#!/usr/bin/env python3 """ Default configurations for models """ import gettext import logging import os from lib.config import FaceswapConfig from plugins.plugin_loader import PluginLoader # LOCALES _LANG = gettext.translation("plugins.train._config", localedir="locales", fallback=True) _ = _LANG.gettext logger = logging.getLogger(__name__) ADDITIONAL_INFO = _("\nNB: Unless specifically stated, values changed here will only take effect " "when creating a new model.") _LOSS_HELP = { "ffl": _( "Focal Frequency Loss. Analyzes the frequency spectrum of the images rather than the " "images themselves. This loss function can be used on its own, but the original paper " "found increased benefits when using it as a complementary loss to another spacial loss " "function (e.g. MSE). Ref: Focal Frequency Loss for Image Reconstruction and Synthesis " "https://arxiv.org/pdf/2012.12821.pdf NB: This loss does not currently work on AMD " "cards."), "flip": _( "Nvidia FLIP. A perceptual loss measure that approximates the difference perceived by " "humans as they alternate quickly (or flip) between two images. Used on its own and this " "loss function creates a distinct grid on the output. However it can be helpful when " "used as a complimentary loss function. Ref: FLIP: A Difference Evaluator for " "Alternating Images: " "https://research.nvidia.com/sites/default/files/node/3260/FLIP_Paper.pdf"), "gmsd": _( "Gradient Magnitude Similarity Deviation seeks to match the global standard deviation of " "the pixel to pixel differences between two images. Similar in approach to SSIM. Ref: " "Gradient Magnitude Similarity Deviation: An Highly Efficient Perceptual Image Quality " "Index https://arxiv.org/ftp/arxiv/papers/1308/1308.3052.pdf"), "l_inf_norm": _( "The L_inf norm will reduce the largest individual pixel error in an image. As " "each largest error is minimized sequentially, the overall error is improved. This loss " "will be extremely focused on outliers."), "laploss": _( "Laplacian Pyramid Loss. Attempts to improve results by focussing on edges using " "Laplacian Pyramids. As this loss function gives priority to edges over other low-" "frequency information, like color, it should not be used on its own. The original " "implementation uses this loss as a complimentary function to MSE. " "Ref: Optimizing the Latent Space of Generative Networks " "https://arxiv.org/abs/1707.05776"), "lpips_alex": _( "LPIPS is a perceptual loss that uses the feature outputs of other pretrained models as a " "loss metric. Be aware that this loss function will use more VRAM. Used on its own and " "this loss will create a distinct moire pattern on the output, however it can be helpful " "as a complimentary loss function. The output of this function is strong, so depending " "on your chosen primary loss function, you are unlikely going to want to set the weight " "above about 25%. Ref: The Unreasonable Effectiveness of Deep Features as a Perceptual " "Metric http://arxiv.org/abs/1801.03924\nThis variant uses the AlexNet backbone. A fairly " "light and old model which performed best in the paper's original implementation.\nNB: " "For AMD Users the final linear layer is not implemented."), "lpips_squeeze": _( "Same as lpips_alex, but using the SqueezeNet backbone. A more lightweight " "version of AlexNet.\nNB: For AMD Users the final linear layer is not implemented."), "lpips_vgg16": _( "Same as lpips_alex, but using the VGG16 backbone. A more heavyweight model.\n" "NB: For AMD Users the final linear layer is not implemented."), "logcosh": _( "log(cosh(x)) acts similar to MSE for small errors and to MAE for large errors. Like " "MSE, it is very stable and prevents overshoots when errors are near zero. Like MAE, it " "is robust to outliers."), "mae": _( "Mean absolute error will guide reconstructions of each pixel towards its median value in " "the training dataset. Robust to outliers but as a median, it can potentially ignore some " "infrequent image types in the dataset."), "mse": _( "Mean squared error will guide reconstructions of each pixel towards its average value in " "the training dataset. As an avg, it will be susceptible to outliers and typically " "produces slightly blurrier results. Ref: Multi-Scale Structural Similarity for Image " "Quality Assessment https://www.cns.nyu.edu/pub/eero/wang03b.pdf"), "ms_ssim": _( "Multiscale Structural Similarity Index Metric is similar to SSIM except that it " "performs the calculations along multiple scales of the input image."), "smooth_loss": _( "Smooth_L1 is a modification of the MAE loss to correct two of its disadvantages. " "This loss has improved stability and guidance for small errors. Ref: A General and " "Adaptive