|
||
|
|
|
||
|
|
return image_interp_scaled
|
||
|
|
|
||
|
|
|
||
|
|
def get_modified_nmask(soft_inpainting, nmask, sigma):
|
||
|
|
"""
|
||
|
|
Converts a negative mask representing the transparency of the original latent vectors being overlayed
|
||
|
|
to a mask that is scaled according to the denoising strength for this step.
|
||
|
|
|
||
|
|
Where:
|
||
|
|
0 = fully opaque, infinite density, fully masked
|
||
|
|
1 = fully transparent, zero density, fully unmasked
|
||
|
|
|
||
|
|
We bring this transparency to a power, as this allows one to simulate N number of blending operations
|
||
|
|
where N can be any positive real value. Using this one can control the balance of influence between
|
||
|
|
the denoiser and the original latents according to the sigma value.
|
||
|
|
|
||
|
|
NOTE: "mask" is not used
|
||
|
|
"""
|
||
|
|
import torch
|
||
|
|
return torch.pow(nmask, (sigma ** soft_inpainting.mask_blend_power) * soft_inpainting.mask_blend_scale)
|
||
|
|
|
||
|
|
|
||
|
|
def apply_adaptive_masks(
|
||
|
|
latent_orig,
|
||
|
|
latent_processed,
|
||
|
|
overlay_images,
|
||
|
|
width, height,
|
||
|
|
paste_to):
|
||
|
|
import torch
|
||
|
|
import numpy as np
|
||
|
|
import modules.processing as proc
|
||
|
|
import modules.images as images
|
||
|
|
from PIL import Image, ImageOps, ImageFilter
|
||
|
|
|
||
|
|
# TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control.
|
||
|
|
# latent_mask = p.nmask[0].float().cpu()
|
||
|
|
# convert the original mask into a form we use to scale distances for thresholding
|
||
|
|
# mask_scalar = 1-(torch.clamp(latent_mask, min=0, max=1) ** (p.mask_blend_scale / 2))
|
||
|
|
# mask_scalar = mask_scalar / (1.00001-mask_scalar)
|
||
|
|
# mask_scalar = mask_scalar.numpy()
|
||
|
|
|
||
|
|
latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
|
||
|
|
|
||
|
|
kernel, kernel_center = images.get_gaussian_kernel(stddev_radius=1.5, max_radius=2)
|
||
|
|
|
||
|
|
masks_for_overlay = []
|
||
|
|
|
||
|
|
for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)):
|
||
|
|
converted_mask = distance_map.float().cpu().numpy()
|
||
|
|
converted_mask = images.weighted_histogram_filter(converted_mask, kernel, kernel_center,
|
||
|
|
percentile_min=0.9, percentile_max=1, min_width=1)
|
||
|
|
converted_mask = images.weighted_histogram_filter(converted_mask, kernel, kernel_center,
|
||
|
|
percentile_min=0.25, percentile_max=0.75, min_width=1)
|
||
|
|
|
||
|
|
# The distance at which opacity of original decreases to 50%
|
||
|
|
# half_weighted_distance = 1 # * mask_scalar
|
||
|
|
# converted_mask = converted_mask / half_weighted_distance
|
||
|
|
|
||
|
|
converted_mask = 1 / (1 + converted_mask ** 2)
|
||
|
|
converted_mask = images.smootherstep(converted_mask)
|
||
|
|
converted_mask = 1 - converted_mask
|
||
|
|
converted_mask = 255. * converted_mask
|
||
|
|
converted_mask = converted_mask.astype(np.uint8)
|
||
|
|
converted_mask = Image.fromarray(converted_mask)
|
||
|
|
converted_mask = images.resize_image(2, converted_mask, width, height)
|
||
|
|
converted_mask = proc.create_binary_mask(converted_mask, round=False)
