Re-implemented soft inpainting via a script. Also fixed some mistakes with the previous hooks, removed unnecessary formatting changes, removed code that I had forgotten to.

4 files changed, 413 insertions, 323 deletions

diff --git a/modules/processing.py b/modules/processing.py
index 5a1a90af..f8d85bdf 100644
--- a/modules/processing.py
+++ b/modules/processing.py
@@ -879,14 +879,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if p.scripts is not None:

                 ps = scripts.PostSampleArgs(samples_ddim)

                 p.scripts.post_sample(p, ps)

-                samples_ddim = pp.samples

+                samples_ddim = ps.samples

 

             if getattr(samples_ddim, 'already_decoded', False):

                 x_samples_ddim = samples_ddim

             else:

                 if opts.sd_vae_decode_method != 'Full':

                     p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method

-

                 x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)

 

             x_samples_ddim = torch.stack(x_samples_ddim).float()

@@ -944,7 +943,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 if p.scripts is not None:

                     ppmo = scripts.PostProcessMaskOverlayArgs(i, mask_for_overlay, overlay_image)

                     p.scripts.postprocess_maskoverlay(p, ppmo)

-                    mask_for_overlay, overlay_image = pp.mask_for_overlay, pp.overlay_image

+                    mask_for_overlay, overlay_image = ppmo.mask_for_overlay, ppmo.overlay_image

 

                 if p.color_corrections is not None and i < len(p.color_corrections):

                     if save_samples and opts.save_images_before_color_correction:

@@ -959,7 +958,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
                 original_denoised_image = image.copy()

 

                 if p.paste_to is not None:

-                    original_denoised_image = uncrop(original_denoised_image, (p.overlay_image.width, p.overlay_image.height), p.paste_to)

+                    original_denoised_image = uncrop(original_denoised_image, (overlay_image.width, overlay_image.height), p.paste_to)

 

                 image = apply_overlay(image, p.paste_to, overlay_image)

 

@@ -1512,9 +1511,6 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
             if self.overlay_images is not None:

                 self.overlay_images = self.overlay_images * self.batch_size

 

-            if self.masks_for_overlay is not None:

-                self.masks_for_overlay = self.masks_for_overlay * self.batch_size

-

             if self.color_corrections is not None and len(self.color_corrections) == 1:

                 self.color_corrections = self.color_corrections * self.batch_size

 

@@ -1565,14 +1561,15 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
 

         samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)

 

-        blended_samples = samples * self.nmask + self.init_latent * self.mask

+        if self.mask is not None:

+            blended_samples = samples * self.nmask + self.init_latent * self.mask

 

-        if self.scripts is not None:

-            mba = scripts.MaskBlendArgs(self, samples, self.nmask, self.init_latent, self.mask, blended_samples, sigma=None, is_final_blend=True)

-            self.scripts.on_mask_blend(self, mba)

-            blended_samples = mba.blended_latent

+            if self.scripts is not None:

+                mba = scripts.MaskBlendArgs(samples, self.nmask, self.init_latent, self.mask, blended_samples)

+                self.scripts.on_mask_blend(self, mba)

+                blended_samples = mba.blended_latent

 

-        samples = blended_samples

+            samples = blended_samples

 

         del x

         devices.torch_gc()

diff --git a/modules/scripts.py b/modules/scripts.py
index 92a07c56..b6fcf96e 100644
--- a/modules/scripts.py
+++ b/modules/scripts.py
@@ -12,12 +12,12 @@ from modules import shared, paths, script_callbacks, extensions, script_loading,
 AlwaysVisible = object()

 

 class MaskBlendArgs:

-    def __init__(self, current_latent, nmask, init_latent, mask, blended_samples, denoiser=None, sigma=None):

+    def __init__(self, current_latent, nmask, init_latent, mask, blended_latent, denoiser=None, sigma=None):

         self.current_latent = current_latent

         self.nmask = nmask

         self.init_latent = init_latent

         self.mask = mask

-        self.blended_samples = blended_samples

+        self.blended_latent = blended_latent

 

         self.denoiser = denoiser

         self.is_final_blend = denoiser is None

diff --git a/modules/soft_inpainting.py b/modules/soft_inpainting.py
deleted file mode 100644
index b36ac8fa..00000000
--- a/modules/soft_inpainting.py
+++ /dev/null
@@ -1,308 +0,0 @@
-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
-    # todo: Why is sigma 2D? Both values are the same.
-    return torch.pow(nmask, (sigma[0] ** soft_inpainting.mask_blend_power) * soft_inpainting.mask_blend_scale)
-
-
-def apply_adaptive_masks(
-        latent_orig,
-        latent_processed,
-        overlay_images,
-        masks_for_overlay,
-        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)
-
-    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[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')
-
-def apply_masks(
-        soft_inpainting,
-        nmask,
-        overlay_images,
-        masks_for_overlay,
-        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)
-
-    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')
-
-
-# ------------------- 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")
-
-
-# ------------------- UI -------------------
-
-
-def gradio_ui():
-    import gradio as gr
-    from modules.ui_components import InputAccordion
-
-    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.
-                    """)
-
-    return (
-        [
-            soft_inpainting_enabled,
-            result.mask_blend_power,
-            result.mask_blend_scale,
-            result.inpaint_detail_preservation
-        ],
-        [
-            (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)
-        ]
-    )
diff --git a/scripts/soft_inpainting.py b/scripts/soft_inpainting.py
new file mode 100644
index 00000000..47e0269b
--- /dev/null
+++ b/scripts/soft_inpainting.py
@@ -0,0 +1,401 @@
+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]
\ No newline at end of file