Cleaned up code, moved main code contributions into soft_inpainting.py

modules/processing.py

@@ -892,55 +892,13 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
 
                 # Generate the mask(s) based on similarity between the original and denoised latent vectors
                 if getattr(p, "image_mask", None) is not None and getattr(p, "soft_inpainting", None) is not None:
-                    # latent_mask = p.nmask[0].float().cpu()
+                    si.generate_adaptive_masks(latent_orig=p.init_latent,
-
+                                               latent_processed=samples_ddim,
-                    # convert the original mask into a form we use to scale distances for thresholding
+                                               overlay_images=p.overlay_images,
-                    # mask_scalar = 1-(torch.clamp(latent_mask, min=0, max=1) ** (p.mask_blend_scale / 2))
+                                               masks_for_overlay=p.masks_for_overlay,
-                    # mask_scalar = mask_scalar / (1.00001-mask_scalar)
+                                               width=p.width,
-                    # mask_scalar = mask_scalar.numpy()
+                                               height=p.height,
-
+                                               paste_to=p.paste_to)
-                    latent_orig = p.init_latent
-                    latent_proc = samples_ddim
-                    latent_distance = torch.norm(latent_proc - 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, p.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, p.width, p.height)
-                        converted_mask = 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 p.paste_to is not None:
-                            converted_mask = uncrop(converted_mask,
-                                                    (overlay_image.width, overlay_image.height),
-                                                    p.paste_to)
-
-                        p.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')))
-
-                        p.overlay_images[i] = image_masked.convert('RGBA')
 
                 x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim,
                                                      target_device=devices.cpu,

modules/sd_samplers_cfg_denoiser.py

@@ -94,76 +94,6 @@ class CFGDenoiser(torch.nn.Module):
         self.sampler.sampler_extra_args['uncond'] = uc
 
     def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
-        def latent_blend(a, b, t, one_minus_t=None):
-
-            """
-            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.
-            """
-            # NOTE: We use inplace operations wherever possible.
-
-            if one_minus_t is None:
-                one_minus_t = 1 - t
-
-            if self.soft_inpainting is None:
-                return a * one_minus_t + b * t
-
-            # Linearly interpolate the image vectors.
-            a_scaled = a * one_minus_t
-            b_scaled = b * t
-            image_interp = a_scaled
-            image_interp.add_(b_scaled)
-            result_type = image_interp.dtype
-            del a_scaled, b_scaled
-
-            # 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).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).to(torch.float64).pow_(self.soft_inpainting.inpaint_detail_preservation) * one_minus_t
-            b_magnitude = torch.norm(b, p=2, dim=1).to(torch.float64).pow_(self.soft_inpainting.inpaint_detail_preservation) * t
-            desired_magnitude = a_magnitude
-            desired_magnitude.add_(b_magnitude).pow_(1 / self.soft_inpainting.inpaint_detail_preservation)
-            del a_magnitude, b_magnitude, one_minus_t
-
-            # 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_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
-
-            image_interp_scaled = image_interp_scaled.to(result_type)
-            del result_type
-
-            return image_interp_scaled
-
-        def get_modified_nmask(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
-            """
-            if self.soft_inpainting is None:
-                return nmask
-
-            return torch.pow(nmask, (_sigma ** self.soft_inpainting.mask_blend_power) * self.soft_inpainting.mask_blend_scale)
 
         if state.interrupted or state.skipped:
             raise sd_samplers_common.InterruptedException
@@ -184,9 +114,12 @@ class CFGDenoiser(torch.nn.Module):
         # Blend in the original latents (before)
         if self.mask_before_denoising and self.mask is not None:
             if self.soft_inpainting is None:
-                x = latent_blend(self.init_latent, x, self.nmask, self.mask)
+                x = self.init_latent * self.mask + self.nmask * x
             else:
-                x = latent_blend(self.init_latent, x, get_modified_nmask(self.nmask, sigma))
+                x = si.latent_blend(self.soft_inpainting,
+                                    self.init_latent,
+                                    x,
+                                    si.get_modified_nmask(self.soft_inpainting, self.nmask, sigma))
 