|
||
|
|
|
||
|
|
# Remove aliasing artifacts using a gaussian blur.
|
||
|
|
converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
|
||
|
|
|
||
|
|
# Expand the mask to fit the whole image if needed.
|
||
|
|
if paste_to is not None:
|
||
|
|
converted_mask = proc.uncrop(converted_mask,
|
||
|
|
(overlay_image.width, overlay_image.height),
|
||
|
|
paste_to)
|
||
|
|
|
||
|
|
masks_for_overlay.append(converted_mask)
|
||
|
|
|
||
|
|
image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
|
||
|
|
image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
|
||
|
|
mask=ImageOps.invert(converted_mask.convert('L')))
|
||
|
|
|
||
|
|
overlay_images[i] = image_masked.convert('RGBA')
|
||
|
|
|
||
|
|
return masks_for_overlay
|
||
|
|
|
||
|
|
|
||
|
|
def apply_masks(
|
||
|
|
soft_inpainting,
|
||
|
|
nmask,
|
||
|
|
overlay_images,
|
||
|
|
width, height,
|
||
|
|
paste_to):
|
||
|
|
import torch
|
||
|
|
import numpy as np
|
||
|
|
import modules.processing as proc
|
||
|
|
import modules.images as images
|
||
|
|
from PIL import Image, ImageOps, ImageFilter
|
||
|
|
|
||
|
|
converted_mask = nmask[0].float()
|
||
|
|
converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(soft_inpainting.mask_blend_scale / 2)
|
||
|
|
converted_mask = 255. * converted_mask
|
||
|
|
converted_mask = converted_mask.cpu().numpy().astype(np.uint8)
|
||
|
|
converted_mask = Image.fromarray(converted_mask)
|
||
|
|
converted_mask = images.resize_image(2, converted_mask, width, height)
|
||
|
|
converted_mask = proc.create_binary_mask(converted_mask, round=False)
|
||
|
|
|
||
|
|
# Remove aliasing artifacts using a gaussian blur.
|
||
|
|
converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4))
|
||
|
|
|
||
|
|
# Expand the mask to fit the whole image if needed.
|
||
|
|
if paste_to is not None:
|
||
|
|
converted_mask = proc.uncrop(converted_mask,
|
||
|
|
(width, height),
|
||
|
|
paste_to)
|
||
|
|
|
||
|
|
masks_for_overlay = []
|
||
|
|
|
||
|
|
for i, overlay_image in enumerate(overlay_images):
|
||
|
|
masks_for_overlay[i] = converted_mask
|
||
|
|
|
||
|
|
image_masked = Image.new('RGBa', (overlay_image.width, overlay_image.height))
|
||
|
|
image_masked.paste(overlay_image.convert("RGBA").convert("RGBa"),
|
||
|
|
mask=ImageOps.invert(converted_mask.convert('L')))
|
||
|
|
|
||
|
|
overlay_images[i] = image_masked.convert('RGBA')
|
||
|
|
|
||
|
|
return masks_for_overlay
|
||
|
|
|
||
|
|
|
||
|
|
# ------------------- Constants -------------------
|
||
|
|
|
||
|
|
|
||
|
|
default = SoftInpaintingSettings(1, 0.5, 4)
|
||
|
|
|
||
|
|
enabled_ui_label = "Soft inpainting"
|
||
|
|
enabled_gen_param_label = "Soft inpainting enabled"
|
||
|
|
enabled_el_id = "soft_inpainting_enabled"
|
||
|
|
|
||
|
|
ui_labels = SoftInpaintingSettings(
|
||
|
|
"Schedule bias",
|
||
|
|
"Preservation strength",
|
||
|
|
"Transition contrast boost")
|
||
|
|
|
||
|
|
ui_info = SoftInpaintingSettings(
|
||
|
|
"Shifts when preservation of original content occurs during denoising.",
|
||
|
|
"How strongly partially masked content should be preserved.",
|
||
|
|
"Amplifies the contrast that may be lost in partially masked regions.")
