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* Remove tensorflow_probability requirement * setup.py - fix progress bars * requirements.txt: Remove pre python 3.9 packages * update apple requirements.txt * update INSTALL.md * Remove python<3.9 code * setup.py - fix Windows Installer * typing: python3.9 compliant * Update pytest and readthedocs python versions * typing fixes * Python Version updates - Reduce max version to 3.10 - Default to 3.10 in installers - Remove incompatible 3.11 tests * Update dependencies * Downgrade imageio dep for Windows * typing: merge optional unions and fixes * Updates - min python version 3.10 - typing to python 3.10 spec - remove pre-tf2.10 code - Add conda tests * train: re-enable optimizer saving * Update dockerfiles * Update setup.py - Apple Conda deps to setup.py - Better Cuda + dependency handling * bugfix: Patch logging to prevent Autograph errors * Update dockerfiles * Setup.py - Setup.py - stdout to utf-8 * Add more OSes to github Actions * suppress mac-os end to end test
718 lines
36 KiB
Python
718 lines
36 KiB
Python
#!/usr/bin/env python3
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"""
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The default options for the faceswap Phaze-A Model plugin.
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Defaults files should be named <plugin_name>_defaults.py
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Any items placed into this file will automatically get added to the relevant config .ini files
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within the faceswap/config folder.
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The following variables should be defined:
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"_HELPTEXT: A string describing what this plugin does
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"_DEFAULTS: A dictionary containing the options, defaults and meta information. The
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" dictionary should be defined as:
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" {<option_name>: {<metadata>}}
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" <option_name> should always be lower text.
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" <metadata> dictionary requirements are listed below.
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The following keys are expected for the _DEFAULTS <metadata> dict:
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"datatype: [required] A python type class. This limits the type of data that can be
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" provided in the .ini file and ensures that the value is returned in the
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" correct type to faceswap. Valid data types are: <class 'int'>, <class 'float'>,
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" <class 'str'>, <class 'bool'>.
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"default: [required] The default value for this option.
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"info: [required] A string describing what this option does.
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"choices: [optional] If this option's datatype is of <class 'str'> then valid
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" selections can be defined here. This validates the option and also enables
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" a combobox / radio option in the GUI.
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"gui_radio: [optional] If <choices> are defined, this indicates that the GUI should use
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" radio buttons rather than a combobox to display this option.
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"min_max: [partial] For <class 'int'> and <class 'float'> data types this is required
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" otherwise it is ignored. Should be a tuple of min and max accepted values.
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" This is used for controlling the GUI slider range. Values are not enforced.
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"rounding: [partial] For <class 'int'> and <class 'float'> data types this is
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" required otherwise it is ignored. Used for the GUI slider. For floats, this
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" is the number of decimal places to display. For ints this is the step size.
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"fixed: [optional] [train only]. Training configurations are fixed when the model is
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" created, and then reloaded from the state file. Marking an item as fixed=False
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" indicates that this value can be changed for existing models, and will override
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" the value saved in the state file with the updated value in config. If not
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" provided this will default to True.
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"""
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_HELPTEXT: str = (
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"Phaze-A Model by TorzDF, with thanks to BirbFakes.\n"
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"Allows for the experimentation of various standard Networks as the encoder and takes "
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"inspiration from Nvidia's StyleGAN for the Decoder. It is highly recommended to research to "
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"understand the parameters better.")
