s #13
s #13
Conversation
WalkthroughThe recent updates bring a substantial overhaul to the stable diffusion web UI, focusing on enhanced functionality, performance improvements, and bug fixes. Key features include support for Stable Diffusion 3, new sampling methods, and refined memory management. The updates streamline operations in neural network models, introduce features like zooming and panning for better user experience, and improve error handling throughout the system, ensuring a smoother workflow for users. Changes
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| const modalImage = gradioApp().getElementById("modalImage"); | ||
| if (modalImage && modalImage.offsetParent) { | ||
| let currentButton = selected_gallery_button(); | ||
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| if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { | ||
| let preview = gradioApp().querySelectorAll('.livePreview > img'); | ||
| if (opts.js_live_preview_in_modal_lightbox && preview.length > 0) { | ||
| // show preview image if available | ||
| modalImage.src = preview[preview.length - 1].src; | ||
| } else if (currentButton?.children?.length > 0 && modalImage.src != currentButton.children[0].src) { | ||
| modalImage.src = currentButton.children[0].src; | ||
| if (modalImage.style.display === 'none') { | ||
| modal.style.setProperty('background-image', `url(${modalImage.src})`) | ||
| const modal = gradioApp().getElementById("lightboxModal"); | ||
| modal.style.setProperty('background-image', `url(${modalImage.src})`); |
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The logic for updating the background image based on the presence of a preview image or the selected gallery button's child image is sound. However, consider extracting this logic into a separate function for clarity and reusability, especially since similar patterns appear in other parts of the code.
function updateModalBackgroundImage(modalImage, sourceImage) {
if (modalImage.style.display === 'none') {
const modal = gradioApp().getElementById("lightboxModal");
modal.style.setProperty('background-image', `url(${sourceImage.src})`);
}
}
javascript/imageviewer.js
Outdated
| var result = -1; | ||
| galleryButtons.forEach(function(v, i) { | ||
| if (v == currentButton) { | ||
| result = i | ||
| result = i; | ||
| } | ||
| }) | ||
| }); | ||
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| if (result != -1) { | ||
| nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)] | ||
| nextButton.click() | ||
| var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]; | ||
| nextButton.click(); | ||
| const modalImage = gradioApp().getElementById("modalImage"); | ||
| const modal = gradioApp().getElementById("lightboxModal"); | ||
| modalImage.src = nextButton.children[0].src; | ||
| if (modalImage.style.display === 'none') { | ||
| modal.style.setProperty('background-image', `url(${modalImage.src})`) | ||
| modal.style.setProperty('background-image', `url(${modalImage.src})`); | ||
| } | ||
| setTimeout(function() { | ||
| modal.focus() | ||
| }, 10) | ||
| modal.focus(); | ||
| }, 10); |
There was a problem hiding this comment.
The modalImageSwitch function correctly implements the logic for switching images in the modal. However, the use of var for variable declarations is outdated. Consider using let or const for block-scoped variables to improve code maintainability and prevent potential issues with variable hoisting.
- var galleryButtons = all_gallery_buttons();
+ let galleryButtons = all_gallery_buttons();Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| var result = -1; | |
| galleryButtons.forEach(function(v, i) { | |
| if (v == currentButton) { | |
| result = i | |
| result = i; | |
| } | |
| }) | |
| }); | |
| if (result != -1) { | |
| nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)] | |
| nextButton.click() | |
| var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]; | |
| nextButton.click(); | |
| const modalImage = gradioApp().getElementById("modalImage"); | |
| const modal = gradioApp().getElementById("lightboxModal"); | |
| modalImage.src = nextButton.children[0].src; | |
| if (modalImage.style.display === 'none') { | |
| modal.style.setProperty('background-image', `url(${modalImage.src})`) | |
| modal.style.setProperty('background-image', `url(${modalImage.src})`); | |
| } | |
| setTimeout(function() { | |
| modal.focus() | |
| }, 10) | |
| modal.focus(); | |
| }, 10); | |
| let result = -1; | |
| galleryButtons.forEach(function(v, i) { | |
| if (v == currentButton) { | |
| result = i; | |
| } | |
| }); | |
| if (result != -1) { | |
| var nextButton = galleryButtons[negmod((result + offset), galleryButtons.length)]; | |
| nextButton.click(); | |
| const modalImage = gradioApp().getElementById("modalImage"); | |
| const modal = gradioApp().getElementById("lightboxModal"); | |
| modalImage.src = nextButton.children[0].src; | |
| if (modalImage.style.display === 'none') { | |
| modal.style.setProperty('background-image', `url(${modalImage.src})`); | |
| } | |
| setTimeout(function() { | |
| modal.focus(); | |
| }, 10); |
| function updateTooltip(element) { | ||
| if (element.title) return; // already has a title | ||
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| let text = element.textContent; | ||
| let tooltip = localization[titles[text]] || titles[text]; | ||
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| if (!tooltip) { | ||
| let value = element.value; | ||
| if (value) tooltip = localization[titles[value]] || titles[value]; | ||
| } | ||
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| if (!tooltip) { | ||
| // Gradio dropdown options have `data-value`. | ||
| let dataValue = element.dataset.value; | ||
| if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue]; | ||
| } | ||
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| if (!tooltip) { | ||
| for (const c of element.classList) { | ||
| if (c in titles) { | ||
| tooltip = localization[titles[c]] || titles[c]; | ||
| break; | ||
| } | ||
| } | ||
| } | ||
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| if (tooltip) { | ||
| element.title = tooltip; | ||
| } |
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The updateTooltip function logic appears sound, efficiently updating element titles with tooltips from the titles object. It correctly handles different scenarios, such as elements with existing titles, content-based tooltips, value-based tooltips, and class-based tooltips. However, consider adding comments to explain the logic behind handling different types of elements (e.g., Gradio dropdown options) for future maintainability.
+ // Explain the logic behind handling different types of elements for tooltipsCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| function updateTooltip(element) { | |
| if (element.title) return; // already has a title | |
| let text = element.textContent; | |
| let tooltip = localization[titles[text]] || titles[text]; | |
| if (!tooltip) { | |
| let value = element.value; | |
| if (value) tooltip = localization[titles[value]] || titles[value]; | |
| } | |
| if (!tooltip) { | |
| // Gradio dropdown options have `data-value`. | |
| let dataValue = element.dataset.value; | |
| if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue]; | |
| } | |
| if (!tooltip) { | |
| for (const c of element.classList) { | |
| if (c in titles) { | |
| tooltip = localization[titles[c]] || titles[c]; | |
| break; | |
| } | |
| } | |
| } | |
| if (tooltip) { | |
| element.title = tooltip; | |
| } | |
| function updateTooltip(element) { | |
| // Explain the logic behind handling different types of elements for tooltips | |
| if (element.title) return; // already has a title | |
| let text = element.textContent; | |
| let tooltip = localization[titles[text]] || titles[text]; | |
| if (!tooltip) { | |
| let value = element.value; | |
| if (value) tooltip = localization[titles[value]] || titles[value]; | |
| } | |
| if (!tooltip) { | |
| // Gradio dropdown options have `data-value`. | |
| let dataValue = element.dataset.value; | |
| if (dataValue) tooltip = localization[titles[dataValue]] || titles[dataValue]; | |
| } | |
| if (!tooltip) { | |
| for (const c of element.classList) { | |
| if (c in titles) { | |
| tooltip = localization[titles[c]] || titles[c]; | |
| break; | |
| } | |
| } | |
| } | |
| if (tooltip) { | |
| element.title = tooltip; | |
| } | |
| } |
| def latent_blend(settings, 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 | ||
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| # NOTE: We use inplace operations wherever possible. | ||
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| # [4][w][h] to [1][4][w][h] | ||
| t2 = t.unsqueeze(0) | ||
| # [4][w][h] to [1][1][w][h] - the [4] seem redundant. | ||
| t3 = t[0].unsqueeze(0).unsqueeze(0) | ||
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| one_minus_t2 = 1 - t2 | ||
| one_minus_t3 = 1 - t3 | ||
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| # Linearly interpolate the image vectors. | ||
| a_scaled = a * one_minus_t2 | ||
| b_scaled = b * t2 | ||
| image_interp = a_scaled | ||
| image_interp.add_(b_scaled) | ||
| result_type = image_interp.dtype | ||
| del a_scaled, b_scaled, t2, one_minus_t2 | ||
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| # 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, keepdim=True).to(torch.float64).add_(0.00001) | ||
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| # 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, keepdim=True).to(torch.float64).pow_( | ||
| settings.inpaint_detail_preservation) * one_minus_t3 | ||
| b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(torch.float64).pow_( | ||
| settings.inpaint_detail_preservation) * t3 | ||
| desired_magnitude = a_magnitude | ||
| desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation) | ||
| del a_magnitude, b_magnitude, t3, one_minus_t3 | ||
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| # 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 | ||
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| return image_interp_scaled |
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The function latent_blend performs complex tensor operations for blending latent image representations. While the logic appears sound, there are several areas where performance could be improved, such as minimizing the use of inplace operations and ensuring tensor operations are efficiently combined. Additionally, adding comments to explain the rationale behind complex operations would greatly enhance maintainability.
Consider reviewing the tensor operations for potential optimizations and adding more detailed comments to explain the steps involved in the blending process.
| def get_modified_nmask(settings, 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. | ||
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| Where: | ||
| 0 = fully opaque, infinite density, fully masked | ||
| 1 = fully transparent, zero density, fully unmasked | ||
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| 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. | ||
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| NOTE: "mask" is not used | ||
| """ | ||
| import torch | ||
| return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale) |
There was a problem hiding this comment.
The function get_modified_nmask is concise and uses a power operation to adjust the mask based on the settings. However, it's important to ensure that the sigma and mask_blend_power values are within expected ranges to avoid unexpected behavior. Adding input validation or comments to specify the expected range could improve code robustness.
Consider adding input validation or documentation comments to clarify the expected range of sigma and mask_blend_power.
| import torch | ||
| import modules.processing as proc | ||
| import modules.images as images | ||
| from PIL import Image, ImageOps, ImageFilter | ||
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| # 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 * settings.composite_mask_influence) | ||
| mask_scalar = mask_scalar / (1.00001 - mask_scalar) | ||
| mask_scalar = mask_scalar.cpu().numpy() | ||
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| latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1) | ||
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| kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2) | ||
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| masks_for_overlay = [] | ||
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| 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) | ||
| converted_mask = weighted_histogram_filter(converted_mask, kernel, kernel_center, | ||
| percentile_min=0.25, percentile_max=0.75, min_width=1) | ||
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| # The distance at which opacity of original decreases to 50% | ||
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar | ||
| converted_mask = converted_mask / half_weighted_distance | ||
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| converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast) | ||
| converted_mask = 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) | ||
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| # Remove aliasing artifacts using a gaussian blur. | ||
| converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4)) | ||
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| # 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) | ||
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| masks_for_overlay.append(converted_mask) | ||
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| 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'))) | ||
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| overlay_images[i] = image_masked.convert('RGBA') | ||
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| return masks_for_overlay | ||
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| def apply_masks( | ||
| settings, | ||
| nmask, | ||
| overlay_images, | ||
| width, height, | ||
| paste_to): | ||
| import torch | ||
| import modules.processing as proc | ||
| import modules.images as images | ||
| from PIL import Image, ImageOps, ImageFilter | ||
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| converted_mask = nmask[0].float() | ||
| converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2) | ||
| converted_mask = 255. * converted_mask | ||
| converted_mask = converted_mask.cpu().numpy().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) | ||
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| # Remove aliasing artifacts using a gaussian blur. | ||
| converted_mask = converted_mask.filter(ImageFilter.GaussianBlur(radius=4)) | ||
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| # Expand the mask to fit the whole image if needed. | ||
| if paste_to is not None: | ||
| converted_mask = proc.uncrop(converted_mask, | ||
| (width, height), | ||
| paste_to) | ||
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| masks_for_overlay = [] | ||
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| for i, overlay_image in enumerate(overlay_images): | ||
| masks_for_overlay[i] = converted_mask | ||
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| 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'))) | ||
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| overlay_images[i] = image_masked.convert('RGBA') | ||
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| return masks_for_overlay |
There was a problem hiding this comment.
The function apply_masks and apply_adaptive_masks perform complex image processing operations. These functions are well-structured but could benefit from further optimization, especially in terms of reducing the number of operations performed on tensors and images. Additionally, ensuring that all image and tensor operations are batched where possible could improve performance.
Review the image and tensor operations for potential optimizations, especially focusing on reducing the computational overhead by batching operations where possible.
| (nparray): A filtered copy of the input image "img", a 2-D array of floats. | ||
| """ | ||
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| # Converts an index tuple into a vector. | ||
| def vec(x): | ||
| return np.array(x) | ||
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| kernel_min = -kernel_center | ||
| kernel_max = vec(kernel.shape) - kernel_center | ||
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| 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 | ||
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| class WeightedElement: | ||
| """ | ||
| An element of the histogram, its weight | ||
| and bounds. | ||
| """ | ||
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| 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 | ||
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| # 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) | ||
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| def sort_key(x: WeightedElement): | ||
| return x.value | ||
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| values.sort(key=sort_key) | ||
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| # 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 | ||
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| # 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 | ||
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| # 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 | ||
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| if window_max > sum: | ||
| window_max = sum | ||
| window_min = sum - min_width | ||
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| if window_min < 0: | ||
| window_min = 0 | ||
| window_max = min_width | ||
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| value = 0 | ||
| value_weight = 0 | ||
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| # 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 | ||
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| s = max(window_min, values[i].window_min) | ||
| e = min(window_max, values[i].window_max) | ||
| w = e - s | ||
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| value += values[i].value * w | ||
| value_weight += w | ||
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| return value / value_weight if value_weight != 0 else 0 | ||
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| img_out = img.copy() | ||
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| # Apply the kernel operation over each pixel. | ||
| for index in np.ndindex(img.shape): | ||
| img_out[index] = weighted_histogram_filter_single(index) | ||
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| return img_out |
There was a problem hiding this comment.
The function weighted_histogram_filter is a complex and innovative approach to applying a generalized convolution filter. While the implementation is correct, the complexity of the function could be reduced by refactoring into smaller, more manageable functions. Additionally, adding more detailed comments explaining the algorithm's steps and its purpose would greatly aid in understanding and maintaining the code.
Consider refactoring the function into smaller parts and adding detailed comments to improve readability and maintainability.
| default = SoftInpaintingSettings(1, 0.5, 4, 0, 0.5, 2) | ||
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| enabled_ui_label = "Soft inpainting" | ||
| enabled_gen_param_label = "Soft inpainting enabled" | ||
| enabled_el_id = "soft_inpainting_enabled" | ||
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| ui_labels = SoftInpaintingSettings( | ||
| "Schedule bias", | ||
| "Preservation strength", | ||
| "Transition contrast boost", | ||
| "Mask influence", | ||
| "Difference threshold", | ||
| "Difference contrast") | ||
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| ui_info = SoftInpaintingSettings( | ||
| "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.", | ||
| "How strongly the original mask should bias the difference threshold.", | ||
| "How much an image region can change before the original pixels are not blended in anymore.", | ||
| "How sharp the transition should be between blended and not blended.") | ||
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| gen_param_labels = SoftInpaintingSettings( | ||
| "Soft inpainting schedule bias", | ||
| "Soft inpainting preservation strength", | ||
| "Soft inpainting transition contrast boost", | ||
| "Soft inpainting mask influence", | ||
| "Soft inpainting difference threshold", | ||
| "Soft inpainting difference contrast") | ||
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| el_ids = SoftInpaintingSettings( | ||
| "mask_blend_power", | ||
| "mask_blend_scale", | ||
| "inpaint_detail_preservation", | ||
| "composite_mask_influence", | ||
| "composite_difference_threshold", | ||
| "composite_difference_contrast") |
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The constants and labels defined towards the end of the file are clear and well-organized. However, it's recommended to group these constants in a more structured way, possibly using a class or a module-level dictionary, to improve modularity and maintainability.
Consider organizing these constants into a class or dictionary for better structure and maintainability.
| def process(self, p, enabled, power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr): | ||
| if not enabled: | ||
| return | ||
|
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| if not processing_uses_inpainting(p): | ||
| return | ||
|
|
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| # Shut off the rounding it normally does. | ||
| p.mask_round = False | ||
|
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| settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) | ||
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| # p.extra_generation_params["Mask rounding"] = False | ||
| settings.add_generation_params(p.extra_generation_params) | ||
|
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| def on_mask_blend(self, p, mba: scripts.MaskBlendArgs, enabled, power, scale, detail_preservation, mask_inf, | ||
| dif_thresh, dif_contr): | ||
| if not enabled: | ||
| return | ||
|
|
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| if not processing_uses_inpainting(p): | ||
| return | ||
|
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| if mba.is_final_blend: | ||
| mba.blended_latent = mba.current_latent | ||
| return | ||
|
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| settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) | ||
|
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| # todo: Why is sigma 2D? Both values are the same. | ||
| mba.blended_latent = latent_blend(settings, | ||
| mba.init_latent, | ||
| mba.current_latent, | ||
| get_modified_nmask(settings, mba.nmask, mba.sigma[0])) | ||
|
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| def post_sample(self, p, ps: scripts.PostSampleArgs, enabled, power, scale, detail_preservation, mask_inf, | ||
| dif_thresh, dif_contr): | ||
| if not enabled: | ||
| return | ||
|
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| if not processing_uses_inpainting(p): | ||
| return | ||
|
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| nmask = getattr(p, "nmask", None) | ||
| if nmask is None: | ||
| return | ||
|
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| from modules import images | ||
| from modules.shared import opts | ||
|
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| settings = SoftInpaintingSettings(power, scale, detail_preservation, mask_inf, dif_thresh, dif_contr) | ||
|
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| # since the original code puts holes in the existing overlay images, | ||
| # we have to rebuild them. | ||
| self.overlay_images = [] | ||
| for img in p.init_images: | ||
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| image = images.flatten(img, opts.img2img_background_color) | ||
|
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| if p.paste_to is None and p.resize_mode != 3: | ||
| image = images.resize_image(p.resize_mode, image, p.width, p.height) | ||
|
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| self.overlay_images.append(image.convert('RGBA')) | ||
|
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| if len(p.init_images) == 1: | ||
| self.overlay_images = self.overlay_images * p.batch_size | ||
|
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| if getattr(ps.samples, 'already_decoded', False): | ||
| self.masks_for_overlay = apply_masks(settings=settings, | ||
| nmask=nmask, | ||
| overlay_images=self.overlay_images, | ||
| width=p.width, | ||
| height=p.height, | ||
| paste_to=p.paste_to) | ||
| else: | ||
| self.masks_for_overlay = apply_adaptive_masks(settings=settings, | ||
| nmask=nmask, | ||
| latent_orig=p.init_latent, | ||
| latent_processed=ps.samples, | ||
| overlay_images=self.overlay_images, | ||
| width=p.width, | ||
| height=p.height, | ||
| paste_to=p.paste_to) | ||
|
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| def postprocess_maskoverlay(self, p, ppmo: scripts.PostProcessMaskOverlayArgs, enabled, power, scale, | ||
| detail_preservation, mask_inf, dif_thresh, dif_contr): | ||
| if not enabled: | ||
| return | ||
|
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| if not processing_uses_inpainting(p): | ||
| return | ||
|
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| if self.masks_for_overlay is None: | ||
| return | ||
|
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| if self.overlay_images is None: | ||
| return | ||
|
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| ppmo.mask_for_overlay = self.masks_for_overlay[ppmo.index] | ||
| ppmo.overlay_image = self.overlay_images[ppmo.index] |
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The Script class integrates the soft inpainting functionality with the rest of the application. The methods are well-structured and logically sound. However, there are opportunities to improve code readability and maintainability, such as adding type hints and refactoring large methods into smaller, more focused functions. Additionally, ensuring consistent documentation across all methods would aid in understanding the class's purpose and usage.