Robust Loss Function https://arxiv.org/pdf/1701.03077.pdf"), "ssim": _( "Structural Similarity Index Metric is a perception-based loss that considers changes in " "texture, luminance, contrast, and local spatial statistics of an image. Potentially " "delivers more realistic looking images. Ref: Image Quality Assessment: From Error " "Visibility to Structural Similarity http://www.cns.nyu.edu/pub/eero/wang03-reprint.pdf"), "pixel_gradient_diff": _( "Instead of minimizing the difference between the absolute value of each " "pixel in two reference images, compute the pixel to pixel spatial difference in each " "image and then minimize that difference between two images. Allows for large color " "shifts, but maintains the structure of the image."), "none": _("Do not use an additional loss function.")} _NON_PRIMARY_LOSS = ["flip", "lpips_alex", "lpips_squeeze", "lpips_vgg16", "none"] class Config(FaceswapConfig): """ Config File for Models """ # pylint:disable=too-many-statements def set_defaults(self) -> None: """ Set the default values for config """ logger.debug("Setting defaults") self._set_globals() self._set_loss() self._defaults_from_plugin(os.path.dirname(__file__)) def _set_globals(self) -> None: """ Set the global options for training """ logger.debug("Setting global config") section = "global" self.add_section(section, _("Options that apply to all models") + ADDITIONAL_INFO) self.add_item( section=section, title="centering", datatype=str, gui_radio=True, default="face", choices=["face", "head", "legacy"], fixed=True, group=_("face"), info=_( "How to center the training image. The extracted images are centered on the " "middle of the skull based on the face's estimated pose. A subsection of these " "images are used for training. The centering used dictates how this subsection " "will be cropped from the aligned images." "\n\tface: Centers the training image on the center of the face, adjusting for " "pitch and yaw." "\n\thead: Centers the training image on the center of the head, adjusting for " "pitch and yaw. NB: You should only select head centering if you intend to " "include the full head (including hair) in the final swap. This may give mixed " "results. Additionally, it is only worth choosing head centering if you are " "training with a mask that includes the hair (e.g. BiSeNet-FP-Head)." "\n\tlegacy: The 'original' extraction technique. Centers the training image " "near the tip of the nose with no adjustment. Can result in the edges of the " "face appearing outside of the training area.")) self.add_item( section=section, title="coverage", datatype=float, default=87.5, min_max=(62.5, 100.0), rounding=2, fixed=True, group=_("face"), info=_( "How much of the extracted image to train on. A lower coverage will limit the " "model's scope to a zoomed-in central area while higher amounts can include the " "entire face. A trade-off exists between lower amounts given more detail " "versus higher amounts avoiding noticeable swap transitions. For 'Face' " "centering you will want to leave this above 75%. For Head centering you will " "most likely want to set this to 100%. Sensible values for 'Legacy' " "centering are:" "\n\t62.5% spans from eyebrow to eyebrow." "\n\t75.0% spans from temple to temple." "\n\t87.5% spans from ear to ear." "\n\t100.0% is a mugshot.")) self.add_item( section=section, title="icnr_init", datatype=bool, default=False, group=_("initialization"), info=_( "Use ICNR to tile the default initializer in a repeating pattern. " "This strategy is designed for pairing with sub-pixel / pixel shuffler " "to reduce the 'checkerboard effect' in image reconstruction. " "\n\t https://arxiv.org/ftp/arxiv/papers/1707/1707.02937.pdf")) self.add_item( section=section, title="conv_aware_init", datatype=bool, default=False, group=_("initialization"), info=_( "Use Convolution Aware Initialization for convolutional layers. " "This can help eradicate the vanishing and exploding gradient problem " "as well as lead to higher accuracy, lower loss and faster convergence.