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_ENCODERS: list[str] = sorted([
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"densenet121", "densenet169", "densenet201", "efficientnet_b0", "efficientnet_b1",
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"efficientnet_b2", "efficientnet_b3", "efficientnet_b4", "efficientnet_b5", "efficientnet_b6",
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"efficientnet_b7", "efficientnet_v2_b0", "efficientnet_v2_b1", "efficientnet_v2_b2",
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"efficientnet_v2_b3", "efficientnet_v2_l", "efficientnet_v2_m", "efficientnet_v2_s",
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"inception_resnet_v2", "inception_v3", "mobilenet", "mobilenet_v2", "mobilenet_v3_large",
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"mobilenet_v3_small", "nasnet_large", "nasnet_mobile", "resnet50", "resnet50_v2", "resnet101",
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"resnet101_v2", "resnet152", "resnet152_v2", "vgg16", "vgg19", "xception", "fs_original"])
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_DEFAULTS = {
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# General
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"output_size": {
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"default": 128,
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"info": (
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"Resolution (in pixels) of the output image to generate.\n"
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"BE AWARE Larger resolution will dramatically increase VRAM requirements."),
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"datatype": int,
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"rounding": 64,
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"min_max": (64, 2048),
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"group": "general",
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"fixed": True},
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"shared_fc": {
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"default": "none",
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"info": (
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"Whether to create a shared fully connected layer. This layer will have the same "
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"structure as the fully connected layers used for each side of the model. A shared "
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"fully connected layer looks for patterns that are common to both sides. NB: "
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"Enabling this option only makes sense if 'split fc' is selected."
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"\n\tnone - Do not create a Fully Connected layer for shared data. (Original method)"
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"\n\tfull - Create an exclusive Fully Connected layer for shared data. (IAE method)"
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"\n\thalf - Use the 'fc_a' layer for shared data. This saves VRAM by re-using the "
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"'A' side's fully connected model for the shared data. However, this will lead to "
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"an 'unbalanced' model and can lead to more identity bleed (DFL method)"),
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"datatype": str,
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"choices": ["none", "full", "half"],
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"gui_radio": True,
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"group": "general",
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"fixed": True},
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"enable_gblock": {
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"default": True,
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"info": (
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"Whether to enable the G-Block. If enabled, this will create a shared fully "
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"connected layer (configurable in the 'G-Block hidden layers' section) to look for "
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"patterns in the combined data, before feeding a block prior to the decoder for "
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"merging this shared and combined data."
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"\n\tTrue - Use the G-Block in the Decoder. A combined fully connected layer will be "
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"created to feed this block which can be configured below."
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"\n\tFalse - Don't use the G-Block in the decoder. No combined fully connected layer "
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"will be created."),
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"datatype": bool,
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"group": "general",
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"fixed": True},
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"split_fc": {
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"default": True,
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"info": (
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"Whether to use a single shared Fully Connected layer or separate Fully Connected "
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"layers for each side."
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"\n\tTrue - Use separate Fully Connected layers for Face A and Face B. This is more "
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"similar to the 'IAE' style of model."
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"\n\tFalse - Use combined Fully Connected layers for both sides. This is more "
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"similar to the original Faceswap architecture."),
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"datatype": bool,
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"group": "general",
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"fixed": True},
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"split_gblock": {
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"default": False,
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"info": (
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"If the G-Block is enabled, Whether to use a single G-Block shared between both "
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"sides, or whether to have a separate G-Block (one for each side). NB: The Fully "
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"Connected layer that feeds the G-Block will always be shared."
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"\n\tTrue - Use separate G-Blocks for Face A and Face B."
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"\n\tFalse - Use a combined G-Block layers for both sides."),
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"datatype": bool,
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"group": "general",
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"fixed": True},
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"split_decoders": {
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"default": False,
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"info": (
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"Whether to use a single decoder or split decoders."
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"\n\tTrue - Use a separate decoder for Face A and Face B. This is more similar to "
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"the original Faceswap architecture."
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"\n\tFalse - Use a combined Decoder. This is more similar to 'IAE' style "
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"architecture."),
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"datatype": bool,
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"group": "general",
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"fixed": True},
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# Encoder
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"enc_architecture": {
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"default": "fs_original",
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"info": (
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"The encoder architecture to use. See the relevant config sections for specific "
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"architecture tweaking.\nNB: For keras based pre-built models, the global "
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"initializers and padding options will be ignored for the selected encoder."