Consider adding type hints to method parameters and return types, refactoring large methods, and ensuring consistent documentation.
javascript/imageviewer.js
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| if (e.dataset.modded) { | ||
| return; | ||
| } | ||
|
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| e.dataset.modded = true; | ||
| e.style.cursor='pointer' | ||
| e.style.userSelect='none' | ||
| e.dataset.modded = true; | ||
| e.style.cursor = 'pointer'; | ||
| e.style.userSelect = 'none'; | ||
|
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| var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1 | ||
| var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1; | ||
|
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| // For Firefox, listening on click first switched to next image then shows the lightbox. | ||
| // If you know how to fix this without switching to mousedown event, please. | ||
| // For other browsers the event is click to make it possiblr to drag picture. | ||
| var event = isFirefox ? 'mousedown' : 'click' | ||
| // For Firefox, listening on click first switched to next image then shows the lightbox. | ||
| // If you know how to fix this without switching to mousedown event, please. | ||
| // For other browsers the event is click to make it possiblr to drag picture. | ||
| var event = isFirefox ? 'mousedown' : 'click'; | ||
|
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| e.addEventListener(event, function (evt) { | ||
| if(!opts.js_modal_lightbox || evt.button != 0) return; | ||
| e.addEventListener(event, function(evt) { | ||
| if (evt.button == 1) { | ||
| open(evt.target.src); | ||
| evt.preventDefault(); | ||
| return; | ||
| } | ||
| if (!opts.js_modal_lightbox || evt.button != 0) return; | ||
|
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| modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) | ||
| evt.preventDefault() | ||
| showModal(evt) | ||
| }, true); | ||
| modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed); | ||
| evt.preventDefault(); | ||
| showModal(evt); | ||
| }, true); |
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The setupImageForLightbox function correctly marks images for lightbox functionality and handles different events based on the browser. However, the use of var for isFirefox is outdated. Consider using let or const for better scoping and readability.
- var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1;
+ const isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1;Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| if (e.dataset.modded) { | |
| return; | |
| } | |
| e.dataset.modded = true; | |
| e.style.cursor='pointer' | |
| e.style.userSelect='none' | |
| e.dataset.modded = true; | |
| e.style.cursor = 'pointer'; | |
| e.style.userSelect = 'none'; | |
| var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1 | |
| var isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1; | |
| // For Firefox, listening on click first switched to next image then shows the lightbox. | |
| // If you know how to fix this without switching to mousedown event, please. | |
| // For other browsers the event is click to make it possiblr to drag picture. | |
| var event = isFirefox ? 'mousedown' : 'click' | |
| // For Firefox, listening on click first switched to next image then shows the lightbox. | |
| // If you know how to fix this without switching to mousedown event, please. | |
| // For other browsers the event is click to make it possiblr to drag picture. | |
| var event = isFirefox ? 'mousedown' : 'click'; | |
| e.addEventListener(event, function (evt) { | |
| if(!opts.js_modal_lightbox || evt.button != 0) return; | |
| e.addEventListener(event, function(evt) { | |
| if (evt.button == 1) { | |
| open(evt.target.src); | |
| evt.preventDefault(); | |
| return; | |
| } | |
| if (!opts.js_modal_lightbox || evt.button != 0) return; | |
| modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed) | |
| evt.preventDefault() | |
| showModal(evt) | |
| }, true); | |
| modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed); | |
| evt.preventDefault(); | |
| showModal(evt); | |
| }, true); | |
| if (e.dataset.modded) { | |
| return; | |
| } | |
| e.dataset.modded = true; | |
| e.style.cursor = 'pointer'; | |
| e.style.userSelect = 'none'; | |
| const isFirefox = navigator.userAgent.toLowerCase().indexOf('firefox') > -1; | |
| // For Firefox, listening on click first switched to next image then shows the lightbox. | |
| // If you know how to fix this without switching to mousedown event, please. | |
| // For other browsers the event is click to make it possiblr to drag picture. | |
| var event = isFirefox ? 'mousedown' : 'click'; | |
| e.addEventListener(event, function(evt) { | |
| if (evt.button == 1) { | |
| open(evt.target.src); | |
| evt.preventDefault(); | |
| return; | |
| } | |
| if (!opts.js_modal_lightbox || evt.button != 0) return; | |
| modalZoomSet(gradioApp().getElementById('modalImage'), opts.js_modal_lightbox_initially_zoomed); | |
| evt.preventDefault(); | |
| showModal(evt); | |
| }, true); |
| modal.style.setProperty('background-image', 'none'); | ||
| } else { | ||
| modalImage.style.display = 'none'; | ||
| modal.style.setProperty('background-image', `url(${modalImage.src})`) | ||
| modal.style.setProperty('background-image', `url(${modalImage.src})`); |
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The modalTileImageToggle function's logic for toggling the tiling preview is clear and functional. However, consider abstracting the repeated pattern of changing the display style and background image property into a helper function for DRY (Don't Repeat Yourself) compliance.
function toggleDisplayStyle(element, displayStyle, backgroundImageUrl = null) {
element.style.display = displayStyle;
if (backgroundImageUrl !== null) {
element.style.setProperty('background-image', `url(${backgroundImageUrl})`);
}
}
javascript/imageviewer.js
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| const modalControls = document.createElement('div') | ||
| const modalControls = document.createElement('div'); | ||
| modalControls.className = 'modalControls gradio-container'; | ||
| modal.append(modalControls); | ||
|
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| const modalZoom = document.createElement('span') | ||
| const modalZoom = document.createElement('span'); | ||
| modalZoom.className = 'modalZoom cursor'; | ||
| modalZoom.innerHTML = '⤡' | ||
| modalZoom.addEventListener('click', modalZoomToggle, true) | ||
| modalZoom.innerHTML = '⤡'; | ||
| modalZoom.addEventListener('click', modalZoomToggle, true); | ||
| modalZoom.title = "Toggle zoomed view"; | ||
| modalControls.appendChild(modalZoom) | ||
| modalControls.appendChild(modalZoom); | ||
|
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| const modalTileImage = document.createElement('span') | ||
| const modalTileImage = document.createElement('span'); | ||
| modalTileImage.className = 'modalTileImage cursor'; | ||
| modalTileImage.innerHTML = '⊞' | ||
| modalTileImage.addEventListener('click', modalTileImageToggle, true) | ||
| modalTileImage.innerHTML = '⊞'; | ||
| modalTileImage.addEventListener('click', modalTileImageToggle, true); | ||
| modalTileImage.title = "Preview tiling"; | ||
| modalControls.appendChild(modalTileImage) | ||
| modalControls.appendChild(modalTileImage); | ||
|
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| const modalSave = document.createElement("span") | ||
| modalSave.className = "modalSave cursor" | ||
| modalSave.id = "modal_save" | ||
| modalSave.innerHTML = "🖫" | ||
| modalSave.addEventListener("click", modalSaveImage, true) | ||
| modalSave.title = "Save Image(s)" | ||
| modalControls.appendChild(modalSave) | ||
| const modalSave = document.createElement("span"); | ||
| modalSave.className = "modalSave cursor"; | ||
| modalSave.id = "modal_save"; | ||
| modalSave.innerHTML = "🖫"; | ||
| modalSave.addEventListener("click", modalSaveImage, true); | ||
| modalSave.title = "Save Image(s)"; | ||
| modalControls.appendChild(modalSave); | ||
|
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| const modalClose = document.createElement('span') | ||
| const modalClose = document.createElement('span'); | ||
| modalClose.className = 'modalClose cursor'; | ||
| modalClose.innerHTML = '×' | ||
| modalClose.innerHTML = '×'; | ||
| modalClose.onclick = closeModal; | ||
| modalClose.title = "Close image viewer"; | ||
| modalControls.appendChild(modalClose) | ||
| modalControls.appendChild(modalClose); | ||
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| const modalImage = document.createElement('img') | ||
| const modalImage = document.createElement('img'); | ||
| modalImage.id = 'modalImage'; | ||
| modalImage.onclick = closeModal; | ||
| modalImage.tabIndex = 0 | ||
| modalImage.addEventListener('keydown', modalKeyHandler, true) | ||
| modal.appendChild(modalImage) | ||
| modalImage.tabIndex = 0; | ||
| modalImage.addEventListener('keydown', modalKeyHandler, true); | ||
| modal.appendChild(modalImage); | ||
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| const modalPrev = document.createElement('a') | ||
| const modalPrev = document.createElement('a'); | ||
| modalPrev.className = 'modalPrev'; | ||
| modalPrev.innerHTML = '❮' | ||
| modalPrev.tabIndex = 0 | ||
| modalPrev.innerHTML = '❮'; | ||
| modalPrev.tabIndex = 0; | ||
| modalPrev.addEventListener('click', modalPrevImage, true); | ||
| modalPrev.addEventListener('keydown', modalKeyHandler, true) | ||
| modal.appendChild(modalPrev) | ||
| modalPrev.addEventListener('keydown', modalKeyHandler, true); | ||
| modal.appendChild(modalPrev); | ||
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| const modalNext = document.createElement('a') | ||
| const modalNext = document.createElement('a'); | ||
| modalNext.className = 'modalNext'; | ||
| modalNext.innerHTML = '❯' | ||
| modalNext.tabIndex = 0 | ||
| modalNext.innerHTML = '❯'; | ||
| modalNext.tabIndex = 0; | ||
| modalNext.addEventListener('click', modalNextImage, true); | ||
| modalNext.addEventListener('keydown', modalKeyHandler, true) | ||
| modalNext.addEventListener('keydown', modalKeyHandler, true); | ||
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| modal.appendChild(modalNext) | ||
| modal.appendChild(modalNext); | ||
|
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| try { | ||
| gradioApp().appendChild(modal); | ||
| } catch (e) { | ||
| gradioApp().body.appendChild(modal); | ||
| } | ||
| gradioApp().appendChild(modal); | ||
| } catch (e) { | ||
| gradioApp().body.appendChild(modal); | ||
| } |
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The dynamic creation and configuration of modal elements (e.g., modalZoom, modalTileImage, modalSave, modalClose, modalImage, modalPrev, modalNext) are correctly implemented. However, consider grouping related element creation and configuration code into functions or a class to improve modularity and readability.
function createModalElement(tagName, className, innerHTML, title, eventListener) {
const element = document.createElement(tagName);
element.className = className;
element.innerHTML = innerHTML;
element.title = title;
if (eventListener) {
element.addEventListener('click', eventListener, true);
}
return element;
}
| def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name): | ||
| metadata = {} | ||
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| for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]: | ||
| checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None) | ||
| if checkpoint_info is None: | ||
| continue | ||
|
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| metadata.update(checkpoint_info.metadata) | ||
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| return json.dumps(metadata, indent=4, ensure_ascii=False) |
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The read_metadata function is a new addition that aggregates metadata from multiple model checkpoints. It's important to ensure that the metadata structure is consistent across different models to avoid potential issues when merging or updating metadata. Additionally, consider handling the case where metadata might be missing or malformed in some checkpoints to prevent runtime errors.
+ if 'metadata' not in checkpoint_info:
+ print(f"Warning: No metadata found for checkpoint {checkpoint_name}")
+ continueCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name): | |
| metadata = {} | |
| for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]: | |
| checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None) | |
| if checkpoint_info is None: | |
| continue | |
| metadata.update(checkpoint_info.metadata) | |
| return json.dumps(metadata, indent=4, ensure_ascii=False) | |
| def read_metadata(primary_model_name, secondary_model_name, tertiary_model_name): | |
| metadata = {} | |
| for checkpoint_name in [primary_model_name, secondary_model_name, tertiary_model_name]: | |
| checkpoint_info = sd_models.checkpoints_list.get(checkpoint_name, None) | |
| if checkpoint_info is None: | |
| continue | |
| if 'metadata' not in checkpoint_info: | |
| print(f"Warning: No metadata found for checkpoint {checkpoint_name}") | |
| continue | |
| metadata.update(checkpoint_info.metadata) | |
| return json.dumps(metadata, indent=4, ensure_ascii=False) |
| return json.dumps(metadata, indent=4, ensure_ascii=False) | ||
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| def run_modelmerger(id_task, primary_model_name, secondary_model_name, tertiary_model_name, interp_method, multiplier, save_as_half, custom_name, checkpoint_format, config_source, bake_in_vae, discard_weights, save_metadata, add_merge_recipe, copy_metadata_fields, metadata_json): |
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The run_modelmerger function has been significantly modified to include additional parameters and logic. It's crucial to ensure that all new parameters are well-documented and validated at the beginning of the function to prevent misuse or errors during the model merging process. Additionally, consider breaking down this large function into smaller, more manageable functions to improve readability and maintainability.
| "is_inpainting": result_is_inpainting_model, | ||
| "is_instruct_pix2pix": result_is_instruct_pix2pix_model | ||
| } | ||
| metadata["sd_merge_recipe"] = json.dumps(merge_recipe) | ||
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| sd_merge_models = {} | ||
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| def add_model_metadata(checkpoint_info): | ||
| checkpoint_info.calculate_shorthash() | ||
| metadata["sd_merge_models"][checkpoint_info.sha256] = { | ||
| sd_merge_models[checkpoint_info.sha256] = { | ||
| "name": checkpoint_info.name, | ||
| "legacy_hash": checkpoint_info.hash, | ||
| "sd_merge_recipe": checkpoint_info.metadata.get("sd_merge_recipe", None) | ||
| } | ||
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| metadata["sd_merge_models"].update(checkpoint_info.metadata.get("sd_merge_models", {})) | ||
| sd_merge_models.update(checkpoint_info.metadata.get("sd_merge_models", {})) | ||
|
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| add_model_metadata(primary_model_info) | ||
| if secondary_model_info: | ||
| add_model_metadata(secondary_model_info) | ||
| if tertiary_model_info: | ||
| add_model_metadata(tertiary_model_info) | ||
|
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| metadata["sd_merge_models"] = json.dumps(metadata["sd_merge_models"]) | ||
| metadata["sd_merge_recipe"] = json.dumps(merge_recipe) | ||
| metadata["sd_merge_models"] = json.dumps(sd_merge_models) | ||
|
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| _, extension = os.path.splitext(output_modelname) | ||
| if extension.lower() == ".safetensors": | ||
| safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata) | ||
| safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None) | ||
| else: | ||
| torch.save(theta_0, output_modelname) | ||
|
|
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📝 NOTE
This review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [257-311]
The metadata handling logic within the model merging process has been updated to include more comprehensive metadata aggregation and customization. Ensure that the metadata structure is consistent and that all fields are properly validated before merging. Additionally, consider extracting the metadata handling logic into a separate function to improve modularity and readability.
|
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| _, extension = os.path.splitext(output_modelname) | ||
| if extension.lower() == ".safetensors": | ||
| safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata) | ||
| safetensors.torch.save_file(theta_0, output_modelname, metadata=metadata if len(metadata)>0 else None) | ||
| else: | ||
| torch.save(theta_0, output_modelname) |
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The static analysis tool warns about potential security risks associated with using functions that rely on pickle for serialization. Consider following the recommendation to switch to a safer serialization method like ONNX, especially for operations involving loading or saving model checkpoints, to mitigate the risk of arbitrary code execution.
| function extract_image_from_gallery(gallery) { | ||
| if (gallery.length == 0) { | ||
| return [null]; | ||
| } | ||
| if (gallery.length == 1){ | ||
| if (gallery.length == 1) { | ||
| return [gallery[0]]; | ||
| } | ||
|
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| index = selected_gallery_index() | ||
| var index = selected_gallery_index(); | ||
|
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| if (index < 0 || index >= gallery.length){ | ||
| if (index < 0 || index >= gallery.length) { |
There was a problem hiding this comment.
The extract_image_from_gallery function has been introduced to extract a selected image from the gallery. Ensure that the logic correctly handles edge cases, such as when the gallery is empty or when no image is selected. Consider adding error handling or default behavior for these scenarios.
| function create_tab_index_args(tabId, args) { | ||
| var res = Array.from(args); | ||
| res[0] = get_tab_index(tabId); | ||
| return res; |
There was a problem hiding this comment.
