diff options
| -rw-r--r-- | scripts/soft_inpainting.py | 26 |
1 files changed, 16 insertions, 10 deletions
diff --git a/scripts/soft_inpainting.py b/scripts/soft_inpainting.py index f10a1e56..d9024344 100644 --- a/scripts/soft_inpainting.py +++ b/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) @@ -152,9 +152,9 @@ def apply_adaptive_masks( for i, (distance_map, overlay_image) in enumerate(zip(latent_distance, overlay_images)): converted_mask = distance_map.float().cpu().numpy() converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, - percentile_min=0.9, percentile_max=1, min_width=1) + percentile_min=0.9, percentile_max=1, min_width=1) converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, - percentile_min=0.25, percentile_max=0.75, min_width=1) + percentile_min=0.25, percentile_max=0.75, min_width=1) # The distance at which opacity of original decreases to 50% half_weighted_distance = settings.composite_difference_threshold * mask_scalar @@ -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 |