\nNB:" "\n\t This can use more VRAM when creating a new model so you may want to " "lower the batch size for the first run. The batch size can be raised " "again when reloading the model. " "\n\t Multi-GPU is not supported for this option, so you should start the model " "on a single GPU. Once training has started, you can stop training, enable " "multi-GPU and resume." "\n\t Building the model will likely take several minutes as the calculations " "for this initialization technique are expensive. This will only impact starting " "a new model.")) self.add_item( section=section, title="optimizer", datatype=str, gui_radio=True, group=_("optimizer"), default="adam", choices=["adabelief", "adam", "nadam", "rms-prop"], info=_( "The optimizer to use." "\n\t adabelief - Adapting Stepsizes by the Belief in Observed Gradients. An " "optimizer with the aim to converge faster, generalize better and remain more " "stable. (https://arxiv.org/abs/2010.07468). NB: Epsilon for AdaBelief needs to " "be set to a smaller value than other Optimizers. Generally setting the 'Epsilon " "Exponent' to around '-16' should work." "\n\t adam - Adaptive Moment Optimization. A stochastic gradient descent method " "that is based on adaptive estimation of first-order and second-order moments." "\n\t nadam - Adaptive Moment Optimization with Nesterov Momentum. Much like " "Adam but uses a different formula for calculating momentum." "\n\t rms-prop - Root Mean Square Propagation. Maintains a moving (discounted) " "average of the square of the gradients. Divides the gradient by the root of " "this average.")) self.add_item( section=section, title="learning_rate", datatype=float, default=5e-5, min_max=(1e-6, 1e-4), rounding=6, fixed=False, group=_("optimizer"), info=_( "Learning rate - how fast your network will learn (how large are the " "modifications to the model weights after one batch of training). Values that " "are too large might result in model crashes and the inability of the model to " "find the best solution. Values that are too small might be unable to escape " "from dead-ends and find the best global minimum.")) self.add_item( section=section, title="epsilon_exponent", datatype=int, default=-7, min_max=(-20, 0), rounding=1, fixed=False, group=_("optimizer"), info=_( "The epsilon adds a small constant to weight updates to attempt to avoid 'divide " "by zero' errors. Unless you are using the AdaBelief Optimizer, then Generally " "this option should be left at default value, For AdaBelief, setting this to " "around '-16' should work.\n" "In all instances if you are getting 'NaN' loss values, and have been unable to " "resolve the issue any other way (for example, increasing batch size, or " "lowering learning rate), then raising the epsilon can lead to a more stable " "model. It may, however, come at the cost of slower training and a less accurate " "final result.\n" "NB: The value given here is the 'exponent' to the epsilon. For example, " "choosing '-7' will set the epsilon to 1e-7. Choosing '-3' will set the epsilon " "to 0.001 (1e-3).")) self.add_item( section=section, title="save_optimizer", datatype=str, group=_("optimizer"), default="exit", fixed=False, gui_radio=True, choices=["never", "always", "exit"], info=_( "When to save the Optimizer Weights. Saving the optimizer weights is not " "necessary and will increase the model file size 3x (and by extension the amount " "of time it takes to save the model). However, it can be useful to save these " "weights if you want to guarantee that a resumed model carries off exactly from " "where it left off, rather than spending a few hundred iterations catching up." "\n\t never - Don't save optimizer weights." "\n\t always - Save the optimizer weights at every save iteration. Model saving " "will take longer, due to the increased file size, but you will always have the " "last saved optimizer state in your model file." "\n\t exit - Only save the optimizer weights when explicitly terminating a " "model. This can be when the model is actively stopped or when the target " "iterations are met. Note: If the training session ends because of another " "reason (e.g. power outage, Out of Memory Error, NaN detected) then the " "optimizer weights will NOT be saved.")) self.add_item( section=section, title="lr_finder_iterations", datatype=int, default=1000, min_max=(100, 10000), rounding=100, fixed=True, group=_("Learning Rate Finder"), info=_( "The number of iterations to process to find the optimal learning rate. Higher " "values will take longer, but will be more accurate.")) self.add_item( section=section, title="lr_finder_mode", datatype=str, default="set", fixed=True, gui_radio=True, choices=["set", "graph_and_set", "graph_and_exit"], group=_("Learning Rate Finder"), info=_( "The operation mode for the learning rate finder. Only applicable to new models. " "For existing models this will always default to 'set'." "\n\tset - Train with the discovered optimal learning rate." "\n\tgraph_and_set - Output a graph in the training folder showing the discovered " "learning rates and train with the optimal learning rate." "\n\tgraph_and_exit - Output a graph in the training folder with the discovered " "learning rates and exit.")) self.add_item( section=section, title="lr_finder_strength", datatype=str, default="default", fixed=True, gui_radio=True, choices=["default", "aggressive", "extreme"], group=_("Learning