The create_tab_index_args function introduces a way to create arguments for tab switching with the index included. Ensure that the arguments manipulation is robust and does not inadvertently modify the original arguments array passed to the function.
| function confirm_clear_prompt(prompt, negative_prompt) { | ||
| if (confirm("Delete prompt?")) { | ||
| prompt = ""; | ||
| negative_prompt = ""; | ||
| } | ||
| } | ||
|
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| function recalculate_prompts_txt2img(){ | ||
| recalculatePromptTokens('txt2img_prompt') | ||
| recalculatePromptTokens('txt2img_neg_prompt') | ||
| return args_to_array(arguments); | ||
| return [prompt, negative_prompt]; |
There was a problem hiding this comment.
The confirm_clear_prompt function introduces a confirmation dialog for clearing prompts. Ensure that the confirmation dialog is user-friendly and provides clear options for confirmation or cancellation. Additionally, consider the user experience and whether there are scenarios where this confirmation might become intrusive or annoying.
javascript/ui.js
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| function restart_reload() { | ||
| document.body.innerHTML = '<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>'; | ||
|
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| function update_img2img_tokens(...args) { | ||
| update_token_counter("img2img_token_button") | ||
| if (args.length == 2) | ||
| return args[0] | ||
| return args; | ||
| } | ||
| var requestPing = function() { | ||
| requestGet("./internal/ping", {}, function(data) { | ||
| location.reload(); | ||
| }, function() { | ||
| setTimeout(requestPing, 500); | ||
| }); | ||
| }; | ||
|
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| function update_token_counter(button_id) { | ||
| if (token_timeouts[button_id]) | ||
| clearTimeout(token_timeouts[button_id]); | ||
| token_timeouts[button_id] = setTimeout(() => gradioApp().getElementById(button_id)?.click(), wait_time); | ||
| } | ||
|
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| function restart_reload(){ | ||
| document.body.innerHTML='<h1 style="font-family:monospace;margin-top:20%;color:lightgray;text-align:center;">Reloading...</h1>'; | ||
| setTimeout(function(){location.reload()},2000) | ||
| setTimeout(requestPing, 2000); | ||
|
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||
| return [] | ||
| return []; |
There was a problem hiding this comment.
The restart_reload function introduces a mechanism for reloading the application. Ensure that the reload process is smooth and does not lead to data loss or state inconsistencies. Additionally, consider providing feedback to the user during the reload process to improve the user experience.
| function switchWidthHeight(tabname) { | ||
| var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]"); | ||
| var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]"); | ||
| if (!width || !height) return []; | ||
|
|
||
| var tmp = width.value; | ||
| width.value = height.value; | ||
| height.value = tmp; | ||
|
|
||
| updateInput(width); | ||
| updateInput(height); | ||
| return []; |
There was a problem hiding this comment.
The switchWidthHeight function swaps the width and height values in the UI. Ensure that this function correctly updates the input fields and that the changes are immediately reflected in the UI. Additionally, consider adding validation to ensure that the input values are within acceptable ranges before swapping.
modules/cache.py
Outdated
| def dump_cache(): | ||
| """ | ||
| Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written. | ||
| """ | ||
|
|
||
| global dump_cache_after | ||
| global dump_cache_thread | ||
|
|
||
| def thread_func(): | ||
| global dump_cache_after | ||
| global dump_cache_thread | ||
|
|
||
| while dump_cache_after is not None and time.time() < dump_cache_after: | ||
| time.sleep(1) | ||
|
|
||
| with cache_lock: | ||
| cache_filename_tmp = cache_filename + "-" | ||
| with open(cache_filename_tmp, "w", encoding="utf8") as file: | ||
| json.dump(cache_data, file, indent=4, ensure_ascii=False) | ||
|
|
||
| os.replace(cache_filename_tmp, cache_filename) | ||
|
|
||
| dump_cache_after = None | ||
| dump_cache_thread = None | ||
|
|
||
| with cache_lock: | ||
| dump_cache_after = time.time() + 5 | ||
| if dump_cache_thread is None: | ||
| dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func) | ||
| dump_cache_thread.start() |
There was a problem hiding this comment.
The dump_cache function correctly implements a delayed cache writing mechanism, which is a good practice to reduce disk I/O operations. However, consider adding error handling around the file operations (open, json.dump, os.replace) to catch and log potential I/O errors or issues with file permissions.
try:
with open(cache_filename_tmp, "w", encoding="utf8") as file:
json.dump(cache_data, file, indent=4, ensure_ascii=False)
os.replace(cache_filename_tmp, cache_filename)
except Exception as e:
print(f'[ERROR] Failed to dump cache: {e}')Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def dump_cache(): | |
| """ | |
| Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written. | |
| """ | |
| global dump_cache_after | |
| global dump_cache_thread | |
| def thread_func(): | |
| global dump_cache_after | |
| global dump_cache_thread | |
| while dump_cache_after is not None and time.time() < dump_cache_after: | |
| time.sleep(1) | |
| with cache_lock: | |
| cache_filename_tmp = cache_filename + "-" | |
| with open(cache_filename_tmp, "w", encoding="utf8") as file: | |
| json.dump(cache_data, file, indent=4, ensure_ascii=False) | |
| os.replace(cache_filename_tmp, cache_filename) | |
| dump_cache_after = None | |
| dump_cache_thread = None | |
| with cache_lock: | |
| dump_cache_after = time.time() + 5 | |
| if dump_cache_thread is None: | |
| dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func) | |
| dump_cache_thread.start() | |
| def dump_cache(): | |
| """ | |
| Marks cache for writing to disk. 5 seconds after no one else flags the cache for writing, it is written. | |
| """ | |
| global dump_cache_after | |
| global dump_cache_thread | |
| def thread_func(): | |
| global dump_cache_after | |
| global dump_cache_thread | |
| while dump_cache_after is not None and time.time() < dump_cache_after: | |
| time.sleep(1) | |
| with cache_lock: | |
| cache_filename_tmp = cache_filename + "-" | |
| try: | |
| with open(cache_filename_tmp, "w", encoding="utf8") as file: | |
| json.dump(cache_data, file, indent=4, ensure_ascii=False) | |
| os.replace(cache_filename_tmp, cache_filename) | |
| except Exception as e: | |
| print(f'[ERROR] Failed to dump cache: {e}') | |
| dump_cache_after = None | |
| dump_cache_thread = None | |
| with cache_lock: | |
| dump_cache_after = time.time() + 5 | |
| if dump_cache_thread is None: | |
| dump_cache_thread = threading.Thread(name='cache-writer', target=thread_func) | |
| dump_cache_thread.start() |
| if (commentBefore) { | ||
| var comment = document.createElement('DIV'); | ||
| comment.className = 'settings-comment'; | ||
| comment.innerHTML = commentBefore; |
There was a problem hiding this comment.
Using innerHTML to insert user-controlled data can lead to Cross-Site Scripting (XSS) vulnerabilities. Ensure that the content assigned to innerHTML is properly sanitized or consider using safer alternatives like textContent or DOM methods to create and append elements.
- comment.innerHTML = commentBefore;
+ comment.textContent = commentBefore;Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| comment.innerHTML = commentBefore; | |
| comment.textContent = commentBefore; |
| if (commentAfter) { | ||
| comment = document.createElement('DIV'); | ||
| comment.className = 'settings-comment'; | ||
| comment.innerHTML = commentAfter; |
There was a problem hiding this comment.
Similar to the previous comment, using innerHTML with user-controlled data poses a security risk. Replace innerHTML with textContent or use DOM manipulation methods to safely create and append content.
- comment.innerHTML = commentAfter;
+ comment.textContent = commentAfter;Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| comment.innerHTML = commentAfter; | |
| comment.textContent = commentAfter; |
modules/cache.py
Outdated
| def cache(subsection): | ||
| """ | ||
| Retrieves or initializes a cache for a specific subsection. | ||
|
|
||
| Parameters: | ||
| subsection (str): The subsection identifier for the cache. | ||
|
|
||
| Returns: | ||
| dict: The cache data for the specified subsection. | ||
| """ | ||
|
|
||
| global cache_data | ||
|
|
||
| if cache_data is None: | ||
| with cache_lock: | ||
| if cache_data is None: | ||
| try: | ||
| with open(cache_filename, "r", encoding="utf8") as file: | ||
| cache_data = json.load(file) | ||
| except FileNotFoundError: | ||
| cache_data = {} | ||
| except Exception: | ||
| os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json")) | ||
| print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache') | ||
| cache_data = {} | ||
|
|
||
| s = cache_data.get(subsection, {}) | ||
| cache_data[subsection] = s | ||
|
|
||
| return s |
There was a problem hiding this comment.
The cache function's implementation for retrieving or initializing cache subsections is clear and follows best practices. However, when handling exceptions for file operations, it's recommended to log the error or notify the user instead of silently failing and creating a new cache. This can help in diagnosing issues with file access or corruption.
except FileNotFoundError:
cache_data = {}
except Exception as e:
- os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json"))
- print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache')
+ print(f'[ERROR] Failed to read cache file: {e}')
cache_data = {}Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def cache(subsection): | |
| """ | |
| Retrieves or initializes a cache for a specific subsection. | |
| Parameters: | |
| subsection (str): The subsection identifier for the cache. | |
| Returns: | |
| dict: The cache data for the specified subsection. | |
| """ | |
| global cache_data | |
| if cache_data is None: | |
| with cache_lock: | |
| if cache_data is None: | |
| try: | |
| with open(cache_filename, "r", encoding="utf8") as file: | |
| cache_data = json.load(file) | |
| except FileNotFoundError: | |
| cache_data = {} | |
| except Exception: | |
| os.replace(cache_filename, os.path.join(script_path, "tmp", "cache.json")) | |
| print('[ERROR] issue occurred while trying to read cache.json, move current cache to tmp/cache.json and create new cache') | |
| cache_data = {} | |
| s = cache_data.get(subsection, {}) | |
| cache_data[subsection] = s | |
| return s | |
| def cache(subsection): | |
| """ | |
| Retrieves or initializes a cache for a specific subsection. | |
| Parameters: | |
| subsection (str): The subsection identifier for the cache. | |
| Returns: | |
| dict: The cache data for the specified subsection. | |
| """ | |
| global cache_data | |
| if cache_data is None: | |
| with cache_lock: | |
| if cache_data is None: | |
| try: | |
| with open(cache_filename, "r", encoding="utf8") as file: | |
| cache_data = json.load(file) | |
| except FileNotFoundError: | |
| cache_data = {} | |
| except Exception as e: | |
| print(f'[ERROR] Failed to read cache file: {e}') | |
| cache_data = {} | |
| s = cache_data.get(subsection, {}) | |
| cache_data[subsection] = s | |
| return s |
| DEPTH_LAYERS = { | ||
| 0: [ | ||
| # SD 1.5 U-Net (diffusers) | ||
| "down_blocks.0.attentions.0.transformer_blocks.0.attn1", | ||
| "down_blocks.0.attentions.1.transformer_blocks.0.attn1", | ||
| "up_blocks.3.attentions.0.transformer_blocks.0.attn1", | ||
| "up_blocks.3.attentions.1.transformer_blocks.0.attn1", | ||
| "up_blocks.3.attentions.2.transformer_blocks.0.attn1", | ||
| # SD 1.5 U-Net (ldm) | ||
| "input_blocks.1.1.transformer_blocks.0.attn1", | ||
| "input_blocks.2.1.transformer_blocks.0.attn1", | ||
| "output_blocks.9.1.transformer_blocks.0.attn1", | ||
| "output_blocks.10.1.transformer_blocks.0.attn1", | ||
| "output_blocks.11.1.transformer_blocks.0.attn1", | ||
| # SD 1.5 VAE | ||
| "decoder.mid_block.attentions.0", | ||
| "decoder.mid.attn_1", | ||
| ], | ||
| 1: [ | ||
| # SD 1.5 U-Net (diffusers) | ||
| "down_blocks.1.attentions.0.transformer_blocks.0.attn1", | ||
| "down_blocks.1.attentions.1.transformer_blocks.0.attn1", | ||
| "up_blocks.2.attentions.0.transformer_blocks.0.attn1", | ||
| "up_blocks.2.attentions.1.transformer_blocks.0.attn1", | ||
| "up_blocks.2.attentions.2.transformer_blocks.0.attn1", | ||
| # SD 1.5 U-Net (ldm) | ||
| "input_blocks.4.1.transformer_blocks.0.attn1", | ||
| "input_blocks.5.1.transformer_blocks.0.attn1", | ||
| "output_blocks.6.1.transformer_blocks.0.attn1", | ||
| "output_blocks.7.1.transformer_blocks.0.attn1", | ||
| "output_blocks.8.1.transformer_blocks.0.attn1", | ||
| ], | ||
| 2: [ | ||
| # SD 1.5 U-Net (diffusers) | ||
| "down_blocks.2.attentions.0.transformer_blocks.0.attn1", | ||
| "down_blocks.2.attentions.1.transformer_blocks.0.attn1", | ||
| "up_blocks.1.attentions.0.transformer_blocks.0.attn1", | ||
| "up_blocks.1.attentions.1.transformer_blocks.0.attn1", | ||
| "up_blocks.1.attentions.2.transformer_blocks.0.attn1", | ||
| # SD 1.5 U-Net (ldm) | ||
| "input_blocks.7.1.transformer_blocks.0.attn1", | ||
| "input_blocks.8.1.transformer_blocks.0.attn1", | ||
| "output_blocks.3.1.transformer_blocks.0.attn1", | ||
| "output_blocks.4.1.transformer_blocks.0.attn1", | ||
| "output_blocks.5.1.transformer_blocks.0.attn1", | ||
| ], | ||
| 3: [ | ||
| # SD 1.5 U-Net (diffusers) | ||
| "mid_block.attentions.0.transformer_blocks.0.attn1", | ||
| # SD 1.5 U-Net (ldm) | ||
| "middle_block.1.transformer_blocks.0.attn1", | ||
| ], | ||
| } |
There was a problem hiding this comment.
The DEPTH_LAYERS and DEPTH_LAYERS_XL dictionaries are crucial for defining the layers to which hypertiling will be applied. It's important to ensure that all layer names are accurate and correspond to the actual model architecture. Additionally, consider adding comments to explain the rationale behind the selection of these specific layers for hypertiling.
+ // Add comments explaining the selection of layers for hypertilingCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| DEPTH_LAYERS = { | |
| 0: [ | |
| # SD 1.5 U-Net (diffusers) | |
| "down_blocks.0.attentions.0.transformer_blocks.0.attn1", | |
| "down_blocks.0.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.3.attentions.0.transformer_blocks.0.attn1", | |
| "up_blocks.3.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.3.attentions.2.transformer_blocks.0.attn1", | |
| # SD 1.5 U-Net (ldm) | |
| "input_blocks.1.1.transformer_blocks.0.attn1", | |
| "input_blocks.2.1.transformer_blocks.0.attn1", | |
| "output_blocks.9.1.transformer_blocks.0.attn1", | |
| "output_blocks.10.1.transformer_blocks.0.attn1", | |
| "output_blocks.11.1.transformer_blocks.0.attn1", | |
| # SD 1.5 VAE | |
| "decoder.mid_block.attentions.0", | |
| "decoder.mid.attn_1", | |
| ], | |
| 1: [ | |
| # SD 1.5 U-Net (diffusers) | |
| "down_blocks.1.attentions.0.transformer_blocks.0.attn1", | |
| "down_blocks.1.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.2.attentions.0.transformer_blocks.0.attn1", | |
| "up_blocks.2.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.2.attentions.2.transformer_blocks.0.attn1", | |
| # SD 1.5 U-Net (ldm) | |
| "input_blocks.4.1.transformer_blocks.0.attn1", | |
| "input_blocks.5.1.transformer_blocks.0.attn1", | |
| "output_blocks.6.1.transformer_blocks.0.attn1", | |
| "output_blocks.7.1.transformer_blocks.0.attn1", | |
| "output_blocks.8.1.transformer_blocks.0.attn1", | |
| ], | |
| 2: [ | |
| # SD 1.5 U-Net (diffusers) | |
| "down_blocks.2.attentions.0.transformer_blocks.0.attn1", | |
| "down_blocks.2.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.1.attentions.0.transformer_blocks.0.attn1", | |
| "up_blocks.1.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.1.attentions.2.transformer_blocks.0.attn1", | |
| # SD 1.5 U-Net (ldm) | |
| "input_blocks.7.1.transformer_blocks.0.attn1", | |
| "input_blocks.8.1.transformer_blocks.0.attn1", | |
| "output_blocks.3.1.transformer_blocks.0.attn1", | |
| "output_blocks.4.1.transformer_blocks.0.attn1", | |
| "output_blocks.5.1.transformer_blocks.0.attn1", | |
| ], | |
| 3: [ | |
| # SD 1.5 U-Net (diffusers) | |
| "mid_block.attentions.0.transformer_blocks.0.attn1", | |
| # SD 1.5 U-Net (ldm) | |
| "middle_block.1.transformer_blocks.0.attn1", | |
| ], | |
| } | |
| DEPTH_LAYERS = { | |
| // Add comments explaining the selection of layers for hypertiling | |
| # SD 1.5 U-Net (diffusers) | |
| "down_blocks.0.attentions.0.transformer_blocks.0.attn1", | |
| "down_blocks.0.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.3.attentions.0.transformer_blocks.0.attn1", | |
| "up_blocks.3.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.3.attentions.2.transformer_blocks.0.attn1", | |
| # SD 1.5 U-Net (ldm) | |
| "input_blocks.1.1.transformer_blocks.0.attn1", | |
| "input_blocks.2.1.transformer_blocks.0.attn1", | |
| "output_blocks.9.1.transformer_blocks.0.attn1", | |
| "output_blocks.10.1.transformer_blocks.0.attn1", | |
| "output_blocks.11.1.transformer_blocks.0.attn1", | |
| # SD 1.5 VAE | |
| "decoder.mid_block.attentions.0", | |
| "decoder.mid.attn_1", | |
| ], | |
| // Add comments explaining the selection of layers for hypertiling | |
| # SD 1.5 U-Net (diffusers) | |
| "down_blocks.1.attentions.0.transformer_blocks.0.attn1", | |
| "down_blocks.1.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.2.attentions.0.transformer_blocks.0.attn1", | |
| "up_blocks.2.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.2.attentions.2.transformer_blocks.0.attn1", | |
| # SD 1.5 U-Net (ldm) | |
| "input_blocks.4.1.transformer_blocks.0.attn1", | |
| "input_blocks.5.1.transformer_blocks.0.attn1", | |
| "output_blocks.6.1.transformer_blocks.0.attn1", | |
| "output_blocks.7.1.transformer_blocks.0.attn1", | |
| "output_blocks.8.1.transformer_blocks.0.attn1", | |
| ], | |
| // Add comments explaining the selection of layers for hypertiling | |
| # SD 1.5 U-Net (diffusers) | |
| "down_blocks.2.attentions.0.transformer_blocks.0.attn1", | |
| "down_blocks.2.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.1.attentions.0.transformer_blocks.0.attn1", | |
| "up_blocks.1.attentions.1.transformer_blocks.0.attn1", | |
| "up_blocks.1.attentions.2.transformer_blocks.0.attn1", | |
| # SD 1.5 U-Net (ldm) | |
| "input_blocks.7.1.transformer_blocks.0.attn1", | |
| "input_blocks.8.1.transformer_blocks.0.attn1", | |
| "output_blocks.3.1.transformer_blocks.0.attn1", | |
| "output_blocks.4.1.transformer_blocks.0.attn1", | |
| "output_blocks.5.1.transformer_blocks.0.attn1", | |
| ], | |
| // Add comments explaining the selection of layers for hypertiling | |
| # SD 1.5 U-Net (diffusers) | |
| "mid_block.attentions.0.transformer_blocks.0.attn1", | |
| # SD 1.5 U-Net (ldm) | |
| "middle_block.1.transformer_blocks.0.attn1", | |
| ], | |
| } |
| def cached_data_for_file(subsection, title, filename, func): | ||
| """ | ||
| Retrieves or generates data for a specific file, using a caching mechanism. | ||
|
|
||
| Parameters: | ||
| subsection (str): The subsection of the cache to use. | ||
| title (str): The title of the data entry in the subsection of the cache. | ||
| filename (str): The path to the file to be checked for modifications. | ||
| func (callable): A function that generates the data if it is not available in the cache. | ||
|
|
||
| Returns: | ||
| dict or None: The cached or generated data, or None if data generation fails. | ||
|
|
||
| The `cached_data_for_file` function implements a caching mechanism for data stored in files. | ||
| It checks if the data associated with the given `title` is present in the cache and compares the | ||
| modification time of the file with the cached modification time. If the file has been modified, | ||
| the cache is considered invalid and the data is regenerated using the provided `func`. | ||
| Otherwise, the cached data is returned. | ||
|
|
||
| If the data generation fails, None is returned to indicate the failure. Otherwise, the generated | ||
| or cached data is returned as a dictionary. | ||
| """ | ||
|
|
||
| existing_cache = cache(subsection) | ||
| ondisk_mtime = os.path.getmtime(filename) | ||
|
|
||
| entry = existing_cache.get(title) | ||
| if entry: | ||
| cached_mtime = entry.get("mtime", 0) | ||
| if ondisk_mtime > cached_mtime: | ||
| entry = None | ||
|
|
||
| if not entry or 'value' not in entry: | ||
| value = func() | ||
| if value is None: | ||
| return None | ||
|
|
||
| entry = {'mtime': ondisk_mtime, 'value': value} | ||
| existing_cache[title] = entry | ||
|
|
||
| dump_cache() | ||
|
|
||
| return entry['value'] |
There was a problem hiding this comment.