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"\n\n\tdensenet: (32px -224px). Ref: Densely Connected Convolutional Networks "
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"(2016): https://arxiv.org/abs/1608.06993?source=post_page"
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"\n\n\tefficientnet: [Tensorflow 2.3+ only] EfficientNet has numerous variants (B0 - "
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"B8) that increases the model width, depth and dimensional space at each step. The "
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"minimum input resolution is 32px for all variants. The maximum input resolution for "
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"each variant is: b0: 224px, b1: 240px, b2: 260px, b3: 300px, b4: 380px, b5: 456px, "
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"b6: 528px, b7 600px. Ref: Rethinking Model Scaling for Convolutional Neural "
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"Networks (2020): https://arxiv.org/abs/1905.11946"
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"\n\n\tefficientnet_v2: [Tensorflow 2.8+ only] EfficientNetV2 is the follow up to "
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"efficientnet. It has numerous variants (B0 - B3 and Small, Medium and Large) that "
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"increases the model width, depth and dimensional space at each step. The minimum "
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"input resolution is 32px for all variants. The maximum input resolution for each "
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"variant is: b0: 224px, b1: 240px, b2: 260px, b3: 300px, s: 384px, m: 480px, l: "
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"480px. Ref: EfficientNetV2: Smaller Models and Faster Training (2021): "
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"https://arxiv.org/abs/2104.00298"
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"\n\n\tfs_original: (32px - 1024px). A configurable variant of the original facewap "
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"encoder. ImageNet weights cannot be loaded for this model. Additional parameters "
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"can be configured with the 'fs_enc' options. A version of this encoder is used in "
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"the following models: Original, Original (lowmem), Dfaker, DFL-H128, DFL-SAE, IAE, "
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"Lightweight."
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"\n\n\tinception_resnet_v2: (75px - 299px). Ref: Inception-ResNet and the Impact of "
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"Residual Connections on Learning (2016): https://arxiv.org/abs/1602.07261"
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"\n\n\tinceptionV3: (75px - 299px). Ref: Rethinking the Inception Architecture for "
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"Computer Vision (2015): https://arxiv.org/abs/1512.00567"
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"\n\n\tmobilenet: (32px - 224px). Additional MobileNet parameters can be set with "
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"the 'mobilenet' options. Ref: MobileNets: Efficient Convolutional Neural Networks "
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"for Mobile Vision Applications (2017): https://arxiv.org/abs/1704.04861"
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"\n\n\tmobilenet_v2: (32px - 224px). Additional MobileNet parameters can be set with "
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"the 'mobilenet' options. Ref: MobileNetV2: Inverted Residuals and Linear "
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"Bottlenecks (2018): https://arxiv.org/abs/1801.04381"
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"\n\n\tmobilenet_v3: (32px - 224px). Additional MobileNet parameters can be set with "
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"the 'mobilenet' options. Ref: Searching for MobileNetV3 (2019): "
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"https://arxiv.org/pdf/1905.02244.pdf"
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"\n\n\tnasnet: (32px - 331px (large) or 224px (mobile)). Ref: Learning Transferable "
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"Architectures for Scalable Image Recognition (2017): "
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"https://arxiv.org/abs/1707.07012"
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"\n\n\tresnet: (32px - 224px). Deep Residual Learning for Image Recognition (2015): "
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"https://arxiv.org/abs/1512.03385"
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"\n\n\tvgg: (32px - 224px). Very Deep Convolutional Networks for Large-Scale Image "
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"Recognition (2014): https://arxiv.org/abs/1409.1556"
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"\n\n\txception: (71px - 229px). Ref: Deep Learning with Depthwise Separable "
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"Convolutions (2017): https://arxiv.org/abs/1409.1556.\n"),
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"datatype": str,
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"choices": _ENCODERS,
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"gui_radio": False,
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"group": "encoder",
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"fixed": True},
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"enc_scaling": {
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"default": 7,
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"info": (
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"Input scaling for the encoder. Some of the encoders have large input sizes, which "
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"often are not helpful for Faceswap. This setting scales the dimensional space that "
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"the encoder works in. For example an encoder with a maximum input size of 224px "
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"will be input an image of 112px at 50%% scaling. See the Architecture tooltip for "
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"the minimum and maximum sizes for each encoder. NB: The input size will be rounded "
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"down to the nearest 16 pixels."),
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"datatype": int,
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"min_max": (0, 100),
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"rounding": 1,
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"group": "encoder",
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"fixed": True},
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"enc_load_weights": {
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"default": True,
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"info": (
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"Load pre-trained weights trained on ImageNet data. Only available for non-"
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"Faceswap encoders (i.e. those not beginning with 'fs'). NB: If you use the global "
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"'load weights' option and have selected to load weights from a previous model's "
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"'encoder' or 'keras_encoder' then the weights loaded here will be replaced by the "
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"weights loaded from your saved model."),
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"datatype": bool,
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"group": "encoder",
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"fixed": True},
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# Bottleneck
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"bottleneck_type": {
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"default": "dense",
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"info": (
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"The type of layer to use for the bottleneck."