Rate Finder"), info=_( "How aggressively to set the Learning Rate. More aggressive can learn faster, but " "is more likely to lead to exploding gradients." "\n\tdefault - The default optimal learning rate. A safe choice for nearly all " "use cases." "\n\taggressive - Set's a higher learning rate than the default. May learn faster " "but with a higher chance of exploding gradients." "\n\textreme - The highest optimal learning rate. A much higher risk of exploding " "gradients.")) self.add_item( section=section, title="autoclip", datatype=bool, default=False, info=_( "Apply AutoClipping to the gradients. AutoClip analyzes the " "gradient weights and adjusts the normalization value dynamically to fit the " "data. Can help prevent NaNs and improve model optimization at the expense of " "VRAM. Ref: AutoClip: Adaptive Gradient Clipping for Source Separation Networks " "https://arxiv.org/abs/2007.14469"), fixed=False, gui_radio=True, group=_("optimizer")) self.add_item( section=section, title="reflect_padding", datatype=bool, default=False, group=_("network"), info=_( "Use reflection padding rather than zero padding with convolutions. " "Each convolution must pad the image boundaries to maintain the proper " "sizing. More complex padding schemes can reduce artifacts at the " "border of the image." "\n\t http://www-cs.engr.ccny.cuny.edu/~wolberg/cs470/hw/hw2_pad.txt")) self.add_item( section=section, title="allow_growth", datatype=bool, default=False, group=_("network"), fixed=False, info=_( "Enable the Tensorflow GPU 'allow_growth' configuration option. " "This option prevents Tensorflow from allocating all of the GPU VRAM at launch " "but can lead to higher VRAM fragmentation and slower performance. Should only " "be enabled if you are receiving errors regarding 'cuDNN fails to initialize' " "when commencing training.")) self.add_item( section=section, title="mixed_precision", datatype=bool, default=False, fixed=False, group=_("network"), info=_( "NVIDIA GPUs can run operations in float16 faster than in " "float32. Mixed precision allows you to use a mix of float16 with float32, to " "get the performance benefits from float16 and the numeric stability benefits " "from float32.\n\nThis is untested on DirectML backend, but will run on most " "Nvidia models. it will only speed up training on more recent GPUs. Those with " "compute capability 7.0 or higher will see the greatest performance benefit from " "mixed precision because they have Tensor Cores. Older GPUs offer no math " "performance benefit for using mixed precision, however memory and bandwidth " "savings can enable some speedups. Generally RTX GPUs and later will offer the " "most benefit.")) self.add_item( section=section, title="nan_protection", datatype=bool, default=True, group=_("network"), info=_( "If a 'NaN' is generated in the model, this means that the model has corrupted " "and the model is likely to start deteriorating from this point on. Enabling NaN " "protection will stop training immediately in the event of a NaN. The last save " "will not contain the NaN, so you may still be able to rescue your model."), fixed=False) self.add_item( section=section, title="convert_batchsize", datatype=int, default=16, min_max=(1, 32), rounding=1, fixed=False, group=_("convert"), info=_( "[GPU Only]. The number of faces to feed through the model at once when running " "the Convert process.\n\nNB: Increasing this figure is unlikely to improve " "convert speed, however, if you are getting Out of Memory errors, then you may " "want to reduce the batch size.")) def _set_loss(self) -> None: # pylint:disable=line-too-long """ Set the default loss options. Loss Documentation MAE https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0 MSE https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0 LogCosh https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9d4b0 L_inf_norm https://medium.com/@montjoile/l0-norm-l1-norm-l2-norm-l-infinity-norm-7a7d18a4f40c """ # noqa # pylint:enable=line-too-long logger.debug("Setting Loss config") section = "global.loss" self.add_section(section, _("Loss configuration options\n" "Loss is the mechanism by which a Neural Network judges how well it " "thinks that it is recreating a face.") + ADDITIONAL_INFO) self.add_item( section=section, title="loss_function", datatype=str, group=_("loss"), default="ssim", fixed=False, choices=[x for x in sorted(_LOSS_HELP) if x not in _NON_PRIMARY_LOSS], info=(_("The loss function to use.") + "\n\n\t" + "\n\n\t".join(f"{k}: {v}" for k, v in sorted(_LOSS_HELP.items()) if k not in _NON_PRIMARY_LOSS))) self.add_item( section=section, title="loss_function_2", datatype=str, group=_("loss"), default="mse", fixed=False, choices=list(sorted(_LOSS_HELP)), info=(_("The second loss function to use. If using a structural based loss (such as " "SSIM, MS-SSIM or GMSD) it is common to add an L1 regularization(MAE) or L2 " "regularization (MSE) function. You can adjust the weighting of this loss " "function with the loss_weight_2 option.") + "\n\n\t" + "\n\n\t".join(f"{k}: {v}" for k, v in sorted(_LOSS_HELP.items())))) self.add_item( section=section, title="loss_weight_2", datatype=int, group=_("loss"), min_max=(0, 400), rounding=1, default=100, fixed=False, info=_( "The amount of weight to apply to the second loss function.