|
||
|
|
|
||
|
|
gen_param_labels = SoftInpaintingSettings(
|
||
|
|
"Soft inpainting schedule bias",
|
||
|
|
"Soft inpainting preservation strength",
|
||
|
|
"Soft inpainting transition contrast boost")
|
||
|
|
|
||
|
|
el_ids = SoftInpaintingSettings(
|
||
|
|
"mask_blend_power",
|
||
|
|
"mask_blend_scale",
|
||
|
|
"inpaint_detail_preservation")
|
||
|
|
|
||
|
|
|
||
|
|
class Script(scripts.Script):
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
self.masks_for_overlay = None
|
||
|
|
self.overlay_images = None
|
||
|
|
|
||
|
|
def title(self):
|
||
|
|
return "Soft Inpainting"
|
||
|
|
|
||
|
|
def show(self, is_img2img):
|
||
|
|
return scripts.AlwaysVisible if is_img2img else False
|
||
|
|
|
||
|
|
def ui(self, is_img2img):
|
||
|
|
if not is_img2img:
|
||
|
|
return
|
||
|
|
|
||
|
|
with InputAccordion(False, label=enabled_ui_label, elem_id=enabled_el_id) as soft_inpainting_enabled:
|
||
|
|
with gr.Group():
|
||
|
|
gr.Markdown(
|
||
|
|
"""
|
||
|
|
Soft inpainting allows you to **seamlessly blend original content with inpainted content** according to the mask opacity.
|
||
|
|
**High _Mask blur_** values are recommended!
|
||
|
|
""")
|
||
|
|
|
||
|
|
result = SoftInpaintingSettings(
|
||
|
|
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),
|
||
|
|
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),
|
||
|
|
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))
|
||
|
|
|
||
|
|
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.
|
||
|
|
""")
|
||
|
|
|
||
|
|
self.infotext_fields = [(soft_inpainting_enabled, enabled_gen_param_label),
|
||
|
|
(result.mask_blend_power, gen_param_labels.mask_blend_power),
|
||
|
|
(result.mask_blend_scale, gen_param_labels.mask_blend_scale),
|
||
|
|
(result.inpaint_detail_preservation, gen_param_labels.inpaint_detail_preservation)]
|
||
|
|
|
||
|
|
self.paste_field_names = []
|
||
|
|
for _, field_name in self.infotext_fields:
|
||
|
|
self.paste_field_names.append(field_name)
|
||
|
|
|
||
|
|
return [soft_inpainting_enabled,
|
||
|
|
result.mask_blend_power,
|
||
|
|
result.mask_blend_scale,
|
||
|
|
result.inpaint_detail_preservation]
|
||
|
|
|
||
|
|
def process(self, p, enabled, power, scale, detail_preservation):
|
||
|
|
if not enabled:
|
||
|
|
return
|
||
|
|
|
||
|
|
# Shut off the rounding it normally does.
|
||
|
|
p.mask_round = False
|
||
|
|
|
||
|
|
settings = SoftInpaintingSettings(power, scale, detail_preservation)
|
||
|
|
|
||
|
|
# 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):
|
||
|
|
if not enabled:
|
||
|
|
return
|
||
|
|
|
||
|
|
if mba.sigma is None:
|
||
|
|
mba.blended_latent = mba.current_latent
|
||
|
|
return
|
||
|
|
|
||
|
|
settings = SoftInpaintingSettings(power, scale, detail_preservation)
|
||
|
|
|
||
|
|
# 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):
|
||
|
|
if not enabled:
|
||
|
|
return
|
||
|
|
|
||
|
|
settings = SoftInpaintingSettings(power, scale, detail_preservation)
|
||
|
|
|
||
|
|
from modules import images
|
||
|
|
from modules.shared import opts
|
||
|
|
|
||
|
|
# 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 getattr(ps.samples, 'already_decoded', False):
|
||
|
|
self.masks_for_overlay = apply_masks(soft_inpainting=settings,
|
||
|
|
nmask=p.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(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):
|
||
|
|
if not enabled:
|
||
|
|
return
|
||
|
|
|
||
|
|
ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index]
|
||
|
|
ppmo.overlay_image = self.overlay_images[ppmo.index]
|