diff --git a/modules/processing.py b/modules/processing.py index b40b1a40d..0b3603875 100644 --- a/modules/processing.py +++ b/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() - - # 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_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') + si.generate_adaptive_masks(latent_orig=p.init_latent, + latent_processed=samples_ddim, + overlay_images=p.overlay_images, + masks_for_overlay=p.masks_for_overlay, + width=p.width, + height=p.height, + paste_to=p.paste_to) x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py index 0ee0b7dde..a700e6922 100644 --- a/modules/sd_samplers_cfg_denoiser.py +++ b/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) diff --git a/modules/soft_inpainting.py b/modules/soft_inpainting.py index 259c36ec8..b81c8dd95 100644 --- a/modules/soft_inpainting.py +++ b/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("Schedule bias", "Preservation strength", "Transition contrast boost") +ui_labels = SoftInpaintingSettings( + "Schedule bias", + "Preservation strength", + "Transition contrast boost") ui_info = SoftInpaintingSettings( - mask_blend_power="Shifts when preservation of original content occurs during denoising.", - # "Below 1: Stronger preservation near the end (with low sigma)\n" - # "1: Balanced (proportional to sigma)\n" - # "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.") + "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") +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(): diff --git a/modules/ui.py b/modules/ui.py index 0e4fb17aa..4f1265a3e 100644 --- a/modules/ui.py +++ b/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")