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"\n\taverage_pooling: Use a Global Average Pooling 2D layer for the bottleneck."
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"\n\tdense: Use a Dense layer for the bottleneck (the traditional Faceswap method). "
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"You can set the size of the Dense layer with the 'bottleneck_size' parameter."
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"\n\tmax_pooling: Use a Global Max Pooling 2D layer for the bottleneck."),
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"datatype": str,
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"group": "bottleneck",
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"gui_radio": True,
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"choices": ["average_pooling", "dense", "max_pooling"],
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"fixed": True},
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"bottleneck_norm": {
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"default": "none",
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"info": (
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"Apply a normalization layer after encoder output and prior to the bottleneck."
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"\n\tnone - Do not apply a normalization layer"
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"\n\tinstance - Apply Instance Normalization"
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"\n\tlayer - Apply Layer Normalization (Ba et al., 2016)"
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"\n\trms - Apply Root Mean Squared Layer Normalization (Zhang et al., 2019). A "
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"simplified version of Layer Normalization with reduced overhead."),
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"datatype": str,
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"gui_radio": True,
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"choices": ["none", "instance", "layer", "rms"],
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"group": "bottleneck",
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"fixed": True},
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"bottleneck_size": {
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"default": 1024,
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"info": (
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"If using a Dense layer for the bottleneck, then this is the number of nodes to "
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"use."),
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"datatype": int,
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"rounding": 128,
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"min_max": (128, 4096),
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"group": "bottleneck",
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"fixed": True},
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"bottleneck_in_encoder": {
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"default": True,
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"info": (
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"Whether to place the bottleneck in the Encoder or to place it with the other "
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"hidden layers. Placing the bottleneck in the encoder means that both sides will "
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"share the same bottleneck. Placing it with the other fully connected layers means "
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"that each fully connected layer will each get their own bottleneck. This may be "
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"combined or split depending on your overall architecture configuration settings."),
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"datatype": bool,
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"group": "bottleneck",
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"fixed": True},
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# Intermediate Layers
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"fc_depth": {
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"default": 1,
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"info": (
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"The number of consecutive Dense (fully connected) layers to include in each "
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"side's intermediate layer."),
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"datatype": int,
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"rounding": 1,
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"min_max": (0, 16),
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"group": "hidden layers",
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"fixed": True},
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"fc_min_filters": {
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"default": 1024,
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"info": (
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"The number of filters to use for the initial fully connected layer. The number of "
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"nodes actually used is: fc_min_filters x fc_dimensions x fc_dimensions.\nNB: This "
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"value may be scaled down, depending on output resolution."),
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"datatype": int,
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"rounding": 16,
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"min_max": (16, 5120),
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"group": "hidden layers",
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"fixed": True},
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"fc_max_filters": {
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"default": 1024,
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"info": (