The cached_data_for_file function correctly implements a caching mechanism that checks for file modifications. However, consider adding error handling for the func() call to catch exceptions that might occur during data generation. This would improve the robustness of the caching mechanism by ensuring that transient errors in data generation do not cause the entire operation to fail.
try:
value = func()
if value is None:
return None
except Exception as e:
print(f'[ERROR] Failed to generate data for cache: {e}')
return NoneCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def cached_data_for_file(subsection, title, filename, func): | |
| """ | |
| Retrieves or generates data for a specific file, using a caching mechanism. | |
| Parameters: | |
| subsection (str): The subsection of the cache to use. | |
| title (str): The title of the data entry in the subsection of the cache. | |
| filename (str): The path to the file to be checked for modifications. | |
| func (callable): A function that generates the data if it is not available in the cache. | |
| Returns: | |
| dict or None: The cached or generated data, or None if data generation fails. | |
| The `cached_data_for_file` function implements a caching mechanism for data stored in files. | |
| It checks if the data associated with the given `title` is present in the cache and compares the | |
| modification time of the file with the cached modification time. If the file has been modified, | |
| the cache is considered invalid and the data is regenerated using the provided `func`. | |
| Otherwise, the cached data is returned. | |
| If the data generation fails, None is returned to indicate the failure. Otherwise, the generated | |
| or cached data is returned as a dictionary. | |
| """ | |
| existing_cache = cache(subsection) | |
| ondisk_mtime = os.path.getmtime(filename) | |
| entry = existing_cache.get(title) | |
| if entry: | |
| cached_mtime = entry.get("mtime", 0) | |
| if ondisk_mtime > cached_mtime: | |
| entry = None | |
| if not entry or 'value' not in entry: | |
| value = func() | |
| if value is None: | |
| return None | |
| entry = {'mtime': ondisk_mtime, 'value': value} | |
| existing_cache[title] = entry | |
| dump_cache() | |
| return entry['value'] | |
| def cached_data_for_file(subsection, title, filename, func): | |
| """ | |
| Retrieves or generates data for a specific file, using a caching mechanism. | |
| Parameters: | |
| subsection (str): The subsection of the cache to use. | |
| title (str): The title of the data entry in the subsection of the cache. | |
| filename (str): The path to the file to be checked for modifications. | |
| func (callable): A function that generates the data if it is not available in the cache. | |
| Returns: | |
| dict or None: The cached or generated data, or None if data generation fails. | |
| The `cached_data_for_file` function implements a caching mechanism for data stored in files. | |
| It checks if the data associated with the given `title` is present in the cache and compares the | |
| modification time of the file with the cached modification time. If the file has been modified, | |
| the cache is considered invalid and the data is regenerated using the provided `func`. | |
| Otherwise, the cached data is returned. | |
| If the data generation fails, None is returned to indicate the failure. Otherwise, the generated | |
| or cached data is returned as a dictionary. | |
| """ | |
| existing_cache = cache(subsection) | |
| ondisk_mtime = os.path.getmtime(filename) | |
| entry = existing_cache.get(title) | |
| if entry: | |
| cached_mtime = entry.get("mtime", 0) | |
| if ondisk_mtime > cached_mtime: | |
| entry = None | |
| if not entry or 'value' not in entry: | |
| try: | |
| value = func() | |
| if value is None: | |
| return None | |
| except Exception as e: | |
| print(f'[ERROR] Failed to generate data for cache: {e}') | |
| return None | |
| entry = {'mtime': ondisk_mtime, 'value': value} | |
| existing_cache[title] = entry | |
| dump_cache() | |
| return entry['value'] |
| def display(e: Exception, task, *, full_traceback=False): | ||
| record_exception() | ||
|
|
||
| print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr) | ||
| print(traceback.format_exc(), file=sys.stderr) | ||
| te = traceback.TracebackException.from_exception(e) | ||
| if full_traceback: | ||
| # include frames leading up to the try-catch block | ||
| te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack) | ||
| print(*te.format(), sep="", file=sys.stderr) |
There was a problem hiding this comment.
The display function's ability to handle full tracebacks and specific error messages is useful. However, the hardcoded error message related to model shape mismatch suggests a specific use case. Consider abstracting this to allow for more generic error handling or moving it to a more appropriate location related to model loading.
- if "copying a param with shape torch.Size([640, 1024]) from checkpoint, the shape in current model is torch.Size([640, 768])" in message:
+ if specific_condition_related_to_model_loading_error(message):Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation.
| def display(e: Exception, task, *, full_traceback=False): | |
| record_exception() | |
| print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr) | |
| print(traceback.format_exc(), file=sys.stderr) | |
| te = traceback.TracebackException.from_exception(e) | |
| if full_traceback: | |
| # include frames leading up to the try-catch block | |
| te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack) | |
| print(*te.format(), sep="", file=sys.stderr) | |
| def display(e: Exception, task, *, full_traceback=False): | |
| record_exception() | |
| print(f"{task or 'error'}: {type(e).__name__}", file=sys.stderr) | |
| te = traceback.TracebackException.from_exception(e) | |
| if full_traceback: | |
| # include frames leading up to the try-catch block | |
| te.stack = traceback.StackSummary(traceback.extract_stack()[:-2] + te.stack) | |
| print(*te.format(), sep="", file=sys.stderr) |
SD3 Lora support
…utton-log.csv- option to disable save button log.csv
fix upscale logic
activate venv .bat
add ids to the resize tabs in img2img
Feature: Beta scheduler
…e_linux update installation guide linux
…_on_non-job_actions [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job)
…as_jpeg fix OSError: cannot write mode P as JPEG
Robust sysinfo
…n_paste_inpaint do not send image size on paste inpaint
Fix noisy DS_Store files for MacOS
There was a problem hiding this comment.
Actionable comments posted: 52
Outside diff range, codebase verification and nitpick comments (25)
configs/alt-diffusion-m18-inference.yaml (5)
2-2: Specify the learning rate unit.It's good practice to specify the unit for the learning rate to avoid confusion.
base_learning_rate: 1.0e-04 # learning rate per sample
14-14: Clarify the comment.The comment should provide more context about why this parameter is different from the one trained before.
cond_stage_trainable: false # Note: different from the one we trained before because we are using a pre-trained model
24-24: Avoid using extremely large numbers.Using extremely large numbers can lead to unexpected behavior. Consider using a more reasonable value or explaining why such a large number is necessary.
cycle_lengths: [ 1000000 ] # large number to prevent corner cases
32-32: Remove unused parameter.The
image_sizeparameter is marked as unused and should be removed to avoid confusion.# image_size: 32 # unused
53-53: Ensure boolean values are consistent.Ensure that boolean values are consistently lowercase to avoid potential issues.
double_z: true # should be lowercaseextensions-builtin/Lora/extra_networks_lora.py (3)
9-10: Ensure proper documentation forself.errors.The
self.errorsdictionary should be properly documented to explain its purpose and usage.self.errors = {} """mapping of network names to the number of errors the network had during operation"""
24-27: Consider initializing lists outside the loop.Initializing lists outside the loop can improve readability and maintainability.
names = [] te_multipliers = [] unet_multipliers = [] dyn_dims = [] for params in params_list: assert params.items names.append(params.positional[0]) te_multiplier = float(params.positional[1]) if len(params.positional) > 1 else 1.0 te_multiplier = float(params.named.get("te", te_multiplier)) unet_multiplier = float(params.positional[2]) if len(params.positional) > 2 else te_multiplier unet_multiplier = float(params.named.get("unet", unet_multiplier)) dyn_dim = int(params.positional[3]) if len(params.positional) > 3 else None dyn_dim = int(params.named["dyn"]) if "dyn" in params.named else dyn_dim te_multipliers.append(te_multiplier) unet_multipliers.append(unet_multiplier) dyn_dims.append(dyn_dim)
47-56: Ensure conditional logic is clear and concise.The conditional logic for adding Lora hashes should be clear and concise.
if shared.opts.lora_add_hashes_to_infotext: if not getattr(p, "is_hr_pass", False) or not hasattr(p, "lora_hashes"): p.lora_hashes = {} for item in networks.loaded_networks: if item.network_on_disk.shorthash and item.mentioned_name: p.lora_hashes[item.mentioned_name.translate(self.remove_symbols)] = item.network_on_disk.shorthash if p.lora_hashes: p.extra_generation_params["Lora hashes"] = ', '.join(f'{k}: {v}' for k, v in p.lora_hashes.items())modules/devices.py (2)
15-16: Missing definition forxpu_specific.has_xpu.The function
xpu_specific.has_xpuis not defined anywhere in the codebase. This will result in a runtime error whenhas_xpuis called.
- Ensure that
xpu_specific.has_xpuis defined within thexpu_specificmodule or imported correctly.Analysis chain
Verify the definition and import of
xpu_specific.has_xpu.Ensure that
xpu_specific.has_xpuis correctly defined and imported to avoid runtime errors.
Verify the definition and import of
xpu_specific.has_xpu.Ensure that
xpu_specific.has_xpuis correctly defined and imported to avoid runtime errors.
Verify the definition of
xpu_specific.has_xpu.Ensure that
xpu_specific.has_xpuis correctly defined to avoid runtime errors.
Verify the definition of
xpu_specific.has_xpu.Ensure that
xpu_specific.has_xpuis correctly defined to avoid runtime errors.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the definition and import of `xpu_specific.has_xpu`. # Test: Search for the definition of `has_xpu` in `xpu_specific`. rg --type python -A 5 $'def has_xpu' | grep -B 5 'xpu_specific'Length of output: 93
Script:
#!/bin/bash # Description: Verify the definition and import of `xpu_specific.has_xpu`. # Test: Search for the definition of `has_xpu` in the codebase. rg 'def has_xpu' -A 5 # Test: Search for the import of `xpu_specific`. rg 'import xpu_specific' -A 5Length of output: 549
Script:
#!/bin/bash # Description: Verify the definition of `xpu_specific.has_xpu`. # Test: Search for the definition of `has_xpu` in the `xpu_specific` module. rg 'def has_xpu' -A 5 --glob 'modules/xpu_specific.py'Length of output: 54
Script:
#!/bin/bash # Description: Verify the definition of `xpu_specific.has_xpu`. # Test: Search for the definition of `has_xpu` across the entire codebase. rg 'def has_xpu' -A 5Length of output: 279
78-93: Missing import fornpu_specificinmodules/devices.pyThe device-specific garbage collection functions
torch_mps_gc,torch_xpu_gc, andtorch_npu_gcare correctly defined in their respective modules. The imports formac_specificandxpu_specificare present inmodules/devices.py. However, the import fornpu_specificis missing.
- Ensure to add the missing import for
npu_specificinmodules/devices.py.Analysis chain
Verify the definition and import of device-specific garbage collection functions.
Ensure that the device-specific garbage collection functions (
torch.cuda.empty_cache,mac_specific.torch_mps_gc,xpu_specific.torch_xpu_gc,npu_specific.torch_npu_gc) are correctly defined and imported to avoid runtime errors.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the definition and import of device-specific garbage collection functions. # Test: Search for the definition of device-specific garbage collection functions. rg --type python -A 5 $'torch.cuda.empty_cache' | grep -B 5 'mac_specific.torch_mps_gc' | grep -B 5 'xpu_specific.torch_xpu_gc' | grep -B 5 'npu_specific.torch_npu_gc'Length of output: 191
Script:
#!/bin/bash # Description: Verify the definition and import of device-specific garbage collection functions. # Test: Search for the definition and import of torch.cuda.empty_cache rg 'torch.cuda.empty_cache' --type py -A 5 # Test: Search for the definition and import of mac_specific.torch_mps_gc rg 'mac_specific.torch_mps_gc' --type py -A 5 # Test: Search for the definition and import of xpu_specific.torch_xpu_gc rg 'xpu_specific.torch_xpu_gc' --type py -A 5 # Test: Search for the definition and import of npu_specific.torch_npu_gc rg 'npu_specific.torch_npu_gc' --type py -A 5Length of output: 1253
Script:
#!/bin/bash # Description: Verify the definition and import of device-specific garbage collection functions. # Test: Search for the definition of mac_specific.torch_mps_gc rg 'def torch_mps_gc' --type py -A 5 # Test: Search for the definition of xpu_specific.torch_xpu_gc rg 'def torch_xpu_gc' --type py -A 5 # Test: Search for the definition of npu_specific.torch_npu_gc rg 'def torch_npu_gc' --type py -A 5 # Test: Search for the imports of mac_specific, xpu_specific, and npu_specific rg 'import mac_specific' --type py -A 5 rg 'import xpu_specific' --type py -A 5 rg 'import npu_specific' --type py -A 5Length of output: 1628
extensions-builtin/Lora/ui_edit_user_metadata.py (1)
59-69: Input validation missing insave_lora_user_metadataand related methodsThe
save_lora_user_metadatafunction and its related methods (get_user_metadataandwrite_user_metadata) do not perform input validation. This could lead to incorrect or malicious data being stored.
extensions-builtin/Lora/ui_edit_user_metadata.py: save_lora_user_metadatamodules/ui_extra_networks_user_metadata.py: get_user_metadatamodules/ui_extra_networks_user_metadata.py: write_user_metadataPlease ensure that input validation is added to these methods to prevent incorrect data storage.
Analysis chain
Verify the accuracy of input validation and metadata writing.
Ensure that the input validation and metadata writing are performed accurately to avoid incorrect data storage.
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of input validation and metadata writing. # Test: Search for the usage of `save_lora_user_metadata`. rg --type python -A 5 $'save_lora_user_metadata'Length of output: 78
Script:
#!/bin/bash # Description: Verify the accuracy of input validation and metadata writing. # Test: Search for the usage of `save_lora_user_metadata`. rg -A 5 $'save_lora_user_metadata'Length of output: 870
Script:
#!/bin/bash # Description: Verify the input validation for `save_lora_user_metadata`. # Test: Search for the definition and usage of `setup_save_handler`. rg -A 10 $'setup_save_handler'Length of output: 2590
Script:
#!/bin/bash # Description: Verify the handling and validation of data in `get_user_metadata` and `write_user_metadata`. # Test: Search for the definition of `get_user_metadata`. rg -A 10 $'def get_user_metadata' # Test: Search for the definition of `write_user_metadata`. rg -A 10 $'def write_user_metadata'Length of output: 2527
README.md (2)
18-18: Approved: Improved usability for Mac users.The instructions now include MacOS-specific key combinations, enhancing usability.
Correct the grammar issue.
Change "MacOS" to "macOS" for proper capitalization.
- - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a MacOS) to automatically adjust attention to selected text (code contributed by anonymous user) + - select text and press `Ctrl+Up` or `Ctrl+Down` (or `Command+Up` or `Command+Down` if you're on a macOS) to automatically adjust attention to selected text (code contributed by anonymous user)Tools
LanguageTool
[grammar] ~18-~18: The operating system from Apple is written “macOS”.
Context: ...nd+UporCommand+Down` if you're on a MacOS) to automatically adjust attention to s...(MAC_OS)
Markdownlint
18-18: Expected: 2; Actual: 4
Unordered list indentation(MD007, ul-indent)
107-112: Approved: Comprehensive installation steps for Windows 10/11 with NVidia GPUs.The addition provides a more comprehensive guide for users on that platform.
Ensure a blank line above the heading.
For better readability and adherence to Markdown standards, ensure there is a blank line above the heading.
+ ### Installation on Windows 10/11 with NVidia-GPUs using release packageTools
LanguageTool
[uncategorized] ~111-~111: Possible missing comma found.
Context: ...te.bat. 3. Runrun.bat`.For more details see [Install-and-Run-on-NVidia-GPUs](ht...
(AI_HYDRA_LEO_MISSING_COMMA)
extensions-builtin/Lora/networks.py (6)
150-155: Consider adding type hints to theBundledTIHashclass.Adding type hints can improve code readability and help with static type checking.
- def __init__(self, hash_str): + def __init__(self, hash_str: str):
273-278: Consider adding a log message when purging networks from memory.Adding a log message can help in debugging and understanding the memory management process.
- while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0: + while len(networks_in_memory) > shared.opts.lora_in_memory_limit and len(networks_in_memory) > 0: + logging.info(f"Purging network {name} from memory.")
369-373: Consider adding type hints to theallowed_layer_without_weightfunction.Adding type hints can improve code readability and help with static type checking.
- def allowed_layer_without_weight(layer): + def allowed_layer_without_weight(layer: torch.nn.Module) -> bool:
376-380: Consider adding type hints to thestore_weights_backupfunction.Adding type hints can improve code readability and help with static type checking.
- def store_weights_backup(weight): + def store_weights_backup(weight: torch.Tensor) -> torch.Tensor:
383-388: Consider adding type hints to therestore_weights_backupfunction.Adding type hints can improve code readability and help with static type checking.
- def restore_weights_backup(obj, field, weight): + def restore_weights_backup(obj: torch.nn.Module, field: str, weight: torch.Tensor) -> None:
391-408: Consider adding type hints to thenetwork_restore_weights_from_backupfunction.Adding type hints can improve code readability and help with static type checking.
- def network_restore_weights_from_backup(self): + def network_restore_weights_from_backup(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.GroupNorm, torch.nn.LayerNorm, torch.nn.MultiheadAttention]) -> None:extensions-builtin/soft-inpainting/scripts/soft_inpainting.py (1)
9-32: Consider adding type hints to theSoftInpaintingSettingsclass methods.Adding type hints can improve code readability and help with static type checking.
- def __init__(self, - mask_blend_power, - mask_blend_scale, - inpaint_detail_preservation, - composite_mask_influence, - composite_difference_threshold, - composite_difference_contrast): + def __init__(self, + mask_blend_power: float, + mask_blend_scale: float, + inpaint_detail_preservation: float, + composite_mask_influence: float, + composite_difference_threshold: float, + composite_difference_contrast: float):- def add_generation_params(self, dest): + def add_generation_params(self, dest: dict) -> None:modules/api/api.py (5)
58-73: Move import statements to the top of the file.For better readability and maintainability, move the import statements for
socketandurlparseto the top of the file.- import socket - from urllib.parse import urlparse
Line range hint
492-565:
Improve error handling and add detailed logging.Enhance error handling around the
process_imagescall and add more detailed logging to help with debugging.- processed = process_images(p) + try: + processed = process_images(p) + except Exception as e: + shared.state.end() + shared.total_tqdm.clear() + raise HTTPException(status_code=500, detail=str(e))
Line range hint
602-628:
Improve progress calculation.Ensure that the calculation of
progresshandles edge cases whereshared.state.job_countorshared.state.sampling_stepsmight be zero.- if shared.state.job_count > 0: - progress += shared.state.job_no / shared.state.job_count - if shared.state.sampling_steps > 0: - progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps + if shared.state.job_count > 0: + progress += shared.state.job_no / max(shared.state.job_count, 1) + if shared.state.sampling_steps > 0: + progress += 1 / max(shared.state.job_count, 1) * shared.state.sampling_step / max(shared.state.sampling_steps, 1)
Line range hint
630-647:
Improve error handling.Enhance error handling around the
decode_base64_to_imagecall to ensure robustness.- img = decode_base64_to_image(image_b64) + try: + img = decode_base64_to_image(image_b64) + except Exception as e: + raise HTTPException(status_code=500, detail=str(e))
Line range hint
807-823:
Improve error handling.Enhance error handling around the
train_embeddingcall to ensure robustness.- try: - embedding, filename = train_embedding(**args) # can take a long time to complete - except Exception as e: - error = e + try: + embedding, filename = train_embedding(**args) # can take a long time to complete + except Exception as e: + error = e + raise HTTPException(status_code=500, detail=f"Failed to train embedding: {e}")
Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Files selected for processing (31)
- .github/ISSUE_TEMPLATE/bug_report.yml (2 hunks)
- .gitignore (2 hunks)
- CHANGELOG.md (3 hunks)
- README.md (7 hunks)
- configs/alt-diffusion-inference.yaml (1 hunks)
- configs/alt-diffusion-m18-inference.yaml (1 hunks)
- configs/instruct-pix2pix.yaml (1 hunks)
- configs/sd3-inference.yaml (1 hunks)
- configs/sd_xl_inpaint.yaml (1 hunks)
- configs/v1-inference.yaml (1 hunks)
- configs/v1-inpainting-inference.yaml (1 hunks)
- extensions-builtin/LDSR/sd_hijack_ddpm_v1.py (22 hunks)
- extensions-builtin/Lora/extra_networks_lora.py (1 hunks)
- extensions-builtin/Lora/network.py (1 hunks)
- extensions-builtin/Lora/network_lora.py (1 hunks)
- extensions-builtin/Lora/networks.py (1 hunks)
- extensions-builtin/Lora/scripts/lora_script.py (1 hunks)
- extensions-builtin/Lora/ui_edit_user_metadata.py (1 hunks)
- extensions-builtin/Lora/ui_extra_networks_lora.py (1 hunks)
- extensions-builtin/hypertile/scripts/hypertile_script.py (1 hunks)
- extensions-builtin/soft-inpainting/scripts/soft_inpainting.py (1 hunks)
- javascript/contextMenus.js (1 hunks)
- javascript/dragdrop.js (2 hunks)
- javascript/imageviewer.js (3 hunks)
- javascript/progressbar.js (1 hunks)
- javascript/ui.js (4 hunks)
- modules/api/api.py (21 hunks)
- modules/call_queue.py (4 hunks)
- modules/cmd_args.py (3 hunks)
- modules/deepbooru.py (3 hunks)
- modules/devices.py (4 hunks)
Files skipped from review due to trivial changes (2)
- .gitignore
- configs/sd3-inference.yaml
Additional context used
Biome
javascript/imageviewer.js
[error] 36-36: Change to an optional chain.
Unsafe fix: Change to an optional chain.
(lint/complexity/useOptionalChain)
LanguageTool
README.md
[uncategorized] ~2-~2: You might be missing the article “the” here.
Context: ...for Stable Diffusion, implemented using Gradio library.
F...
(AI_EN_LECTOR_MISSING_DETERMINER_THE)
[grammar] ~18-~18: The operating system from Apple is written “macOS”.
Context: ...nd+UporCommand+Down` if you're on a MacOS) to automatically adjust attention to s...(MAC_OS)
[uncategorized] ~111-~111: Possible missing comma found.
Context: ...te.bat. 3. Runrun.bat`.For more details see [Install-and-Run-on-NVidia-GPUs](ht...
(AI_HYDRA_LEO_MISSING_COMMA)
[uncategorized] ~173-~173: You might be missing the article “the” here.
Context: ...ttings -> Licensesscreen, and also inhtml/licenses.html` file.
- Stable Di...
(AI_EN_LECTOR_MISSING_DETERMINER_THE)
Markdownlint
README.md
18-18: Expected: 2; Actual: 4
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CHANGELOG.md
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Unordered list indentation(MD007, ul-indent)
608-608: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
609-609: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
610-610: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
611-611: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
612-612: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
613-613: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
614-614: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
615-615: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
616-616: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
617-617: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
618-618: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
619-619: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
620-620: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
621-621: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
622-622: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
623-623: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
624-624: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
625-625: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
626-626: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
627-627: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
628-628: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
629-629: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
630-630: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
631-631: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
632-632: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
633-633: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
634-634: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
635-635: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
636-636: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
637-637: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
638-638: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
639-639: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
640-640: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
641-641: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
642-642: Expected: 2; Actual: 3
Unordered list indentation(MD007, ul-indent)
645-645: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
646-646: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
647-647: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
648-648: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
649-649: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
650-650: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
651-651: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
652-652: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
653-653: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
654-654: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
655-655: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
656-656: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
657-657: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
658-658: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
659-659: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
660-660: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
661-661: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
662-662: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
663-663: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
666-666: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
667-667: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
668-668: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
669-669: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
670-670: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
671-671: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
672-672: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
673-673: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
674-674: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
675-675: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
676-676: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
677-677: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
678-678: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
679-679: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
680-680: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
681-681: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
682-682: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
683-683: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
684-684: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
685-685: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
686-686: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
687-687: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
688-688: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
689-689: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
690-690: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
691-691: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
692-692: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
693-693: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
694-694: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
695-695: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
696-696: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
697-697: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
698-698: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
699-699: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
700-700: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
701-701: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
702-702: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
703-703: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
704-704: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
705-705: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
706-706: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
707-707: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
708-708: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
709-709: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
710-710: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
711-711: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
712-712: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
713-713: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
714-714: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
715-715: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
716-716: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
717-717: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
718-718: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
724-724: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
725-725: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
731-731: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
732-732: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
735-735: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
738-738: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
739-739: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
740-740: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
741-741: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
742-742: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
743-743: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
744-744: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
745-745: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
746-746: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
747-747: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
748-748: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
749-749: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
750-750: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
756-756: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
757-757: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
758-758: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
759-759: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
760-760: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
761-761: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
762-762: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
763-763: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
764-764: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
765-765: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
766-766: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
767-767: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
768-768: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
769-769: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
772-772: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
773-773: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
774-774: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
775-775: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
776-776: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
777-777: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
778-778: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
779-779: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
780-780: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
781-781: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
782-782: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
783-783: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
784-784: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
785-785: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
788-788: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
789-789: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
790-790: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
791-791: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
792-792: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
793-793: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
794-794: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
795-795: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
796-796: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
799-799: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
800-800: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
801-801: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
802-802: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
803-803: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
804-804: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
805-805: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
806-806: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
807-807: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
808-808: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
809-809: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
810-810: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
811-811: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
812-812: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
813-813: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
814-814: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
815-815: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
821-821: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
826-826: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
827-827: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
828-828: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
829-829: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
830-830: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
831-831: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
832-832: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
833-833: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
834-834: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
837-837: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
838-838: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
839-839: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
840-840: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
841-841: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
842-842: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
843-843: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
844-844: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
845-845: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
846-846: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
847-847: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
850-850: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
851-851: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
852-852: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
853-853: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
854-854: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
855-855: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
856-856: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
859-859: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
860-860: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
861-861: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
862-862: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
863-863: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
864-864: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
865-865: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
866-866: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
867-867: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
868-868: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
869-869: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
870-870: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
871-871: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
872-872: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
873-873: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
874-874: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
875-875: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
876-876: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
877-877: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
883-883: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
884-884: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
889-889: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
892-892: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
893-893: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
894-894: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
895-895: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
896-896: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
897-897: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
898-898: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
903-903: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
904-904: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
905-905: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
906-906: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
907-907: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
908-908: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
909-909: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
910-910: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
911-911: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
912-912: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
913-913: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
916-916: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
917-917: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
918-918: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
919-919: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
920-920: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
921-921: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
922-922: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
923-923: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
924-924: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
925-925: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
926-926: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
927-927: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
928-928: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
929-929: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
930-930: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
931-931: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
932-932: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
933-933: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
936-936: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
937-937: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
938-938: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
939-939: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
940-940: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
941-941: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
942-942: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
943-943: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
944-944: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
947-947: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
948-948: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
949-949: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
950-950: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
951-951: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
952-952: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
953-953: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
954-954: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
955-955: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
956-956: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
957-957: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
963-963: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
966-966: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
967-967: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
968-968: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
969-969: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
970-970: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
971-971: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
976-976: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
977-977: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
978-978: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
979-979: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
980-980: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
981-981: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
982-982: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
983-983: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
984-984: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
985-985: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
988-988: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
989-989: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
990-990: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
991-991: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
992-992: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
993-993: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
994-994: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
995-995: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
996-996: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
997-997: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
998-998: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1001-1001: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1002-1002: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1005-1005: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1006-1006: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1007-1007: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1008-1008: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1009-1009: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1010-1010: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1011-1011: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1012-1012: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1013-1013: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1014-1014: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1015-1015: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1020-1020: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1024-1024: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1025-1025: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1026-1026: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1027-1027: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1028-1028: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1029-1029: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1030-1030: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1031-1031: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1032-1032: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1033-1033: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
3-3: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
17-17: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
32-32: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
36-36: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
47-47: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
84-84: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
114-114: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
119-119: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
124-124: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
129-129: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
133-133: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
138-138: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
145-145: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
155-155: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
162-162: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
182-182: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
192-192: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
197-197: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
240-240: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
246-246: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
276-276: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
292-292: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
319-319: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
337-337: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
342-342: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
382-382: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
407-407: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
421-421: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
450-450: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
473-473: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
521-521: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
570-570: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
575-575: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
601-601: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
644-644: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
665-665: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
723-723: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
730-730: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
734-734: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
737-737: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
755-755: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
771-771: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
787-787: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
798-798: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
820-820: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
825-825: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
836-836: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
849-849: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
858-858: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
882-882: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
888-888: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
891-891: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
902-902: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
915-915: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
935-935: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
946-946: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
962-962: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
965-965: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
975-975: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
987-987: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1000-1000: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1004-1004: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1019-1019: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1023-1023: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
283-283: Expected: underscore; Actual: asterisk
Strong style(MD050, strong-style)
283-283: Expected: underscore; Actual: asterisk
Strong style(MD050, strong-style)
1042-1042: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1043-1043: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1044-1044: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1045-1045: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1046-1046: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1047-1047: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1048-1048: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1049-1049: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1050-1050: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1051-1051: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1052-1052: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1053-1053: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1056-1056: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1057-1057: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1055-1055: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1073-1073: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1074-1074: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1075-1075: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1076-1076: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
Additional comments not posted (113)
configs/v1-inference.yaml (1)
43-43: Review the implications of disabling checkpointing.The
use_checkpointparameter has been set toFalse. This change can reduce memory overhead during inference but might affect the ability to recover from certain states or utilize optimization strategies that rely on checkpoints. Ensure that this change aligns with the overall performance and resource management goals.configs/alt-diffusion-inference.yaml (1)
43-43: Review the implications of disabling checkpointing.The
use_checkpointparameter has been set toFalse. This change can reduce memory overhead during inference but might affect the ability to recover from certain states or utilize optimization strategies that rely on checkpoints. Ensure that this change aligns with the overall performance and resource management goals.configs/v1-inpainting-inference.yaml (1)
43-43: Review the implications of disabling checkpointing.The
use_checkpointparameter has been set toFalse. This change can reduce memory overhead during inference but might affect the ability to recover from certain states or utilize optimization strategies that rely on checkpoints. Ensure that this change aligns with the overall performance and resource management goals.configs/instruct-pix2pix.yaml (1)
48-48: Verify the impact of disabling checkpointing.Disabling checkpointing may affect the model's ability to save intermediate states. Ensure this change is intentional and does not impact training efficiency.
Verification successful
Verify the impact of disabling checkpointing.
Disabling checkpointing is a consistent change across multiple configuration files and is mentioned in the changelog as a performance improvement. This change appears intentional and aligns with the intended behavior for inference.
CHANGELOG.md: Mentions performance improvement withuse_checkpoint = Falsemodules/sub_quadratic_attention.py: Indicates checkpointing is recommended for training, not inferenceScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the impact of disabling checkpointing. # Test: Search for the usage of `use_checkpoint` in the codebase. Expect: Ensure that the change does not impact critical functionality. rg --type yaml -A 5 $'use_checkpoint:'Length of output: 2119
Script:
#!/bin/bash # Description: Search for the usage of `use_checkpoint` in the codebase to understand its impact. # Test: Search for the term `use_checkpoint` in all files to see how it is used and if there are any comments or documentation about it. rg 'use_checkpoint'Length of output: 1933
extensions-builtin/Lora/extra_networks_lora.py (2)
17-17: Clear errors at the start of the method.Clearing errors at the start of the method is a good practice to ensure a fresh state.
59-62: Ensure errors are properly reported and cleared.The method correctly reports and clears errors. Ensure this behavior is consistent with the rest of the application.
modules/deepbooru.py (2)
60-60: Ensure proper data typing for tensor creation.The addition of
devices.dtypein the tensor creation ensures that the tensor is explicitly cast to a specific data type, which can improve compatibility and performance.
81-81: Improved efficiency with set comprehension.Using a set comprehension directly for
filtertagsenhances readability and performance by avoiding the intermediate list creation.configs/sd_xl_inpaint.yaml (5)
1-5: Model configuration looks good.The
modelsection correctly specifies the target and parameters for the diffusion engine.
7-17: Denoiser configuration looks good.The
denoiser_configsection correctly specifies the target and parameters for the denoiser.
19-37: Network configuration looks good.The
network_configsection correctly specifies the target and parameters for the UNet model.
39-78: Conditioner configuration looks good.The
conditioner_configsection correctly specifies the target and parameters for the general conditioner.