         batch_size = len(conds_list)
         repeats = [len(conds_list[i]) for i in range(batch_size)]
@@ -290,9 +223,12 @@ class CFGDenoiser(torch.nn.Module):
         # Blend in the original latents (after)
         if not self.mask_before_denoising and self.mask is not None:
             if self.soft_inpainting is None:
-                denoised = latent_blend(self.init_latent, denoised, self.nmask, self.mask)
+                denoised = self.init_latent * self.mask + self.nmask * denoised
             else:
-                denoised = latent_blend(self.init_latent, denoised, get_modified_nmask(self.nmask, sigma))
+                denoised = si.latent_blend(self.soft_inpainting,
+                                           self.init_latent,
+                                           denoised,
+                                           si.get_modified_nmask(self.soft_inpainting, self.nmask, sigma))
 
         self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
 

modules/soft_inpainting.py

@@ -4,13 +4,6 @@ class SoftInpaintingSettings:
         self.mask_blend_scale = mask_blend_scale
         self.inpaint_detail_preservation = inpaint_detail_preservation
 
-    def get_paste_fields(self):
-        return [
-            (self.mask_blend_power, gen_param_labels.mask_blend_power),
-            (self.mask_blend_scale, gen_param_labels.mask_blend_scale),
-            (self.inpaint_detail_preservation, gen_param_labels.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
@@ -18,25 +11,169 @@ class SoftInpaintingSettings:
         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.
+
+    one_minus_t = 1 - t
+
+    # Linearly interpolate the image vectors.
+    a_scaled = a * one_minus_t
+    b_scaled = b * t
+    image_interp = a_scaled
+    image_interp.add_(b_scaled)
+    result_type = image_interp.dtype
+    del a_scaled, b_scaled
+
+    # 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).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).to(torch.float64).pow_(soft_inpainting.inpaint_detail_preservation) * one_minus_t
+    b_magnitude = torch.norm(b, p=2, dim=1).to(torch.float64).pow_(soft_inpainting.inpaint_detail_preservation) * t
+    desired_magnitude = a_magnitude
+    desired_magnitude.add_(b_magnitude).pow_(1 / soft_inpainting.inpaint_detail_preservation)
+    del a_magnitude, b_magnitude, one_minus_t
+
+    # 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 generate_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')
+
+
+# ------------------- 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"
 
-default = SoftInpaintingSettings(1, 0.5, 4)
+ui_labels = SoftInpaintingSettings(
-ui_labels = SoftInpaintingSettings("Schedule bias", "Preservation strength", "Transition contrast boost")
+    "Schedule bias",
+    "Preservation strength",
+    "Transition contrast boost")
 
 ui_info = SoftInpaintingSettings(
-    mask_blend_power="Shifts when preservation of original content occurs during denoising.",
+    "Shifts when preservation of original content occurs during denoising.",
-                     # "Below 1: Stronger preservation near the end (with low sigma)\n"
+    "How strongly partially masked content should be preserved.",
-                     # "1: Balanced (proportional to sigma)\n"
+    "Amplifies the contrast that may be lost in partially masked regions.")
-                     # "Above 1: Stronger preservation in the beginning (with high sigma)",
-    mask_blend_scale="How strongly partially masked content should be preserved.",
-                     # "Low values: Favors generated content.\n"
-                     # "High values: Favors original content.",
-    inpaint_detail_preservation="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")
+gen_param_labels = SoftInpaintingSettings(
-el_ids = SoftInpaintingSettings("mask_blend_power", "mask_blend_scale", "inpaint_detail_preservation")
+    "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():

modules/ui.py

@@ -683,13 +683,6 @@ def create_ui():
                             with FormRow():
                                 soft_inpainting = si.gradio_ui()
 
-
-                            """
-                                mask_blend_power = gr.Slider(label='Blending bias', minimum=0, maximum=8, step=0.1, value=1, elem_id="img2img_mask_blend_power")
-                                mask_blend_scale = gr.Slider(label='Blending preservation', minimum=0, maximum=8, step=0.05, value=0.5, elem_id="img2img_mask_blend_scale")
-                                inpaint_detail_preservation = gr.Slider(label='Blending contrast boost', minimum=1, maximum=32, step=0.5, value=4, elem_id="img2img_mask_blend_offset")
-                            """
-
                             with FormRow():
                                 inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")