From f593cbfec417a3ea40589fe64e7f8806c9a81e5a Mon Sep 17 00:00:00 2001 From: AngelBottomless Date: Sun, 3 Sep 2023 21:07:36 +0900 Subject: fallback if exif data was invalid --- modules/images.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) (limited to 'modules/images.py') diff --git a/modules/images.py b/modules/images.py index eb644733..8512a46e 100644 --- a/modules/images.py +++ b/modules/images.py @@ -718,7 +718,12 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]: geninfo = items.pop('parameters', None) if "exif" in items: - exif = piexif.load(items["exif"]) + exif_data = items["exif"] + try: + exif = piexif.load(exif_data) + except OSError: + # memory / exif was not valid so piexif tried to read from a file + exif = None exif_comment = (exif or {}).get("Exif", {}).get(piexif.ExifIFD.UserComment, b'') try: exif_comment = piexif.helper.UserComment.load(exif_comment) -- cgit v1.2.1 From 022639a145751d61db1c144e5e657aa6481e2bc0 Mon Sep 17 00:00:00 2001 From: JaredTherriault Date: Mon, 4 Sep 2023 17:37:48 -0700 Subject: Load comments from gif images to gather geninfo from gif outputs --- modules/images.py | 2 ++ 1 file changed, 2 insertions(+) (limited to 'modules/images.py') diff --git a/modules/images.py b/modules/images.py index eb644733..8c6e862f 100644 --- a/modules/images.py +++ b/modules/images.py @@ -728,6 +728,8 @@ def read_info_from_image(image: Image.Image) -> tuple[str | None, dict]: if exif_comment: items['exif comment'] = exif_comment geninfo = exif_comment + elif "comment" in items: # for gif + geninfo = items["comment"].decode('utf8', errors="ignore") for field in IGNORED_INFO_KEYS: items.pop(field, None) -- cgit v1.2.1 From 35d1c94549cf75e7e312372d90fee0acc2806426 Mon Sep 17 00:00:00 2001 From: w-e-w <40751091+w-e-w@users.noreply.github.com> Date: Wed, 6 Sep 2023 20:24:26 +0900 Subject: save_images_add_number_suffix --- modules/images.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) (limited to 'modules/images.py') diff --git a/modules/images.py b/modules/images.py index eb644733..10dcd9ab 100644 --- a/modules/images.py +++ b/modules/images.py @@ -661,7 +661,15 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name) - os.replace(temp_file_path, filename_without_extension + extension) + full_file_name = filename_without_extension + extension + if shared.opts.save_images_add_number_suffix and os.path.exists(full_file_name): + count = 1 + while True: + full_file_name = f"{filename_without_extension}_{count}{extension}" + if not os.path.exists(full_file_name): + break + count += 1 + os.replace(temp_file_path, full_file_name) fullfn_without_extension, extension = os.path.splitext(params.filename) if hasattr(os, 'statvfs'): -- cgit v1.2.1 From 3ca4655a18eb80cca5f806412f2cb2d56cc536e5 Mon Sep 17 00:00:00 2001 From: AUTOMATIC1111 <16777216c@gmail.com> Date: Sat, 9 Sep 2023 09:08:31 +0300 Subject: update for #12926 --- modules/images.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) (limited to 'modules/images.py') diff --git a/modules/images.py b/modules/images.py index 10dcd9ab..5cf3c825 100644 --- a/modules/images.py +++ b/modules/images.py @@ -661,15 +661,13 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i save_image_with_geninfo(image_to_save, info, temp_file_path, extension, existing_pnginfo=params.pnginfo, pnginfo_section_name=pnginfo_section_name) - full_file_name = filename_without_extension + extension - if shared.opts.save_images_add_number_suffix and os.path.exists(full_file_name): - count = 1 - while True: - full_file_name = f"{filename_without_extension}_{count}{extension}" - if not os.path.exists(full_file_name): - break - count += 1 - os.replace(temp_file_path, full_file_name) + filename = filename_without_extension + extension + if shared.opts.save_images_replace_action != "Replace": + n = 0 + while os.path.exists(filename): + n += 1 + filename = f"{filename_without_extension}-{n}{extension}" + os.replace(temp_file_path, filename) fullfn_without_extension, extension = os.path.splitext(params.filename) if hasattr(os, 'statvfs'): -- cgit v1.2.1 From ab63054f95a7b7dfb2f0ffa88e805c8d2f950605 Mon Sep 17 00:00:00 2001 From: AUTOMATIC1111 <16777216c@gmail.com> Date: Sat, 30 Sep 2023 09:34:50 +0300 Subject: write infotext to gif image as comment --- modules/images.py | 2 ++ 