Formatted soft_inpainting.

scripts/soft_inpainting.py

@@ -122,7 +122,7 @@ def get_modified_nmask(settings, nmask, sigma):
 
 
 def apply_adaptive_masks(
-        settings:SoftInpaintingSettings,
+        settings: SoftInpaintingSettings,
         nmask,
         latent_orig,
         latent_processed,
@@ -137,10 +137,10 @@ def apply_adaptive_masks(
     # TODO: Bias the blending according to the latent mask, add adjustable parameter for bias control.
     latent_mask = nmask[0].float()
     # 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) ** (settings.mask_blend_scale / 2))
-    mask_scalar = (0.5 * (1-settings.composite_mask_influence)
+    mask_scalar = 1 - (torch.clamp(latent_mask, min=0, max=1) ** (settings.mask_blend_scale / 2))
+    mask_scalar = (0.5 * (1 - settings.composite_mask_influence)
                    + mask_scalar * settings.composite_mask_influence)
-    mask_scalar = mask_scalar / (1.00001-mask_scalar)
+    mask_scalar = mask_scalar / (1.00001 - mask_scalar)
     mask_scalar = mask_scalar.cpu().numpy()
 
     latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
@@ -276,6 +276,7 @@ def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, pe
             An element of the histogram, its weight
             and bounds.
             """
+
             def __init__(self, value, weight):
                 self.value: float = value
                 self.weight: float = weight
@@ -355,6 +356,7 @@ def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, pe
 
     return img_out
 
+
 def smoothstep(x):
     """
     The smoothstep function, input should be clamped to 0-1 range.
@@ -362,6 +364,7 @@ def smoothstep(x):
     """
     return x * x * (3 - 2 * x)
 
+
 def smootherstep(x):
     """
     The smootherstep function, input should be clamped to 0-1 range.
@@ -385,6 +388,7 @@ def get_gaussian_kernel(stddev_radius=1.0, max_radius=2):
     Returns:
         (nparray, nparray): A kernel array (shape: (N, N)), its center coordinate (shape: (2))
     """
+
     # Evaluates a 0-1 normalized gaussian function for a given square distance from the mean.
     def gaussian(sqr_mag):
         return math.exp(-sqr_mag / (stddev_radius * stddev_radius))
@@ -656,7 +660,8 @@ class Script(scripts.Script):
         # p.extra_generation_params["Mask rounding"] = False
         settings.add_generation_params(p.extra_generation_params)
 
-    def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr):
+    def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf,
+                      dif_thresh, dif_contr):
         if not enabled:
             return
 
@@ -675,7 +680,8 @@ class Script(scripts.Script):
                                           mba.current_latent,
                                           get_modified_nmask(settings, mba.nmask, mba.sigma[0]))
 
-    def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr):
+    def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf,
+                    dif_thresh, dif_contr):
         if not enabled:
             return
 
@@ -723,8 +729,8 @@ class Script(scripts.Script):
                                                           height=p.height,
                                                           paste_to=p.paste_to)
 
-
-    def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr):
+    def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale,
+                                detail_preservation, mask_inf, dif_thresh, dif_contr):
         if not enabled:
             return