\n\n" "\n\nThe value given here is as a percentage denoting how much the selected " "function should contribute to the overall loss cost of the model. For example:" "\n\t 100 - The loss calculated for the second loss function will be applied at " "its full amount towards the overall loss score. " "\n\t 25 - The loss calculated for the second loss function will be reduced by a " "quarter prior to adding to the overall loss score. " "\n\t 400 - The loss calculated for the second loss function will be mulitplied " "4 times prior to adding to the overall loss score. " "\n\t 0 - Disables the second loss function altogether.")) self.add_item( section=section, title="loss_function_3", datatype=str, group=_("loss"), default="none", fixed=False, choices=list(sorted(_LOSS_HELP)), info=(_("The third loss function to use. You can adjust the weighting of this loss " "function with the loss_weight_3 option.") + "\n\n\t" + "\n\n\t".join(f"{k}: {v}" for k, v in sorted(_LOSS_HELP.items())))) self.add_item( section=section, title="loss_weight_3", datatype=int, group=_("loss"), min_max=(0, 400), rounding=1, default=0, fixed=False, info=_( "The amount of weight to apply to the third loss function.\n\n" "\n\nThe value given here is as a percentage denoting how much the selected " "function should contribute to the overall loss cost of the model. For example:" "\n\t 100 - The loss calculated for the third loss function will be applied at " "its full amount towards the overall loss score. " "\n\t 25 - The loss calculated for the third loss function will be reduced by a " "quarter prior to adding to the overall loss score. " "\n\t 400 - The loss calculated for the third loss function will be mulitplied 4 " "times prior to adding to the overall loss score. " "\n\t 0 - Disables the third loss function altogether.")) self.add_item( section=section, title="loss_function_4", datatype=str, group=_("loss"), default="none", fixed=False, choices=list(sorted(_LOSS_HELP)), info=(_("The fourth loss function to use. You can adjust the weighting of this loss " "function with the loss_weight_3 option.") + "\n\n\t" + "\n\n\t".join(f"{k}: {v}" for k, v in sorted(_LOSS_HELP.items())))) self.add_item( section=section, title="loss_weight_4", datatype=int, group=_("loss"), min_max=(0, 400), rounding=1, default=0, fixed=False, info=_( "The amount of weight to apply to the fourth loss function.\n\n" "\n\nThe value given here is as a percentage denoting how much the selected " "function should contribute to the overall loss cost of the model. For example:" "\n\t 100 - The loss calculated for the fourth loss function will be applied at " "its full amount towards the overall loss score. " "\n\t 25 - The loss calculated for the fourth loss function will be reduced by a " "quarter prior to adding to the overall loss score. " "\n\t 400 - The loss calculated for the fourth loss function will be mulitplied " "4 times prior to adding to the overall loss score. " "\n\t 0 - Disables the fourth loss function altogether.")) self.add_item( section=section, title="mask_loss_function", datatype=str, group=_("loss"), default="mse", fixed=False, choices=["mae", "mse"], info=_( "The loss function to use when learning a mask." "\n\t MAE - Mean absolute error will guide reconstructions of each pixel " "towards its median value in the training dataset. Robust to outliers but as " "a median, it can potentially ignore some infrequent image types in the dataset." "\n\t MSE - Mean squared error will guide reconstructions of each pixel " "towards its average value in the training dataset. As an average, it will be " "susceptible to outliers and typically produces slightly blurrier results.")) self.add_item( section=section, title="eye_multiplier", datatype=int, group=_("loss"), min_max=(1, 40), rounding=1, default=3, fixed=False, info=_( "The amount of priority to give to the eyes.