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"This is the number of filters to be used in the final reshape layer at the end of "
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"the fully connected layers. The actual number of nodes used for the final fully "
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"connected layer is: fc_min_filters x fc_dimensions x fc_dimensions.\nNB: This value "
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"may be scaled down, depending on output resolution."),
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"datatype": int,
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"rounding": 64,
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"min_max": (128, 5120),
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"group": "hidden layers",
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"fixed": True},
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"fc_dimensions": {
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"default": 4,
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"info": (
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"The height and width dimension for the final reshape layer at the end of the "
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"fully connected layers.\nNB: The total number of nodes within the final fully "
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"connected layer will be: fc_dimensions x fc_dimensions x fc_max_filters."),
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"datatype": int,
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"rounding": 1,
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"min_max": (1, 16),
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"group": "hidden layers",
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"fixed": True},
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"fc_filter_slope": {
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"default": -0.5,
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"info": (
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"The rate that the filters move from the minimum number of filters to the maximum "
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"number of filters. EG:\n"
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"Negative numbers will change the number of filters quicker at first and slow down "
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"each layer.\n"
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"Positive numbers will change the number of filters slower at first but then speed "
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"up each layer.\n"
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"0.0 - This will change at a linear rate (i.e. the same number of filters will be "
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"changed at each layer)."),
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"datatype": float,
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"min_max": (-.99, .99),
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"rounding": 2,
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"group": "hidden layers",
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"fixed": True},
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"fc_dropout": {
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"default": 0.0,
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"info": (
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"Dropout is a form of regularization that can prevent a model from over-fitting "
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"and help to keep neurons 'alive'. 0.5 will dropout half the connections between each "
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"fully connected layer, 0.25 will dropout a quarter of the connections etc. Set to "
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"0.0 to disable."),
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"datatype": float,
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"rounding": 2,
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"min_max": (0.0, 0.99),
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"group": "hidden layers",
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"fixed": False},
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"fc_upsampler": {
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"default": "upsample2d",
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"info": (
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"The type of dimensional upsampling to perform at the end of the fully connected "
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"layers, if upsamples > 0. The number of filters used for the upscale layers will be "
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"the value given in 'fc_upsample_filters'."
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"\n\tupsample2d - A lightweight and VRAM friendly method. 'quick and dirty' but does "
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"not learn any parameters"
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"\n\tsubpixel - Sub-pixel upscaler using depth-to-space which may require more "
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"VRAM."
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"\n\tresize_images - Uses the Keras resize_image function to save about half as much "
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"vram as the heaviest methods."
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"\n\tupscale_fast - Developed by Andenixa. Focusses on speed to upscale, but "
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"requires more VRAM."
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"\n\tupscale_hybrid - Developed by Andenixa. Uses a combination of PixelShuffler and "
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"Upsampling2D to upscale, saving about 1/3rd of VRAM of the heaviest methods."),
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"datatype": str,