1 file changed, 2 insertions(+) (limited to 'modules/images.py') diff --git a/modules/images.py b/modules/images.py index 1e63118e..daf4eebe 100644 --- a/modules/images.py +++ b/modules/images.py @@ -561,6 +561,8 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p }) piexif.insert(exif_bytes, filename) + elif extension.lower() == ".gif": + image.save(filename, format=image_format, comment=geninfo) else: image.save(filename, format=image_format, quality=opts.jpeg_quality) -- cgit v1.2.1 From 609dea36ea919aa7db42fd4233c416a45c74578b Mon Sep 17 00:00:00 2001 From: CodeHatchling Date: Sat, 2 Dec 2023 18:56:49 -0700 Subject: Added utility functions related to processing masks. --- modules/images.py | 191 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 191 insertions(+) (limited to 'modules/images.py') diff --git a/modules/images.py b/modules/images.py index eb644733..b5a0cead 100644 --- a/modules/images.py +++ b/modules/images.py @@ -776,3 +776,194 @@ def flatten(img, bgcolor): img = background return img.convert('RGB') + + +def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0): + """ + Generalization convolution filter capable of applying + weighted mean, median, maximum, and minimum filters + parametrically using an arbitrary kernel. + + Args: + img (nparray): + The image, a 2-D array of floats, to which the filter is being applied. + kernel (nparray): + The kernel, a 2-D array of floats. + kernel_center (nparray): + The kernel center coordinate, a 1-D array with two elements. + percentile_min (float): + The lower bound of the histogram window used by the filter, + from 0 to 1. + percentile_max (float): + The upper bound of the histogram window used by the filter, + from 0 to 1. + min_width (float): + The minimum size of the histogram window bounds, in weight units. + Must be greater than 0. + + Returns: + (nparray): A filtered copy of the input image "img", a 2-D array of floats. + """ + + # Converts an index tuple into a vector. + def vec(x): + return np.array(x) + + kernel_min = -kernel_center + kernel_max = vec(kernel.shape) - kernel_center + + def weighted_histogram_filter_single(idx): + idx = vec(idx) + min_index = np.maximum(0, idx + kernel_min) + max_index = np.minimum(vec(img.shape), idx + kernel_max) + window_shape = max_index - min_index + + class WeightedElement: + """ + An element of the histogram, its weight + and bounds. + """ + def __init__(self, value, weight): + self.value: float = value + self.weight: float = weight + self.window_min: float = 0.0 + self.window_max: float = 1.0 + + # Collect the values in the image as WeightedElements, + # weighted by their corresponding kernel values. + values = [] + for window_tup in np.ndindex(tuple(window_shape)): + window_index = vec(window_tup) + image_index = window_index + min_index + centered_kernel_index = image_index - idx + kernel_index = centered_kernel_index + kernel_center + element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)]) + values.append(element) + + def sort_key(x: WeightedElement): + return x.value + + values.sort(key=sort_key) + + # Calculate the height of the stack (sum) + # and each sample's range they occupy in the stack + sum = 0 + for i in range(len(values)): + values[i].window_min = sum + sum += values[i].weight + values[i].window_max = sum + + # Calculate what range of this stack ("window") + # we want to get the weighted average across. + window_min = sum * percentile_min + window_max = sum * percentile_max + window_width = window_max - window_min + + # Ensure the window is within the stack and at least a certain size. + if window_width < min_width: + window_center = (window_min + window_max) / 2 + window_min = window_center - min_width / 2 + window_max = window_center + min_width / 2 + + if window_max > sum: + window_max = sum + window_min = sum - min_width + + if window_min < 0: + window_min = 0 + window_max = min_width + + value = 0 + value_weight = 0 + + # Get the weighted average of all the samples + # that overlap with the window, weighted + # by the size of their overlap. + for i in range(len(values)): + if window_min >= values[i].window_max: + continue + if window_max <= values[i].window_min: + break + + s = max(window_min, values[i].window_min) + e = min(window_max, values[i].window_max) + w = e - s + + value += values[i].value * w + value_weight += w + + return value / value_weight if value_weight != 0 else 0 + + img_out = img.copy() + + # Apply the kernel operation over each pixel. + for index in np.ndindex(img.shape): + img_out[index] = weighted_histogram_filter_single(index) + + return img_out + +def smoothstep(x): + """ + The smoothstep function, input should be clamped to 0-1 range. + Turns a diagonal line (f(x) = x) into a sigmoid-like curve. + """ + return x * x * (3 - 2 * x) + +def smootherstep(x): + """ + The smootherstep function, input should be clamped to 0-1 range. + Turns a diagonal line (f(x) = x) into a sigmoid-like curve. + """ + return x * x * x * (x * (6 * x - 15) + 10) + + +def get_gaussian_kernel(stddev_radius=1.0, max_radius=2): + """ + Creates a Gaussian kernel with thresholded edges. + + Args: + stddev_radius (float): + Standard deviation of the gaussian kernel, in pixels. + max_radius (int): + The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2. + The kernel is thresholded so that any values one pixel beyond this radius + is weighted at 0. + + 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)) + + # Helper function for converting a tuple to an array. + def vec(x): + return np.array(x) + + """ + Since a gaussian is unbounded, we need to limit ourselves + to a finite range. + We taper the ends off at the end of that range so they equal zero + while preserving the maximum value of 1 at the mean. + """ + zero_radius = max_radius + 1.0 + gauss_zero = gaussian(zero_radius * zero_radius) + gauss_kernel_scale = 1 / (1 - gauss_zero) + + def gaussian_kernel_func(coordinate): + x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0 + x = gaussian(x) + x -= gauss_zero + x /= gauss_kernel_scale + x = max(0.0, x) + return x + + size = max_radius * 2 + 1 + kernel_center = max_radius + kernel = np.zeros((size, size)) + + for index in np.ndindex(kernel.shape): + kernel[index] = gaussian_kernel_func(vec(index) - kernel_center) + + return kernel, kernel_center + -- cgit v1.2.1 From 28a2b5b4aab43424733039c31d910e8b8dd507cd Mon Sep 17 00:00:00 2001 From: CodeHatchling Date: Sun, 3 Dec 2023 14:20:20 -0700 Subject: Fixed a math mistake. --- modules/images.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'modules/images.py') diff --git a/modules/images.py b/modules/images.py index 6648097e..94953498 100644 --- a/modules/images.py +++ b/modules/images.py @@ -969,7 +969,7 @@ def get_gaussian_kernel(stddev_radius=1.0, max_radius=2): x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0 x = gaussian(x) x -= gauss_zero - x /= gauss_kernel_scale + x *= gauss_kernel_scale x = max(0.0, x) return x -- cgit v1.2.1 From 56604f08a18588e8e6b57d7c3f9c61d6624846f8 Mon Sep 17 00:00:00 2001 From: CodeHatchling Date: Thu, 7 Dec 2023 14:53:44 -0700 Subject: Moved image filters used by soft inpainting into soft_inpainting.py from images.py --- modules/images.py | 190 ------------------------------------------------------ 1 file changed, 190 deletions(-) (limited to 'modules/images.py') diff --git a/modules/images.py b/modules/images.py index 94953498..16f9ae7c 100644 --- a/modules/images.py +++ b/modules/images.py @@ -792,193 +792,3 @@ def flatten(img, bgcolor): return img.convert('RGB') - -def weighted_histogram_filter(img, kernel, kernel_center, percentile_min=0.0, percentile_max=1.0, min_width=1.0): - """ - Generalization convolution filter capable of applying - weighted mean, median, maximum, and minimum filters - parametrically using an arbitrary kernel. - - Args: - img (nparray): - The image, a 2-D array of floats, to which the filter is being applied. - kernel (nparray): - The kernel, a 2-D array of floats. - kernel_center (nparray): - The kernel center coordinate, a 1-D array with two elements. - percentile_min (float): - The lower bound of the histogram window used by the filter, - from 0 to 1. - percentile_max (float): - The upper bound of the histogram window used by the filter, - from 0 to 1. - min_width (float): - The minimum size of the histogram window bounds, in weight units. - Must be greater than 0. - - Returns: - (nparray): A filtered copy of the input image "img", a 2-D array of floats. - """ - - # Converts an index tuple into a vector. - def vec(x): - return np.array(x) - - kernel_min = -kernel_center - kernel_max = vec(kernel.shape) - kernel_center - - def weighted_histogram_filter_single(idx): - idx = vec(idx) - min_index = np.maximum(0, idx + kernel_min) - max_index = np.minimum(vec(img.shape), idx + kernel_max) - window_shape = max_index - min_index - - class WeightedElement: - """ - An element of the histogram, its weight - and bounds. - """ - def __init__(self, value, weight): - self.value: float = value - self.weight: float = weight - self.window_min: float = 0.0 - self.window_max: float = 1.0 - - # Collect the values in the image as WeightedElements, - # weighted by their corresponding kernel values. - values = [] - for window_tup in np.ndindex(tuple(window_shape)): - window_index = vec(window_tup) - image_index = window_index + min_index - centered_kernel_index = image_index - idx - kernel_index = centered_kernel_index + kernel_center - element = WeightedElement(img[tuple(image_index)], kernel[tuple(kernel_index)]) - values.append(element) - - def sort_key(x: WeightedElement): - return x.value - - values.sort(key=sort_key) - - # Calculate the height of the stack (sum) - # and each sample's range they occupy in the stack - sum = 0 - for i in range(len(values)): - values[i].window_min = sum - sum += values[i].weight - values[i].window_max = sum - - # Calculate what range of this stack ("window") - # we want to get the weighted average across. - window_min = sum * percentile_min - window_max = sum * percentile_max - window_width = window_max - window_min - - # Ensure the window is within the stack and at least a certain size. - if window_width < min_width: - window_center = (window_min + window_max) / 2 - window_min = window_center - min_width / 2 - window_max = window_center + min_width / 2 - - if window_max > sum: - window_max = sum - window_min = sum - min_width - - if window_min < 0: - window_min = 0 - window_max = min_width - - value = 0 - value_weight = 0 - - # Get the weighted average of all the samples - # that overlap with the window, weighted - # by the size of their overlap. - for i in range(len(values)): - if window_min >= values[i].window_max: - continue - if window_max <= values[i].window_min: - break - - s = max(window_min, values[i].window_min) - e = min(window_max, values[i].window_max) - w = e - s - - value += values[i].value * w - value_weight += w - - return value / value_weight if value_weight != 0 else 0 - - img_out = img.copy() - - # Apply the kernel operation over each pixel. - for index in np.ndindex(img.shape): - img_out[index] = weighted_histogram_filter_single(index) - - return img_out - -def smoothstep(x): - """ - The smoothstep function, input should be clamped to 0-1 range. - Turns a diagonal line (f(x) = x) into a sigmoid-like curve. - """ - return x * x * (3 - 2 * x) - -def smootherstep(x): - """ - The smootherstep function, input should be clamped to 0-1 range. - Turns a diagonal line (f(x) = x) into a sigmoid-like curve. - """ - return x * x * x * (x * (6 * x - 15) + 10) - - -def get_gaussian_kernel(stddev_radius=1.0, max_radius=2): - """ - Creates a Gaussian kernel with thresholded edges. - - Args: - stddev_radius (float): - Standard deviation of the gaussian kernel, in pixels. - max_radius (int): - The size of the filter kernel. The number of pixels is (max_radius*2+1) ** 2. - The kernel is thresholded so that any values one pixel beyond this radius - is weighted at 0. - - 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)) - - # Helper function for converting a tuple to an array. - def vec(x): - return np.array(x) - - """ - Since a gaussian is unbounded, we need to limit ourselves - to a finite range. - We taper the ends off at the end of that range so they equal zero - while preserving the maximum value of 1 at the mean. - """ - zero_radius = max_radius + 1.0 - gauss_zero = gaussian(zero_radius * zero_radius) - gauss_kernel_scale = 1 / (1 - gauss_zero) - - def gaussian_kernel_func(coordinate): - x = coordinate[0] ** 2.0 + coordinate[1] ** 2.0 - x = gaussian(x) - x -= gauss_zero - x *= gauss_kernel_scale - x = max(0.0, x) - return x - - size = max_radius * 2 + 1 - kernel_center = max_radius - kernel = np.zeros((size, size)) - - for index in np.ndindex(kernel.shape): - kernel[index] = gaussian_kernel_func(vec(index) - kernel_center) - - return kernel, kernel_center - -- cgit v1.2.1