\n\nThe value given here is as a " "multiplier of the main loss score. For example:" "\n\t 1 - The eyes will receive the same priority as the rest of the face. " "\n\t 10 - The eyes will be given a score 10 times higher than the rest of the " "face." "\n\nNB: Penalized Mask Loss must be enable to use this option.")) self.add_item( section=section, title="mouth_multiplier", datatype=int, group=_("loss"), min_max=(1, 40), rounding=1, default=2, fixed=False, info=_( "The amount of priority to give to the mouth.\n\nThe value given here is as a " "multiplier of the main loss score. For Example:" "\n\t 1 - The mouth will receive the same priority as the rest of the face. " "\n\t 10 - The mouth will be given a score 10 times higher than the rest of the " "face." "\n\nNB: Penalized Mask Loss must be enable to use this option.")) self.add_item( section=section, title="penalized_mask_loss", datatype=bool, default=True, group=_("loss"), info=_( "Image loss function is weighted by mask presence. For areas of " "the image without the facial mask, reconstruction errors will be " "ignored while the masked face area is prioritized. May increase " "overall quality by focusing attention on the core face area.")) self.add_item( section=section, title="mask_type", datatype=str, default="extended", choices=PluginLoader.get_available_extractors("mask", add_none=True, extend_plugin=True), group=_("mask"), gui_radio=True, info=_( "The mask to be used for training. If you have selected 'Learn Mask' or " "'Penalized Mask Loss' you must select a value other than 'none'. The required " "mask should have been selected as part of the Extract process. If it does not " "exist in the alignments file then it will be generated prior to training " "commencing." "\n\tnone: Don't use a mask." "\n\tbisenet-fp_face: Relatively lightweight NN based mask that provides more " "refined control over the area to be masked (configurable in mask settings). " "Use this version of bisenet-fp if your model is trained with 'face' or " "'legacy' centering." "\n\tbisenet-fp_head: Relatively lightweight NN based mask that provides more " "refined control over the area to be masked (configurable in mask settings). " "Use this version of bisenet-fp if your model is trained with 'head' centering." "\n\tcomponents: Mask designed to provide facial segmentation based on the " "positioning of landmark locations. A convex hull is constructed around the " "exterior of the landmarks to create a mask." "\n\tcustom_face: Custom user created, face centered mask." "\n\tcustom_head: Custom user created, head centered mask." "\n\textended: Mask designed to provide facial segmentation based on the " "positioning of landmark locations. A convex hull is constructed around the " "exterior of the landmarks and the mask is extended upwards onto the forehead." "\n\tvgg-clear: Mask designed to provide smart segmentation of mostly frontal " "faces clear of obstructions. Profile faces and obstructions may result in " "sub-par performance." "\n\tvgg-obstructed: Mask designed to provide smart segmentation of mostly " "frontal faces. The mask model has been specifically trained to recognize " "some facial obstructions (hands and eyeglasses). Profile faces may result in " "sub-par performance." "\n\tunet-dfl: Mask designed to provide smart segmentation of mostly frontal " "faces. The mask model has been trained by community members and will need " "testing for further description. Profile faces may result in sub-par " "performance.")) self.add_item( section=section, title="mask_dilation", datatype=float, min_max=(-5.0, 5.0), rounding=1, default=0, fixed=False, group=_("mask"), info=_( "Dilate or erode the mask. Negative values erode the mask (make it smaller). " "Positive values dilate the mask (make it larger). The value given is a " "percentage of the total mask size.")) self.add_item( section=section, title="mask_blur_kernel", datatype=int, min_max=(0, 9), rounding=1, default=3, fixed=False, group=_("mask"), info=_( "Apply gaussian blur to the mask input. This has the effect of smoothing the " "edges of the mask, which can help with poorly calculated masks and give less " "of a hard edge to the predicted mask. The size is in pixels (calculated from " "a 128px mask). Set to 0 to not apply gaussian blur. This value should be odd, " "if an even number is passed in then it will be rounded to the next odd number.")) self.add_item( section=section, title="mask_threshold", datatype=int, default=4, min_max=(0, 50), rounding=1, fixed=False, group=_("mask"), info=_( "Sets pixels that are near white to white and near black to black. Set to 0 for " "off.")) self.add_item( section=section, title="learn_mask", datatype=bool, default=False, group=_("mask"), info=_( "Dedicate a portion of the model to learning how to duplicate the input " "mask. Increases VRAM usage in exchange for learning a quick ability to try " "to replicate more complex mask models."))