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msgid ""
msgstr ""
"Project-Id-Version: PACKAGE VERSION\n"
"Report-Msgid-Bugs-To: \n"
"POT-Creation-Date: 2023-06-25 13:39+0100\n"
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
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"Language-Team: LANGUAGE <LL@li.org>\n"
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"Content-Transfer-Encoding: 8bit\n"

#: plugins/train/_config.py:17
msgid ""
"\n"
"NB: Unless specifically stated, values changed here will only take effect "
"when creating a new model."
msgstr ""

#: plugins/train/_config.py:22
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:29
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:36
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:41
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:45
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:52
msgid ""
"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\n"
"This variant uses the AlexNet backbone. A fairly light and old model which "
"performed best in the paper's original implementation.\n"
"NB: For AMD Users the final linear layer is not implemented."
msgstr ""

#: plugins/train/_config.py:62
msgid ""
"Same as lpips_alex, but using the SqueezeNet backbone. A more lightweight "
"version of AlexNet.\n"
"NB: For AMD Users the final linear layer is not implemented."
msgstr ""

#: plugins/train/_config.py:65
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:68
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:72
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:76
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:81
msgid ""
"Multiscale Structural Similarity Index Metric is similar to SSIM except that "
"it performs the calculations along multiple scales of the input image."
msgstr ""

#: plugins/train/_config.py:84
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:88
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:93
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:97
msgid "Do not use an additional loss function."
msgstr ""

#: plugins/train/_config.py:117
msgid "Options that apply to all models"
msgstr ""

#: plugins/train/_config.py:126 plugins/train/_config.py:150
msgid "face"
msgstr ""

#: plugins/train/_config.py:128
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:152
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:168 plugins/train/_config.py:179
msgid "initialization"
msgstr ""

#: plugins/train/_config.py:170
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:181
msgid ""
"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.\n"
"NB:\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."
msgstr ""

#: plugins/train/_config.py:198 plugins/train/_config.py:223
#: plugins/train/_config.py:238 plugins/train/_config.py:256
#: plugins/train/_config.py:290
msgid "optimizer"
msgstr ""

#: plugins/train/_config.py:202
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:225
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:240
msgid ""
"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)."
msgstr ""

#: plugins/train/_config.py:262
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:283
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:296 plugins/train/_config.py:308
#: plugins/train/_config.py:322 plugins/train/_config.py:339
msgid "network"
msgstr ""

#: plugins/train/_config.py:298
msgid ""
"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"
msgstr ""

#: plugins/train/_config.py:311
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:324
msgid ""
"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"
"\n"
"This 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."
msgstr ""

#: plugins/train/_config.py:341
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:354
msgid "convert"
msgstr ""

#: plugins/train/_config.py:356
msgid ""
"[GPU Only]. The number of faces to feed through the model at once when "
"running the Convert process.\n"
"\n"
"NB: 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."
msgstr ""

#: plugins/train/_config.py:375
msgid ""
"Loss configuration options\n"
"Loss is the mechanism by which a Neural Network judges how well it thinks "
"that it is recreating a face."
msgstr ""

#: plugins/train/_config.py:382 plugins/train/_config.py:394
#: plugins/train/_config.py:407 plugins/train/_config.py:427
#: plugins/train/_config.py:439 plugins/train/_config.py:459
#: plugins/train/_config.py:471 plugins/train/_config.py:491
#: plugins/train/_config.py:507 plugins/train/_config.py:523
#: plugins/train/_config.py:540
msgid "loss"
msgstr ""

#: plugins/train/_config.py:386
msgid "The loss function to use."
msgstr ""

#: plugins/train/_config.py:398
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:413
msgid ""
"The amount of weight to apply to the second loss function.\n"
"\n"
"\n"
"\n"
"The 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."
msgstr ""

#: plugins/train/_config.py:431
msgid ""
"The third loss function to use. You can adjust the weighting of this loss "
"function with the loss_weight_3 option."
msgstr ""

#: plugins/train/_config.py:445
msgid ""
"The amount of weight to apply to the third loss function.\n"
"\n"
"\n"
"\n"
"The 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."
msgstr ""

#: plugins/train/_config.py:463
msgid ""
"The fourth loss function to use. You can adjust the weighting of this loss "
"function with the loss_weight_3 option."
msgstr ""

#: plugins/train/_config.py:477
msgid ""
"The amount of weight to apply to the fourth loss function.\n"
"\n"
"\n"
"\n"
"The 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."
msgstr ""

#: plugins/train/_config.py:496
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:513
msgid ""
"The amount of priority to give to the eyes.\n"
"\n"
"The 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"
"\n"
"NB: Penalized Mask Loss must be enable to use this option."
msgstr ""

#: plugins/train/_config.py:529
msgid ""
"The amount of priority to give to the mouth.\n"
"\n"
"The 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"
"\n"
"NB: Penalized Mask Loss must be enable to use this option."
msgstr ""

#: plugins/train/_config.py:542
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:553 plugins/train/_config.py:595
#: plugins/train/_config.py:609 plugins/train/_config.py:618
msgid "mask"
msgstr ""

#: plugins/train/_config.py:556
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:597
msgid ""
"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."
msgstr ""

#: plugins/train/_config.py:611
msgid ""
"Sets pixels that are near white to white and near black to black. Set to 0 "
"for off."
msgstr ""

#: plugins/train/_config.py:620
msgid ""
"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."
msgstr ""