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"choices": ["resize_images", "subpixel", "upscale_fast", "upscale_hybrid", "upsample2d"],
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"group": "hidden layers",
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"gui_radio": False,
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"fixed": True},
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"fc_upsamples": {
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"default": 1,
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"info": (
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"Some upsampling can occur within the Fully Connected layers rather than in the "
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"Decoder to increase the dimensional space. Set how many upscale layers should occur "
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"within the Fully Connected layers."),
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"datatype": int,
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"min_max": (0, 4),
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"rounding": 1,
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"group": "hidden layers",
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"fixed": True},
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"fc_upsample_filters": {
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"default": 512,
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"info": (
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"If you have selected an upsampler which requires filters (i.e. any upsampler with "
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"the exception of Upsampling2D), then this is the number of filters to be used for "
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"the upsamplers within the fully connected layers, NB: This value may be scaled "
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"down, depending on output resolution. Also note, that this figure will dictate the "
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"number of filters used for the G-Block, if selected."),
|
|
"datatype": int,
|
|
"rounding": 64,
|
|
"min_max": (128, 5120),
|
|
"group": "hidden layers",
|
|
"fixed": True},
|
|
|
|
# G-Block
|
|
"fc_gblock_depth": {
|
|
"default": 3,
|
|
"info": (
|
|
"The number of consecutive Dense (fully connected) layers to include in the "
|
|
"G-Block shared layer."),
|
|
"datatype": int,
|
|
"rounding": 1,
|
|
"min_max": (1, 16),
|
|
"group": "g-block hidden layers",
|
|
"fixed": True},
|
|
"fc_gblock_min_nodes": {
|
|
"default": 512,
|
|
"info": "The number of nodes to use for the initial G-Block shared fully connected layer.",
|
|
"datatype": int,
|
|
"rounding": 64,
|
|
"min_max": (128, 5120),
|
|
"group": "g-block hidden layers",
|
|
"fixed": True},
|
|
"fc_gblock_max_nodes": {
|
|
"default": 512,
|
|
"info": "The number of nodes to use for the final G-Block shared fully connected layer.",
|
|
"datatype": int,
|
|
"rounding": 64,
|
|
"min_max": (128, 5120),
|
|
"group": "g-block hidden layers",
|
|
"fixed": True},
|
|
"fc_gblock_filter_slope": {
|
|
"default": -0.5,
|
|
"info": (
|
|
"The rate that the filters move from the minimum number of filters to the maximum "
|
|
"number of filters for the G-Block shared layers. EG:\n"
|
|
"Negative numbers will change the number of filters quicker at first and slow down "
|
|
"each layer.\n"
|
|
"Positive numbers will change the number of filters slower at first but then speed "
|
|
"up each layer.\n"
|
|
"0.0 - This will change at a linear rate (i.e. the same number of filters will be "
|
|
"changed at each layer)."),
|
|
"datatype": float,
|
|
"min_max": (-.99, .99),
|
|
"rounding": 2,
|
|
"group": "g-block hidden layers",
|
|
"fixed": True},
|
|
"fc_gblock_dropout": {
|
|
"default": 0.0,
|
|
"info": (
|
|
"Dropout is a regularization technique that can prevent a model from over-fitting "
|
|
"and help to keep neurons 'alive'. 0.5 will dropout half the connections between "
|
|
"each fully connected layer, 0.25 will dropout a quarter of the connections etc. Set "
|
|
"to 0.0 to disable."),
|
|
"datatype": float,
|
|
"rounding": 2,
|
|
"min_max": (0.0, 0.99),
|
|
"group": "g-block hidden layers",
|
|
"fixed": False},
|
|
|
|
# Decoder
|
|
"dec_upscale_method": {
|
|
"default": "subpixel",
|
|
"info": (
|
|
"The method to use for the upscales within the decoder. Images are upscaled "
|
|
"multiple times within the decoder as the network learns to reconstruct the face."
|
|
"\n\tsubpixel - Sub-pixel upscaler using depth-to-space which requires more "
|
|
"VRAM."
|
|
"\n\tresize_images - Uses the Keras resize_image function to save about half as much "
|
|
"vram as the heaviest methods."
|
|
"\n\tupscale_fast - Developed by Andenixa. Focusses on speed to upscale, but "
|
|
"requires more VRAM."
|
|
"\n\tupscale_hybrid - Developed by Andenixa. Uses a combination of PixelShuffler and "
|
|
"Upsampling2D to upscale, saving about 1/3rd of VRAM of the heaviest methods."
|
|
"\n\tupscale_dny - An alternative upscale implementation using Upsampling2D to "
|
|
"upsale."),
|
|
"datatype": str,
|
|
"choices": ["subpixel", "resize_images", "upscale_fast", "upscale_hybrid", "upscale_dny"],
|
|
"gui_radio": True,
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_upscales_in_fc": {
|
|
"default": 0,
|
|
"min_max": (0, 6),
|
|
"rounding": 1,
|
|
"info": (
|
|
"It is possible to place some of the upscales at the end of the fully connected "
|
|
"model. For models with split decoders, but a shared fully connected layer, this "
|
|
"would have the effect of saving some VRAM but possibly at the cost of introducing "
|
|
"artefacts. For models with a shared decoder but split fully connected layers, this "
|
|
"would have the effect of increasing VRAM usage by processing some of the upscales "
|
|
"for each side rather than together."),
|
|
"datatype": int,
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_norm": {
|
|
"default": "none",
|
|
"info": (
|
|
"Normalization to apply to apply after each upscale."