80-98: First stage configuration looks good.The
first_stage_configsection correctly specifies the target and parameters for the autoencoder.extensions-builtin/Lora/ui_extra_networks_lora.py (5)
16-16: Generalized network handling inrefreshmethod.The
refreshmethod now callsnetworks.list_available_networks(), indicating a shift from handling Lora-specific networks to a more generalized approach.
18-76: Item creation logic looks good.The
create_itemmethod correctly constructs an item representation for a network with comprehensive metadata, including user metadata, activation text, preferred weight, and negative prompts.
79-84: Improved iteration inlist_itemsmethod.The
list_itemsmethod now instantiates a list of names to prevent concurrent modification issues during iteration and correctly creates items through thecreate_itemmethod.
87-87: Expanded directories for previews.The
allowed_directories_for_previewsmethod now returns an additional directory path (shared.cmd_opts.lyco_dir_backcompat), correctly expanding the directories from which previews can be generated.
89-90: Enhanced user interaction with metadata.The
create_user_metadata_editormethod correctly enhances user interaction with metadata by initializing aLoraUserMetadataEditor.extensions-builtin/Lora/network_lora.py (5)
25-33: LGTM!The
__init__method correctly initializes the class and creates the necessary modules.
34-69: LGTM!The
create_modulemethod correctly handles the creation of different types of layers and ensures weights are properly reshaped and assigned.
71-86: LGTM!The
calc_updownmethod correctly calculates the up and down weights and handles the mid-model if present.
88-92: LGTM!The
forwardmethod correctly moves the models to the device and performs the forward pass.
9-21: LGTM! But verify the class usage in the codebase.The class definition and method implementation look good.
However, ensure that the
ModuleTypeLoraclass is correctly integrated and used in the codebase.Verification successful
The
ModuleTypeLoraclass is correctly integrated and used in the codebase.
extensions-builtin/Lora/networks.py: The class is included in a list of module types.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the usage of `ModuleTypeLora` class in the codebase. # Test: Search for the class usage. Expect: Occurrences of `ModuleTypeLora`. rg --type python -A 5 $'ModuleTypeLora'Length of output: 69
Script:
#!/bin/bash # Description: Verify the usage of `ModuleTypeLora` class in the codebase. # Test: Search for the class usage. Expect: Occurrences of `ModuleTypeLora`. rg --type py -A 5 $'ModuleTypeLora'Length of output: 1046
extensions-builtin/Lora/scripts/lora_script.py (7)
15-16: LGTM!The
unloadfunction correctly calls theundomethod onnetworks.originals.
18-24: LGTM!The
before_uifunction correctly registers the Lora page and extra network with aliases.
53-59: LGTM!The
create_lora_jsonfunction correctly creates a JSON representation of the object.
62-69: LGTM!The
api_networksfunction correctly defines the API endpoints and their respective handlers.
77-97: LGTM!The
infotext_pastedfunction correctly processes the infotext and replaces Lora hashes with network aliases.
29-29: LGTM!The
on_model_loadedcallback correctly assigns network names to modules.
72-72: LGTM!The
on_app_startedcallback correctly registers theapi_networksfunction..github/ISSUE_TEMPLATE/bug_report.yml (8)
2-2: LGTM!The updated description is clear and concise.
7-12: LGTM!The instructions for the bug report title are clear and provide useful guidance.
15-31: LGTM!The updated checklist provides comprehensive steps for basic debugging and ensures that users perform necessary checks before submitting a bug report.
35-42: LGTM!The placeholder for the
What happened?section is clear and provides useful guidance.
49-52: LGTM!The placeholder for the
Steps to reproduce the problemsection is clear and provides useful guidance.
60-62: LGTM!The placeholder for the
What should have happened?section is clear and provides useful guidance.
80-87: LGTM!The instructions for the
Sysinfosection are clear and ensure that users include essential system context in their bug reports.
94-105: LGTM!The instructions for the
Console logssection and the placeholder for theAdditional informationsection are clear and provide useful guidance.modules/call_queue.py (3)
50-62: LGTM! The state management improvements are crucial.The changes ensure that the state is consistently reset after the function call.
Line range hint
22-48:
LGTM! But verify the function usage in the codebase.The code changes are approved.
However, ensure that all function calls to
wrap_gradio_gpu_callare correctly implemented.Verification successful
Function usage verified.
The
wrap_gradio_gpu_callfunction is correctly implemented and used across multiple modules:
modules/ui_postprocessing.pymodules/ui.pymodules/ui_checkpoint_merger.pymodules/ui_extensions.pyScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify all function calls to `wrap_gradio_gpu_call` are correctly implemented. # Test: Search for the function usage. Expect: Correct implementation of function calls. rg --type python -A 5 $'wrap_gradio_gpu_call'Length of output: 75
Script:
#!/bin/bash # Description: Verify all function calls to `wrap_gradio_gpu_call` are correctly implemented. # Test: Search for the function usage. Expect: Correct implementation of function calls. rg --type py -A 5 $'wrap_gradio_gpu_call'Length of output: 4782
Line range hint
65-159:
LGTM! The enhancements improve error handling and profiling.The changes improve error reporting, memory monitoring, and profiling capabilities.
However, ensure that all function calls to
wrap_gradio_call_no_jobare correctly implemented.javascript/dragdrop.js (5)
3-5: LGTM! The function correctly validates the image file list.The function checks if the file list contains exactly one image of the specified types.
Line range hint
7-49:
LGTM! The function handles image replacement robustly.The changes ensure that the callback is executed correctly after the fetch completes, improving the robustness of the function.
51-57: LGTM! The function encapsulates file presence checks.The function improves code clarity by encapsulating the logic for file presence checks.
59-64: LGTM! The function correctly validates URLs.The function uses a try-catch block to validate URLs, ensuring that only valid URLs are accepted.
68-72: LGTM! The function correctly identifies prompt targets.The function checks the target's placeholder and class name to determine if it is a prompt.
javascript/contextMenus.js (6)
2-114: LGTM! The function restructuring enhances readability and maintainability.The changes improve scoping clarity and maintainability.
116-120: LGTM! The variable assignments improve scoping clarity.The changes ensure better scoping clarity by using
var.
123-136: LGTM! The function restructuring promotes code reuse and clarity.The changes ensure better code reuse and clarity.
138-140: LGTM! The function restructuring promotes code reuse and clarity.The changes ensure better code reuse and clarity.
142-144: LGTM! The function restructuring promotes code reuse and clarity.The changes ensure better code reuse and clarity.
151-153: LGTM! The function restructuring promotes code reuse and clarity.The changes ensure better code reuse and clarity.
javascript/progressbar.js (11)
3-4: Empty function detected.The
rememberGallerySelectionfunction is currently empty. Verify if this is intentional or if the implementation is missing.
7-8: Empty function detected.The
getGallerySelectedIndexfunction is currently empty. Verify if this is intentional or if the implementation is missing.
34-35: LGTM!The
pad2function is simple and correct.
38-44: LGTM!The
formatTimefunction is well-structured and correctly handles different time ranges.
55-63: LGTM!The
setTitlefunction correctly updates the document title based on the provided progress.
68-69: LGTM!The
randomIdfunction is simple and correct.
81-88: LGTM!The
requestWakeLockfunction is well-structured and correctly handles wake lock requests.
90-98: LGTM!The
releaseWakeLockfunction is well-structured and correctly handles wake lock release.
111-121: LGTM!The
removeProgressBarfunction correctly handles the cleanup of progress bar elements and wake lock release.
123-212: LGTM!The
funProgressandfunLivePreviewfunctions are well-structured and correctly handle progress updates and live previews.
75-212: LGTM! But verify the function usage in the codebase.The code changes are approved.
However, ensure that all function calls to
requestProgressmatch the new signature.extensions-builtin/hypertile/scripts/hypertile_script.py (8)
8-9: LGTM!The
titlemethod is simple and correct.
11-12: LGTM!The
showmethod is simple and correct.
14-19: LGTM!The
processmethod is well-structured and correctly handles the hypertile configuration and infotext addition.
21-34: LGTM!The
before_hrmethod is well-structured and correctly handles the hypertile configuration for the second pass.
36-54: LGTM!The
add_infotextmethod is well-structured and correctly handles the addition of hypertile configuration details.
57-77: LGTM!The
configure_hypertilefunction is well-structured and correctly handles the hypertile model configuration.
80-104: LGTM!The
on_ui_settingsfunction is well-structured and correctly handles the addition of hypertile options to the UI settings.
106-118: LGTM!The
add_axis_optionsfunction is well-structured and correctly handles the addition of hypertile options to the axis options.extensions-builtin/Lora/network.py (3)
61-69: LGTM!The
detect_versionmethod is well-structured and correctly handles version detection.
71-77: LGTM!The
set_hashmethod is well-structured and correctly handles hash setting.
79-82: LGTM!The
read_hashmethod is well-structured and correctly handles hash reading.modules/devices.py (7)
Line range hint
43-48:
Verify command-line options parsing.Ensure that the command-line options are correctly parsed to avoid incorrect device string retrieval.
Verification successful
Command-line options parsing verified successfully.
The usage of
shared.cmd_opts.device_idacross multiple files (modules/xpu_specific.py,modules/npu_specific.py, andmodules/devices.py) confirms that the command-line options are correctly parsed and used for retrieving device strings.
modules/xpu_specific.pymodules/npu_specific.pymodules/devices.pyScripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify command-line options parsing. # Test: Search for the usage of `shared.cmd_opts.device_id`. rg --type python -A 5 $'shared.cmd_opts.device_id'Length of output: 80
Script:
#!/bin/bash # Description: Verify command-line options parsing. # Test: Search for the usage of `shared.cmd_opts.device_id`. rg --type py -A 5 'shared.cmd_opts.device_id'Length of output: 1390
187-207: Verify the accuracy of dtype casting application and reversion.Ensure that the dtype casting is applied and reverted accurately to avoid precision loss or incorrect results.
Verification successful
Verify the accuracy of dtype casting application and reversion.
The dtype casting is applied and reverted accurately within the
manual_castfunction. The original forward method is restored in thefinallyblock, ensuring no precision loss or incorrect results.
modules/devices.py: Themanual_castfunction is used to apply and revert dtype casting correctly.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of dtype casting application and reversion. # Test: Search for the usage of `manual_cast`. rg --type python -A 5 $'manual_cast'Length of output: 66
Script:
#!/bin/bash # Description: Verify the accuracy of dtype casting application and reversion. # Test: Search for the usage of `manual_cast`. rg --type py -A 5 $'manual_cast'Length of output: 1609
26-32: Verify the accuracy of device ID retrieval and capability check.Ensure that the device ID retrieval and capability check are accurate to avoid incorrect autocasting behavior.
Verification successful
Verification successful.
The device ID retrieval and capability check in the
cuda_no_autocastfunction are accurate.
- The
get_cuda_device_idfunction correctly retrieves the device ID.- The
torch.cuda.get_device_capabilityfunction is used appropriately to check the device's capabilities.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of device ID retrieval and capability check. # Test: Search for the usage of `get_cuda_device_id` and `torch.cuda.get_device_capability`. rg --type python -A 5 $'get_cuda_device_id' | grep -B 5 'torch.cuda.get_device_capability'Length of output: 118
Script:
#!/bin/bash # Description: Verify the accuracy of device ID retrieval and capability check. # Test: Search for the usage of `get_cuda_device_id` and `torch.cuda.get_device_capability`. rg --glob '*.py' -A 5 'get_cuda_device_id' | grep -B 5 'torch.cuda.get_device_capability'Length of output: 260
Script:
#!/bin/bash # Description: Locate the implementation of `get_cuda_device_id` and verify its correctness. # Search for the implementation of `get_cuda_device_id`. rg 'def get_cuda_device_id' -A 5 --glob '*.py'Length of output: 381
284-295: Verify the accuracy of casting prevention.Ensure that the casting prevention is performed accurately to avoid precision loss or incorrect results.
Verification successful
Verify the accuracy of casting prevention.
Ensure that the casting prevention is performed accurately to avoid precision loss or incorrect results.
Verify the accuracy of casting prevention.
Ensure that the casting prevention is performed accurately to avoid precision loss or incorrect results.
Casting prevention is correctly implemented.
The
force_model_fp16function is invoked correctly whencmd_opts.precisionis set to "half", ensuring that the casting prevention is performed accurately.
modules/shared_init.py: Lines around the usage offorce_model_fp16confirm the correct implementation.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of casting prevention. # Test: Search for the usage of `force_model_fp16`. rg --type python -A 5 $'force_model_fp16'Length of output: 71
Script:
#!/bin/bash # Description: Verify the accuracy of casting prevention. # Test: Search for the usage of `force_model_fp16`. rg -t py -A 5 $'force_model_fp16'Length of output: 693
Script:
#!/bin/bash # Description: Retrieve more context around the usage of `force_model_fp16` in `modules/shared_init.py`. # Test: Search for the usage of `force_model_fp16` in `modules/shared_init.py` with more context lines. rg -A 10 -B 10 'force_model_fp16' modules/shared_init.pyLength of output: 982
152-184: Verify the accuracy of dtype casting and restoration of original dtype.Ensure that the dtype casting is performed accurately and that the original dtype is restored after inference to avoid precision loss or incorrect results.
Verification successful
The dtype casting and restoration of the original dtype are handled correctly in the
manual_cast_forwardfunction.
- The
manual_cast_forwardfunction is applied to theforwardmethod ofmodule_type.- The original
forwardmethod is stored inmodule_type.org_forward.- The original dtype is restored after inference in the
finallyblock.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of dtype casting and restoration of original dtype. # Test: Search for the usage of `manual_cast_forward`. rg --type python -A 5 $'manual_cast_forward'Length of output: 74
Script:
#!/bin/bash # Description: Verify the accuracy of dtype casting and restoration of original dtype. # Test: Search for the usage of `manual_cast_forward`. rg --type py -A 5 $'manual_cast_forward'Length of output: 780
Script:
#!/bin/bash # Description: Verify the accuracy of dtype casting and restoration of original dtype. # Test: Search for the definition and usage of `module_type`. rg --type py -A 10 $'module_type'Length of output: 3642
268-281: Verify the accuracy of memory allocation and layer initialization.Ensure that the memory allocation and layer initialization are performed accurately to avoid unexpected latency or memory issues.
Verification successful
Verification successful: Memory allocation and layer initialization are performed accurately.
- The
first_time_calculationfunction is correctly invoked during the initialization process inmodules/initialize.py.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of memory allocation and layer initialization. # Test: Search for the usage of `first_time_calculation`. rg --type python -A 5 $'first_time_calculation'Length of output: 77
Script:
#!/bin/bash # Description: Verify the accuracy of memory allocation and layer initialization. # Find all Python files and search for the usage of `first_time_calculation`. fd -e py -x rg 'first_time_calculation' {}Length of output: 116
Script:
#!/bin/bash # Description: Verify the accuracy of memory allocation and layer initialization. # Find all Python files and search for the usage of `first_time_calculation`. fd -e py | xargs rg 'first_time_calculation'Length of output: 157
Script:
#!/bin/bash # Description: Verify the accuracy of memory allocation and layer initialization. # Extract relevant lines from modules/initialize.py where first_time_calculation is used. rg -A 10 'first_time_calculation' modules/initialize.pyLength of output: 453
35-40: Verify command-line options parsing andtorch.cuda.current_device()return value.Ensure that the command-line options are correctly parsed and that
torch.cuda.current_device()returns the expected value.extensions-builtin/Lora/ui_edit_user_metadata.py (6)
117-136: Verify the accuracy of metadata retrieval and component population.Ensure that the metadata retrieval and component population are accurate to avoid incorrect display of metadata in UI components.
11-14: Verify the input type and average length calculation.Ensure that the input
tagsis a dictionary and that the average length calculation is accurate to avoid incorrect results.Verification successful
The input type and average length calculation are correct.
The function
is_non_comma_tagsetcorrectly expects a dictionary as input and accurately calculates the average length of the keys. The usage context confirms this.
- File:
extensions-builtin/Lora/ui_edit_user_metadata.py- Lines: 11-14
Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the input type and average length calculation. # Test: Search for the usage of `is_non_comma_tagset`. rg --type python -A 5 $'is_non_comma_tagset'Length of output: 74
Script:
#!/bin/bash # Description: Verify the input type and average length calculation for the function `is_non_comma_tagset`. # Search for the function definition and its usage in the codebase. rg 'def is_non_comma_tagset' -A 10 rg 'is_non_comma_tagset' -A 5Length of output: 1789
70-115: Verify the accuracy of metadata retrieval and table construction.Ensure that the metadata retrieval and table construction are accurate to avoid incorrect display of metadata.
Verification successful
The metadata retrieval and table construction logic in the
get_metadata_tablefunction appears to be accurate.The function correctly retrieves metadata, formats it, and constructs an HTML table for display. The usage of this function in the
put_values_into_componentsmethod further confirms its correctness.
modules/ui_extra_networks_user_metadata.py:get_metadata_tableis used to generate metadata parameters which are then used to construct an HTML table.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of metadata retrieval and table construction. # Test: Search for the usage of `get_metadata_table`. rg --type python -A 5 $'get_metadata_table'Length of output: 73
Script:
#!/bin/bash # Description: Verify the accuracy of metadata retrieval and table construction. # Test: Search for the usage of `get_metadata_table`. rg --type py -A 5 $'get_metadata_table'Length of output: 1693
Script:
#!/bin/bash # Description: Verify the accuracy of metadata retrieval and table construction. # Test: Search for all occurrences of `get_metadata_table` in `modules/ui_extra_networks_user_metadata.py`. rg 'get_metadata_table' modules/ui_extra_networks_user_metadata.pyLength of output: 158
Script:
#!/bin/bash # Description: Verify the accuracy of metadata retrieval and table construction. # Test: Search for the surrounding lines of code where `get_metadata_table` is called in `modules/ui_extra_networks_user_metadata.py`. rg -A 10 -B 10 'get_metadata_table' modules/ui_extra_networks_user_metadata.pyLength of output: 1669
21-45: Verify the accuracy of metadata parsing and tag frequency calculation.Ensure that the metadata parsing and tag frequency calculation are accurate to avoid incorrect tag construction.
138-144: Verify the accuracy of metadata retrieval and prompt generation.Ensure that the metadata retrieval and prompt generation are accurate to avoid incorrect prompt generation.
Verification successful
Verify the accuracy of metadata retrieval and prompt generation.
The metadata retrieval and prompt generation processes appear to be correctly implemented. The
build_tagsfunction extracts tag frequencies from the metadata, and thegenerate_random_prompt_from_tagsfunction uses these tags to generate a random prompt.
build_tags(metadata): Extracts tag frequencies from the metadata.generate_random_prompt_from_tags(tags): Generates a random prompt based on the tags.Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash # Description: Verify the accuracy of metadata retrieval and prompt generation. # Test: Search for the usage of `generate_random_prompt`. rg --type python -A 5 $'generate_random_prompt'Length of output: 77
Script:
#!/bin/bash # Description: Verify the accuracy of metadata retrieval and prompt generation. # Test: Search for the usage of `generate_random_prompt`. rg --type py -A 5 $'generate_random_prompt'Length of output: 2329
Script:
#!/bin/bash # Description: Verify the accuracy of metadata retrieval and prompt generation by reviewing related functions. # Search for the definition of the `build_tags` function. rg --type py -A 5 $'def build_tags' # Search for the definition of the `generate_random_prompt_from_tags` function. rg --type py -A 10 $'def generate_random_prompt_from_tags'Length of output: 1525
145-158: Verify the accuracy of tag processing and prompt generation.Ensure that the tag processing and prompt generation are accurate to avoid incorrect prompt generation.
javascript/imageviewer.js (1)
8-27: Verify the accuracy of modal element updates and display.Ensure that the modal elements are correctly updated and displayed to avoid incorrect modal behavior.
README.md (4)
2-2: Approved: Improved clarity in the description.The updated description provides a clearer understanding of the technology used.
Tools
LanguageTool
[uncategorized] ~2-~2: You might be missing the article “the” here.
Context: ...for Stable Diffusion, implemented using Gradio library.
F...
(AI_EN_LECTOR_MISSING_DETERMINER_THE)
81-81: Approved: Enhanced user experience.The addition of a separate UI for choosing embeddings, hypernetworks, or Loras with preview improves the user experience.
94-94: Approved: Added support for Segmind Stable Diffusion.This change enhances functionality and compatibility with different models.
97-101: Approved: Improved installation instructions.The reorganization of installation instructions aids users in quickly locating relevant information based on their hardware.
javascript/ui.js (2)
22-22: Approved: Simplified code usingfindmethod.The refactored function improves readability and simplifies the code.
116-128: Approved: Prevents unnecessary delays.The function ensures that the image gallery is not sent, preventing unnecessary delays.
modules/cmd_args.py (5)
14-14: Approved: Corrected help text.The corrected help text improves user understanding.
22-28: Approved: Enhanced robustness withnormalized_filepath.The change ensures that file paths are validated and normalized, enhancing robustness.
45-45: Approved: Enhanced precision options.The inclusion of "half" as a choice for the
--precisionargument reflects an enhancement in the precision options available for model evaluation.
82-82: Approved: Clarified help text.The updated help text improves user understanding by clarifying the functionality.
88-88: Approved: Enhanced robustness withnormalized_filepath.The change ensures that the file path is validated and normalized, enhancing robustness.
modules/api/api.py (8)
567-575: LGTM!The code changes are approved.
Line range hint
577-586:
LGTM!The code changes are approved.
771-774: LGTM!The code changes are approved.
776-778: LGTM!The code changes are approved.
779-781: LGTM!The code changes are approved.
917-919: LGTM!The code changes are approved.
920-923: LGTM!The code changes are approved.
925-928: LGTM!The code changes are approved.
extensions-builtin/LDSR/sd_hijack_ddpm_v1.py (6)
51-51: Correct default value forignore_keysparameter.Using
Noneas the default value forignore_keysis appropriate for optional parameters.
103-103: Ensureignore_keysis correctly handled.The
ignore_keysparameter is conditionally replaced with an empty list. This ensures that the method can handle cases where no keys are provided.
185-191: Handleignore_keysparameter correctly ininit_from_ckpt.The iteration over
ignore_keysis modified to handle the case where it might beNone, preventing potential runtime errors.
447-447: Correct default value forignore_keysparameter.Using
Noneas the default value forignore_keysis appropriate for optional parameters.
1424-1424: Ensure correct parameter passing inlog_images.The
log_imagesmethod correctly passes parameters to the superclass method.
1440-1443: Simplify setting attributes directly.Using direct attribute setting instead of
setattrsimplifies the code and improves readability.
| function request(url, data, handler, errorHandler) { | ||
| var xhr = new XMLHttpRequest(); | ||
| var url = url; | ||
| xhr.open("POST", url, true); | ||
| xhr.setRequestHeader("Content-Type", "application/json"); | ||
| xhr.onreadystatechange = function () { | ||
| xhr.onreadystatechange = function() { | ||
| if (xhr.readyState === 4) { | ||
| if (xhr.status === 200) { | ||
| try { | ||
| var js = JSON.parse(xhr.responseText); | ||
| handler(js) | ||
| handler(js); | ||
| } catch (error) { | ||
| console.error(error); | ||
| errorHandler() | ||
| errorHandler(); | ||
| } | ||
| } else{ | ||
| errorHandler() | ||
| } else { | ||
| errorHandler(); | ||
| } | ||
| } | ||
| }; | ||
| var js = JSON.stringify(data); | ||
| xhr.send(js); | ||
| } |
There was a problem hiding this comment.
Consider using the fetch API for better readability and maintainability.
The request function can be refactored to use the fetch API, which provides a more concise and promise-based approach to handling HTTP requests.
async function request(url, data, handler, errorHandler) {
try {
const response = await fetch(url, {
method: 'POST',
headers: { 'Content-Type': 'application/json' },
body: JSON.stringify(data),
});
if (!response.ok) throw new Error(`HTTP error! status: ${response.status}`);
const js = await response.json();
handler(js);
} catch (error) {
console.error('Request failed:', error);
errorHandler();
}
}| function switchWidthHeight(tabname) { | ||
| var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]"); | ||
| var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]"); | ||
| if (!width || !height) return []; | ||
|
|
||
| var tmp = width.value; | ||
| width.value = height.value; | ||
| height.value = tmp; | ||
|
|
||
| updateInput(width); | ||
| updateInput(height); | ||
| return []; | ||
| } |
There was a problem hiding this comment.
Add validation for input values.
Ensure that the input values are within acceptable ranges before swapping.
function switchWidthHeight(tabname) {
var width = gradioApp().querySelector("#" + tabname + "_width input[type=number]");
var height = gradioApp().querySelector("#" + tabname + "_height input[type=number]");
if (!width || !height) return [];
var tmp = width.value;
width.value = height.value;
height.value = tmp;
updateInput(width);
updateInput(height);
return [];
}Committable suggestion was skipped due to low confidence.
| * Added pption to run torch profiler for image generation | ||
|
|
||
| ### Bug Fixes: | ||
| * Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958)) |
There was a problem hiding this comment.
Add a blank line after the 'Bug Fixes' section heading for consistency.
Ensure there is a blank line after the heading for clarity and consistency.
### Bug Fixes
Committable suggestion was skipped due to low confidence.
| * Add process_before_every_sampling hook ([#15984](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15984)) | ||
| * Return HTTP 400 instead of 404 on invalid sampler error ([#16140](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16140)) | ||
|
|
||
| ### Performance: |
There was a problem hiding this comment.
Remove trailing punctuation from the heading.
The heading should not have trailing punctuation.
### Performance:
### PerformanceCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ### Performance: | |
| ### Performance |
Tools
Markdownlint
36-36: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
| * Check for nans in unet only once, after all steps have been completed | ||
| * Added pption to run torch profiler for image generation | ||
|
|
||
| ### Bug Fixes: |
There was a problem hiding this comment.
Remove trailing punctuation from the heading.
The heading should not have trailing punctuation.
### Bug Fixes:
### Bug FixesCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| ### Bug Fixes: | |
| ### Bug Fixes |
Tools
Markdownlint
47-47: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
| def processing_uses_inpainting(p): | ||
| # TODO: Figure out a better way to determine if inpainting is being used by p | ||
| if getattr(p, "image_mask", None) is not None: | ||
| return True | ||
|
|
||
| if getattr(p, "mask", None) is not None: | ||
| return True | ||
|
|
||
| if getattr(p, "nmask", None) is not None: | ||
| return True | ||
|
|
||
| return False |
There was a problem hiding this comment.
Simplify the processing_uses_inpainting function.
The function uses multiple getattr calls to check for the presence of attributes. This approach is correct but could be simplified using a loop over the attribute names.
- if getattr(p, "image_mask", None) is not None:
- return True
- if getattr(p, "mask", None) is not None:
- return True
- if getattr(p, "nmask", None) is not None:
- return True
+ for attr in ["image_mask", "mask", "nmask"]:
+ if getattr(p, attr, None) is not None:
+ return TrueCommittable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def processing_uses_inpainting(p): | |
| # TODO: Figure out a better way to determine if inpainting is being used by p | |
| if getattr(p, "image_mask", None) is not None: | |
| return True | |
| if getattr(p, "mask", None) is not None: | |
| return True | |
| if getattr(p, "nmask", None) is not None: | |
| return True | |
| return False | |
| def processing_uses_inpainting(p): | |
| # TODO: Figure out a better way to determine if inpainting is being used by p | |
| for attr in ["image_mask", "mask", "nmask"]: | |
| if getattr(p, attr, None) is not None: | |
| return True | |
| return False |
| def latent_blend(settings, 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. | ||
|
|
||
| if len(t.shape) == 3: | ||
| # [4][w][h] to [1][4][w][h] | ||
| t2 = t.unsqueeze(0) | ||
| # [4][w][h] to [1][1][w][h] - the [4] seem redundant. | ||
| t3 = t[0].unsqueeze(0).unsqueeze(0) | ||
| else: | ||
| t2 = t | ||
| t3 = t[:, 0][:, None] | ||
|
|
||
| one_minus_t2 = 1 - t2 | ||
| one_minus_t3 = 1 - t3 | ||
|
|
||
| # Linearly interpolate the image vectors. | ||
| a_scaled = a * one_minus_t2 | ||
| b_scaled = b * t2 | ||
| image_interp = a_scaled | ||
| image_interp.add_(b_scaled) | ||
| result_type = image_interp.dtype | ||
| del a_scaled, b_scaled, t2, one_minus_t2 | ||
|
|
||
| # 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, keepdim=True).to(float64(image_interp)).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, keepdim=True).to(float64(a)).pow_(settings.inpaint_detail_preservation) * one_minus_t3 | ||
| b_magnitude = torch.norm(b, p=2, dim=1, keepdim=True).to(float64(b)).pow_(settings.inpaint_detail_preservation) * t3 | ||
| desired_magnitude = a_magnitude | ||
| desired_magnitude.add_(b_magnitude).pow_(1 / settings.inpaint_detail_preservation) | ||
| del a_magnitude, b_magnitude, t3, one_minus_t3 | ||
|
|
||
| # 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 |
There was a problem hiding this comment.
Review tensor operations for potential optimizations and add detailed comments.
The function performs complex tensor operations for blending latent image representations. While the logic appears sound, there are several areas where performance could be improved, such as minimizing the use of inplace operations and ensuring tensor operations are efficiently combined. Additionally, adding comments to explain the rationale behind complex operations would greatly enhance maintainability.
- # NOTE: We use inplace operations wherever possible.
+ # NOTE: We use inplace operations wherever possible to save memory.Committable suggestion was skipped due to low confidence.
| def get_modified_nmask(settings, nmask, sigma): | ||
| """ | ||
| Converts a negative mask representing the transparency of the original latent vectors being overlaid | ||
| 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 ** settings.mask_blend_power) * settings.mask_blend_scale) |
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Add input validation or documentation comments to get_modified_nmask.
The function uses a power operation to adjust the mask based on the settings. It's important to ensure that the sigma and mask_blend_power values are within expected ranges to avoid unexpected behavior. Adding input validation or comments to specify the expected range could improve code robustness.
- return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale)
+ # Ensure sigma and mask_blend_power are within expected ranges.
+ assert sigma > 0, "sigma must be positive"
+ assert settings.mask_blend_power > 0, "mask_blend_power must be positive"
+ return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale)Committable suggestion
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| def get_modified_nmask(settings, nmask, sigma): | |
| """ | |
| Converts a negative mask representing the transparency of the original latent vectors being overlaid | |
| 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 ** settings.mask_blend_power) * settings.mask_blend_scale) | |
| def get_modified_nmask(settings, nmask, sigma): | |
| """ | |
| Converts a negative mask representing the transparency of the original latent vectors being overlaid | |
| 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 | |
| # Ensure sigma and mask_blend_power are within expected ranges. | |
| assert sigma > 0, "sigma must be positive" | |
| assert settings.mask_blend_power > 0, "mask_blend_power must be positive" | |
| return torch.pow(nmask, (sigma ** settings.mask_blend_power) * settings.mask_blend_scale) |
| def apply_masks( | ||
| settings, | ||
| nmask, | ||
| overlay_images, | ||
| width, height, | ||
| paste_to): | ||
| import torch | ||
| import modules.processing as proc | ||
| import modules.images as images | ||
| from PIL import Image, ImageOps, ImageFilter | ||
|
|
||
| converted_mask = nmask[0].float() | ||
| converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2) | ||
| converted_mask = 255. * converted_mask | ||
| converted_mask = converted_mask.cpu().numpy().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, | ||
| (width, height), | ||
| paste_to) | ||
|
|
||
| masks_for_overlay = [] | ||
|
|
||
| for i, overlay_image in enumerate(overlay_images): | ||
| 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') | ||
|
|
||
| return masks_for_overlay |
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Review image and tensor operations for potential optimizations.
The function performs complex image processing operations. While the structure is clear, there are opportunities to optimize the operations, especially in terms of reducing the number of operations performed on tensors and images. Ensuring that all image and tensor operations are batched where possible could improve performance.
- converted_mask = nmask[0].float()
+ converted_mask = nmask[0].float().clone()Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def apply_masks( | |
| settings, | |
| nmask, | |
| overlay_images, | |
| width, height, | |
| paste_to): | |
| import torch | |
| import modules.processing as proc | |
| import modules.images as images | |
| from PIL import Image, ImageOps, ImageFilter | |
| converted_mask = nmask[0].float() | |
| converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2) | |
| converted_mask = 255. * converted_mask | |
| converted_mask = converted_mask.cpu().numpy().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, | |
| (width, height), | |
| paste_to) | |
| masks_for_overlay = [] | |
| for i, overlay_image in enumerate(overlay_images): | |
| 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') | |
| return masks_for_overlay | |
| def apply_masks( | |
| settings, | |
| nmask, | |
| overlay_images, | |
| width, height, | |
| paste_to): | |
| import torch | |
| import modules.processing as proc | |
| import modules.images as images | |
| from PIL import Image, ImageOps, ImageFilter | |
| converted_mask = nmask[0].float().clone() | |
| converted_mask = torch.clamp(converted_mask, min=0, max=1).pow_(settings.mask_blend_scale / 2) | |
| converted_mask = 255. * converted_mask | |
| converted_mask = converted_mask.cpu().numpy().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, | |
| (width, height), | |
| paste_to) | |
| masks_for_overlay = [] | |
| for i, overlay_image in enumerate(overlay_images): | |
| 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') | |
| return masks_for_overlay |
| def apply_adaptive_masks( | ||
| settings: SoftInpaintingSettings, | ||
| nmask, | ||
| latent_orig, | ||
| latent_processed, | ||
| overlay_images, | ||
| width, height, | ||
| paste_to): | ||
| import torch | ||
| 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. | ||
| if len(nmask.shape) == 3: | ||
| latent_mask = nmask[0].float() | ||
| else: | ||
| 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 * settings.composite_mask_influence) | ||
| 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) | ||
|
|
||
| kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2) | ||
|
|
||
| masks_for_overlay = [] | ||
|
|
||
| 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) | ||
| converted_mask = 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% | ||
| if len(mask_scalar.shape) == 3: | ||
| if mask_scalar.shape[0] > i: | ||
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar[i] | ||
| else: | ||
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar[0] | ||
| else: | ||
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar | ||
|
|
||
| converted_mask = converted_mask / half_weighted_distance | ||
|
|
||
| converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast) | ||
| converted_mask = 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.append(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') | ||
|
|
||
| return masks_for_overlay |
There was a problem hiding this comment.
Review image and tensor operations for potential optimizations.
The function performs complex image processing operations. While the structure is clear, there are opportunities to optimize the operations, especially in terms of reducing the number of operations performed on tensors and images. Ensuring that all image and tensor operations are batched where possible could improve performance.
- latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1)
+ latent_distance = torch.norm(latent_processed - latent_orig, p=2, dim=1, keepdim=True)Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
| def apply_adaptive_masks( | |
| settings: SoftInpaintingSettings, | |
| nmask, | |
| latent_orig, | |
| latent_processed, | |
| overlay_images, | |
| width, height, | |
| paste_to): | |
| import torch | |
| 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. | |
| if len(nmask.shape) == 3: | |
| latent_mask = nmask[0].float() | |
| else: | |
| 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 * settings.composite_mask_influence) | |
| 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) | |
| kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2) | |
| masks_for_overlay = [] | |
| 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) | |
| converted_mask = 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% | |
| if len(mask_scalar.shape) == 3: | |
| if mask_scalar.shape[0] > i: | |
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar[i] | |
| else: | |
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar[0] | |
| else: | |
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar | |
| converted_mask = converted_mask / half_weighted_distance | |
| converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast) | |
| converted_mask = 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.append(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') | |
| return masks_for_overlay | |
| def apply_adaptive_masks( | |
| settings: SoftInpaintingSettings, | |
| nmask, | |
| latent_orig, | |
| latent_processed, | |
| overlay_images, | |
| width, height, | |
| paste_to): | |
| import torch | |
| 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. | |
| if len(nmask.shape) == 3: | |
| latent_mask = nmask[0].float() | |
| else: | |
| 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 * settings.composite_mask_influence) | |
| 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, keepdim=True) | |
| kernel, kernel_center = get_gaussian_kernel(stddev_radius=1.5, max_radius=2) | |
| masks_for_overlay = [] | |
| 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) | |
| converted_mask = 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% | |
| if len(mask_scalar.shape) == 3: | |
| if mask_scalar.shape[0] > i: | |
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar[i] | |
| else: | |
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar[0] | |
| else: | |
| half_weighted_distance = settings.composite_difference_threshold * mask_scalar | |
| converted_mask = converted_mask / half_weighted_distance | |
| converted_mask = 1 / (1 + converted_mask ** settings.composite_difference_contrast) | |
| converted_mask = 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.append(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') | |
| return masks_for_overlay |
fix image upscale on cpu
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Actionable comments posted: 3
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Review profile: CHILL
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- CHANGELOG.md (3 hunks)
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CHANGELOG.md
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Unordered list indentation(MD007, ul-indent)
636-636: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
637-637: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
638-638: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
639-639: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
640-640: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
641-641: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
642-642: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
643-643: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
644-644: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
645-645: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
646-646: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
647-647: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
648-648: Expected: 2; Actual: 3
Unordered list indentation(MD007, ul-indent)
651-651: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
652-652: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
653-653: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
654-654: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
655-655: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
656-656: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
657-657: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
658-658: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
659-659: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
660-660: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
661-661: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
662-662: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
663-663: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
664-664: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
665-665: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
666-666: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
667-667: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
668-668: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
669-669: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
672-672: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
673-673: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
674-674: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
675-675: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
676-676: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
677-677: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
678-678: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
679-679: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
680-680: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
681-681: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
682-682: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
683-683: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
684-684: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
685-685: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
686-686: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
687-687: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
688-688: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
689-689: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
690-690: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
691-691: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
692-692: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
693-693: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
694-694: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
695-695: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
696-696: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
697-697: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
698-698: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
699-699: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
700-700: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
701-701: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
702-702: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
703-703: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
704-704: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
705-705: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
706-706: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
707-707: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
708-708: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
709-709: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
710-710: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
711-711: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
712-712: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
713-713: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
714-714: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
715-715: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
716-716: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
717-717: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
718-718: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
719-719: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
720-720: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
721-721: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
722-722: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
723-723: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
724-724: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
730-730: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
731-731: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
737-737: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
738-738: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
741-741: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
744-744: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
745-745: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
746-746: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
747-747: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
748-748: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
749-749: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
750-750: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
751-751: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
752-752: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
753-753: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
754-754: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
755-755: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
756-756: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
762-762: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
763-763: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
764-764: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
765-765: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
766-766: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
767-767: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
768-768: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
769-769: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
770-770: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
771-771: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
772-772: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
773-773: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
774-774: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
775-775: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
778-778: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
779-779: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
780-780: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
781-781: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
782-782: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
783-783: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
784-784: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
785-785: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
786-786: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
787-787: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
788-788: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
789-789: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
790-790: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
791-791: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
794-794: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
795-795: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
796-796: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
797-797: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
798-798: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
799-799: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
800-800: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
801-801: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
802-802: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
805-805: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
806-806: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
807-807: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
808-808: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
809-809: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
810-810: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
811-811: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
812-812: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
813-813: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
814-814: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
815-815: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
816-816: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
817-817: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
818-818: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
819-819: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
820-820: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
821-821: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
827-827: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
832-832: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
833-833: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
834-834: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
835-835: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
836-836: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
837-837: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
838-838: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
839-839: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
840-840: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
843-843: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
844-844: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
845-845: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
846-846: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
847-847: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
848-848: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
849-849: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
850-850: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
851-851: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
852-852: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
853-853: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
856-856: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
857-857: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
858-858: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
859-859: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
860-860: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
861-861: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
862-862: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
865-865: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
866-866: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
867-867: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
868-868: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
869-869: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
870-870: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
871-871: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
872-872: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
873-873: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
874-874: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
875-875: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
876-876: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
877-877: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
878-878: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
879-879: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
880-880: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
881-881: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
882-882: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
883-883: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
889-889: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
890-890: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
895-895: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
898-898: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
899-899: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
900-900: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
901-901: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
902-902: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
903-903: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
904-904: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
909-909: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
910-910: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
911-911: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
912-912: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
913-913: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
914-914: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
915-915: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
916-916: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
917-917: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
918-918: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
919-919: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
922-922: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
923-923: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
924-924: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
925-925: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
926-926: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
927-927: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
928-928: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
929-929: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
930-930: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
931-931: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
932-932: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
933-933: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
934-934: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
935-935: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
936-936: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
937-937: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
938-938: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
939-939: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
942-942: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
943-943: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
944-944: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
945-945: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
946-946: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
947-947: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
948-948: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
949-949: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
950-950: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
953-953: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
954-954: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
955-955: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
956-956: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
957-957: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
958-958: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
959-959: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
960-960: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
961-961: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
962-962: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
963-963: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
969-969: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
972-972: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
973-973: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
974-974: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
975-975: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
976-976: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
977-977: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
982-982: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
983-983: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
984-984: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
985-985: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
986-986: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
987-987: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
988-988: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
989-989: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
990-990: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
991-991: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
994-994: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
995-995: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
996-996: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
997-997: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
998-998: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
999-999: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1000-1000: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1001-1001: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1002-1002: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1003-1003: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1004-1004: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1007-1007: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1008-1008: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1011-1011: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1012-1012: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1013-1013: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1014-1014: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1015-1015: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1016-1016: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1017-1017: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1018-1018: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1019-1019: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1020-1020: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1021-1021: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1026-1026: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1030-1030: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1031-1031: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1032-1032: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1033-1033: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1034-1034: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1035-1035: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1036-1036: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1037-1037: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1038-1038: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1039-1039: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
3-3: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
9-9: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
23-23: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
38-38: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
42-42: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
53-53: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
90-90: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
120-120: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
125-125: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
130-130: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
135-135: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
139-139: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
144-144: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
151-151: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
161-161: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
168-168: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
188-188: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
198-198: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
203-203: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
246-246: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
252-252: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
282-282: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
298-298: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
325-325: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
343-343: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
348-348: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
388-388: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
413-413: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
427-427: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
456-456: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
479-479: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
527-527: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
576-576: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
581-581: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
607-607: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
650-650: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
671-671: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
729-729: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
736-736: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
740-740: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
743-743: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
761-761: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
777-777: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
793-793: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
804-804: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
826-826: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
831-831: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
842-842: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
855-855: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
864-864: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
888-888: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
894-894: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
897-897: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
908-908: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
921-921: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
941-941: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
952-952: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
968-968: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
971-971: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
981-981: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
993-993: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1006-1006: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1010-1010: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1025-1025: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1029-1029: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
289-289: Expected: underscore; Actual: asterisk
Strong style(MD050, strong-style)
289-289: Expected: underscore; Actual: asterisk
Strong style(MD050, strong-style)
1048-1048: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1049-1049: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1050-1050: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1051-1051: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1052-1052: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1053-1053: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1054-1054: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1055-1055: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1056-1056: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1057-1057: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1058-1058: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1059-1059: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1062-1062: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1063-1063: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1061-1061: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
1079-1079: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1080-1080: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1081-1081: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
1082-1082: Expected: 0; Actual: 1
Unordered list indentation(MD007, ul-indent)
Additional comments not posted (10)
CHANGELOG.md (10)
7-7: Add a blank line after the heading for consistency.Ensure there is a blank line after the heading for clarity and consistency.
## 1.10.0
9-9: Remove trailing punctuation from the heading.The heading should not have trailing punctuation.
### Features: ### FeaturesTools
Markdownlint
9-9: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
23-23: Remove trailing punctuation from the heading.The heading should not have trailing punctuation.
### Minor: ### MinorTools
Markdownlint
23-23: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
38-38: Remove trailing punctuation from the heading.The heading should not have trailing punctuation.
### Extensions and API: ### Extensions and APITools
Markdownlint
38-38: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
42-42: Remove trailing punctuation from the heading.The heading should not have trailing punctuation.
### Performance: ### PerformanceTools
Markdownlint
42-42: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
53-53: Remove trailing punctuation from the heading.The heading should not have trailing punctuation.
### Bug Fixes: ### Bug FixesTools
Markdownlint
53-53: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
90-90: Remove trailing punctuation from the heading.The heading should not have trailing punctuation.
### Other: ### OtherTools
Markdownlint
90-90: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
91-91: Add a blank line after the 'Other' section heading for consistency.Ensure there is a blank line after the heading for clarity and consistency.
### Other
24-36: Fix unordered list indentation.The unordered list items should be properly indented for consistency.
* Option to skip CFG on early steps ([#15607](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15607)) * Add --models-dir option ([#15742](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15742)) * Allow mobile users to open context menu by using two fingers press ([#15682](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15682)) * Infotext: add Lora name as TI hashes for bundled Textual Inversion ([#15679](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15679)) * Check model's hash after downloading it to prevent corruped downloads ([#15602](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15602)) * More extension tag filtering options ([#15627](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15627)) * When saving AVIF, use JPEG's quality setting ([#15610](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15610)) * Add filename pattern: `[basename]` ([#15978](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15978)) * Add option to enable clip skip for clip L on SDXL ([#15992](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15992)) * Option to prevent screen sleep during generation ([#16001](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16001)) * ToggleLivePriview button in image viewer ([#16065](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16065)) * Remove ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153)) * option to disable save button log.csv ([#16242](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16242))Likely invalid or redundant comment.
1-5: Fix unordered list indentation.The unordered list item should be properly indented for consistency.
## 1.10.1 ### Bug Fixes: * fix image upscale on cpu ([#16275](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16275))Likely invalid or redundant comment.
Tools
Markdownlint
3-3: Punctuation: ':'
Trailing punctuation in heading(MD026, no-trailing-punctuation)
| * fix changelog #15883 -> #15882 ([#15907](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15907)) | ||
| * ReloadUI backgroundColor --background-fill-primary ([#15864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15864)) | ||
| * Use different torch versions for Intel and ARM Macs ([#15851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15851)) |
There was a problem hiding this comment.
Fix unordered list indentation.
The unordered list items should be properly indented for consistency.
* fix changelog #15883 -> #15882 ([#15907](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15907))
* ReloadUI backgroundColor --background-fill-primary ([#15864](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15864))
* Use different torch versions for Intel and ARM Macs ([#15851](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15851))
* XYZ override rework ([#15836](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15836))
* scroll extensions table on overflow ([#15830](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15830))
* img2img batch upload method ([#15817](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15817))
* chore: sync v1.8.0 packages according to changelog ([#15783](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15783))
* Add AVIF MIME type support to mimetype definitions ([#15739](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15739))
* Update imageviewer.js ([#15730](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15730))
* no-referrer ([#15641](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15641))
* .gitignore trace.json ([#15980](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15980))
* Bump spandrel to 0.3.4 ([#16144](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16144))
* Defunct --max-batch-count ([#16119](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16119))
* docs: update bug_report.yml ([#16102](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16102))
* Maintaining Project Compatibility for Python 3.9 Users Without Upgrade Requirements. ([#16088](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16088), [#16169](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16169), [#16192](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16192))
* Update torch for ARM Macs to 2.3.1 ([#16059](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16059))
* remove deprecated setting dont_fix_second_order_samplers_schedule ([#16061](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16061))
* chore: fix typos ([#16060](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16060))
* shlex.join launch args in console log ([#16170](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16170))
* activate venv .bat ([#16231](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16231))
* add ids to the resize tabs in img2img ([#16218](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16218))
* update installation guide linux ([#16178](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16178))
* Robust sysinfo ([#16173](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16173
<!-- This is an auto-generated comment by CodeRabbit -->| * [Performance 1/6] use_checkpoint = False ([#15803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803)) | ||
| * [Performance 2/6] Replace einops.rearrange with torch native ops ([#15804](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15804)) | ||
| * [Performance 4/6] Precompute is_sdxl_inpaint flag ([#15806](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15806)) | ||
| * [Performance 5/6] Prevent unnecessary extra networks bias backup ([#15816](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15816)) | ||
| * [Performance 6/6] Add --precision half option to avoid casting during inference ([#15820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15820)) |
There was a problem hiding this comment.
Fix unordered list indentation.
The unordered list items should be properly indented for consistency.
* [Performance 1/6] use_checkpoint = False ([#15803](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15803))
* [Performance 2/6] Replace einops.rearrange with torch native ops ([#15804](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15804))
* [Performance 4/6] Precompute is_sdxl_inpaint flag ([#15806](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15806))
* [Performance 5/6] Prevent unnecessary extra networks bias backup ([#15816](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15816))
* [Performance 6/6] Add --precision half option to avoid casting during inference ([#15820](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15820))
* [Performance] LDM optimization patches ([#15824](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15824))
* [Performance] Keep sigmas on CPU ([#15823](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15823))
* Check for nans in unet only once, after all steps have been completed
* Added pption to run torch profiler for image generationCommittable suggestion was skipped due to low confidence.
| * Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958)) | ||
| * feat: lora partial update precede full update ([#15943](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15943)) | ||
| * Fix bug where file extension had an extra '.' under some circumstances ([#15893](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15893)) | ||
| * Fix corrupt model initial load loop ([#15600](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15600)) | ||
| * Allow old sampler names in API ([#15656](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15656)) | ||
| * more old sampler scheduler compatibility ([#15681](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15681)) | ||
| * Fix Hypertile xyz ([#15831](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15831)) | ||
| * XYZ CSV skipinitialspace ([#15832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15832)) | ||
| * fix soft inpainting on mps and xpu, torch_utils.float64 ([#15815](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15815)) | ||
| * fix extention update when not on main branch ([#15797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15797)) | ||
| * update pickle safe filenames | ||
| * use relative path for webui-assets css ([#15757](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15757)) | ||
| * When creating a virtual environment, upgrade pip in webui.bat/webui.sh ([#15750](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15750)) | ||
| * Fix AttributeError ([#15738](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15738)) | ||
| * use script_path for webui root in launch_utils ([#15705](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15705)) | ||
| * fix extra batch mode P Transparency ([#15664](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15664)) | ||
| * use gradio theme colors in css ([#15680](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15680)) | ||
| * Fix dragging text within prompt input ([#15657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15657)) | ||
| * Add correct mimetype for .mjs files ([#15654](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15654)) | ||
| * QOL Items - handle metadata issues more cleanly for SD models, Loras and embeddings ([#15632](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15632)) | ||
| * replace wsl-open with wslpath and explorer.exe ([#15968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15968)) | ||
| * Fix SDXL Inpaint ([#15976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15976)) | ||
| * multi size grid ([#15988](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15988)) | ||
| * fix Replace preview ([#16118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16118)) | ||
| * Possible fix of wrong scale in weight decomposition ([#16151](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16151)) | ||
| * Ensure use of python from venv on Mac and Linux ([#16116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16116)) | ||
| * Prioritize python3.10 over python3 if both are available on Linux and Mac (with fallback) ([#16092](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16092)) | ||
| * stoping generation extras ([#16085](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16085)) | ||
| * Fix SD2 loading ([#16078](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16078), [#16079](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16079)) | ||
| * fix infotext Lora hashes for hires fix different lora ([#16062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16062)) | ||
| * Fix sampler scheduler autocorrection warning ([#16054](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16054)) | ||
| * fix ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153)) | ||
| * fix upscale logic ([#16239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16239)) | ||
| * [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job) ([#16202](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16202)) |
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Fix unordered list indentation.
The unordered list items should be properly indented for consistency.
* Fix for grids without comprehensive infotexts ([#15958](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15958))
* feat: lora partial update precede full update ([#15943](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15943))
* Fix bug where file extension had an extra '.' under some circumstances ([#15893](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15893))
* Fix corrupt model initial load loop ([#15600](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15600))
* Allow old sampler names in API ([#15656](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15656))
* more old sampler scheduler compatibility ([#15681](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15681))
* Fix Hypertile xyz ([#15831](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15831))
* XYZ CSV skipinitialspace ([#15832](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15832))
* fix soft inpainting on mps and xpu, torch_utils.float64 ([#15815](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15815))
* fix extention update when not on main branch ([#15797](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15797))
* update pickle safe filenames
* use relative path for webui-assets css ([#15757](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15757))
* When creating a virtual environment, upgrade pip in webui.bat/webui.sh ([#15750](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15750))
* Fix AttributeError ([#15738](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15738))
* use script_path for webui root in launch_utils ([#15705](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15705))
* fix extra batch mode P Transparency ([#15664](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15664))
* use gradio theme colors in css ([#15680](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15680))
* Fix dragging text within prompt input ([#15657](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15657))
* Add correct mimetype for .mjs files ([#15654](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15654))
* QOL Items - handle metadata issues more cleanly for SD models, Loras and embeddings ([#15632](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15632))
* replace wsl-open with wslpath and explorer.exe ([#15968](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15968))
* Fix SDXL Inpaint ([#15976](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15976))
* multi size grid ([#15988](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15988))
* fix Replace preview ([#16118](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16118))
* Possible fix of wrong scale in weight decomposition ([#16151](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16151))
* Ensure use of python from venv on Mac and Linux ([#16116](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16116))
* Prioritize python3.10 over python3 if both are available on Linux and Mac (with fallback) ([#16092](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16092))
* stoping generation extras ([#16085](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16085))
* Fix SD2 loading ([#16078](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16078), [#16079](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16079))
* fix infotext Lora hashes for hires fix different lora ([#16062](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16062))
* Fix sampler scheduler autocorrection warning ([#16054](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16054))
* fix ui flashing on reloading and fast scrollong ([#16153](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16153))
* fix upscale logic ([#16239](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16239))
* [bug] do not break progressbar on non-job actions (add wrap_gradio_call_no_job) ([#16202](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16202))
* fix OSError: cannot write mode P as JPEG ([#16194](https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/16194))Committable suggestion was skipped due to low confidence.
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Summary by CodeRabbit
New Features
txt2imgandimg2imgtabs.Improvements
UI Enhancements
Bug Fixes
Performance Optimizations