|
|
"\n\tnone - Do not apply a normalization layer"
|
|
"\n\tbatch - Apply Batch Normalization"
|
|
"\n\tgroup - Apply Group Normalization"
|
|
"\n\tinstance - Apply Instance Normalization"
|
|
"\n\tlayer - Apply Layer Normalization (Ba et al., 2016)"
|
|
"\n\trms - Apply Root Mean Squared Layer Normalization (Zhang et al., 2019). A "
|
|
"simplified version of Layer Normalization with reduced overhead."),
|
|
"datatype": str,
|
|
"gui_radio": True,
|
|
"choices": ["none", "batch", "group", "instance", "layer", "rms"],
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_min_filters": {
|
|
"default": 64,
|
|
"info": (
|
|
"The minimum number of filters to use in decoder upscalers (i.e. the number of "
|
|
"filters to use for the final upscale layer)."),
|
|
"datatype": int,
|
|
"min_max": (16, 512),
|
|
"rounding": 16,
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_max_filters": {
|
|
"default": 512,
|
|
"info": (
|
|
"The maximum number of filters to use in decoder upscalers (i.e. the number of "
|
|
"filters to use for the first upscale layer)."),
|
|
"datatype": int,
|
|
"min_max": (256, 5120),
|
|
"rounding": 64,
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_slope_mode": {
|
|
"default": "full",
|
|
"info": (
|
|
"Alters the action of the filter slope.\n"
|
|
"\n\tfull: The number of filters at each upscale layer will reduce from the chosen "
|
|
"max_filters at the first layer to the chosen min_filters at the last layer as "
|
|
"dictated by the dec_filter_slope."
|
|
"\n\tcap_max: The filters will decline at a fixed rate from each upscale to the next "
|
|
"based on the filter_slope setting. If there are more upscales than filters, "
|
|
"then the earliest upscales will be capped at the max_filter value until the filters "
|
|
"can reduce to the min_filters value at the final upscale. (EG: 512 -> 512 -> 512 -> "
|
|
"256 -> 128 -> 64)."
|
|
"\n\tcap_min: The filters will decline at a fixed rate from each upscale to the next "
|
|
"based on the filter_slope setting. If there are more upscales than filters, then "
|
|
"the earliest upscales will drop their filters until the min_filter value is met and "
|
|
"repeat the min_filter value for the remaining upscales. (EG: 512 -> 256 -> 128 -> "
|
|
"64 -> 64 -> 64)."),
|
|
"choices": ["full", "cap_max", "cap_min"],
|
|
"group": "decoder",
|
|
"fixed": True,
|
|
"gui_radio": True},
|
|
"dec_filter_slope": {
|
|
"default": -0.45,
|
|
"info": (
|
|
"The rate that the filters reduce at each upscale layer.\n"
|
|
"\n\tFull Slope Mode: Negative numbers will drop the number of filters quicker at "
|
|
"first and slow down each upscale. Positive numbers will drop the number of filters "
|
|
"slower at first but then speed up each upscale. A value of 0.0 will reduce at a "
|
|
"linear rate (i.e. the same number of filters will be reduced at each upscale).\n"
|
|
"\n\tCap Min/Max Slope Mode: Only positive values will work here. Negative values "
|
|
"will automatically be converted to their positive counterpart. A value of 0.5 will "
|
|
"halve the number of filters at each upscale until the minimum value is reached. A "
|
|
"value of 0.33 will be reduce the number of filters by a third until the minimum "
|
|
"value is reached etc."),
|
|
"datatype": float,
|
|
"min_max": (-.99, .99),
|
|
"rounding": 2,
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_res_blocks": {
|
|
"default": 1,
|
|
"info": (
|
|
"The number of Residual Blocks to apply to each upscale layer. Set to 0 to disable "
|
|
"residual blocks entirely."),
|
|
"datatype": int,
|
|
"rounding": 1,
|
|
"min_max": (0, 8),
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_output_kernel": {
|
|
"default": 5,
|
|
"info": "The kernel size to apply to the final Convolution layer.",
|
|
"datatype": int,
|
|
"rounding": 2,
|
|
"min_max": (1, 9),
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_gaussian": {
|
|
"default": True,
|
|
"info": (
|
|
"Gaussian Noise acts as a regularization technique for preventing overfitting of "
|
|
"data."
|
|
"\n\tTrue - Apply a Gaussian Noise layer to each upscale."
|
|
"\n\tFalse - Don't apply a Gaussian Noise layer to each upscale."),
|
|
"datatype": bool,
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
"dec_skip_last_residual": {
|
|
"default": True,
|
|
"info": (
|
|
"If Residual blocks have been enabled, enabling this option will not apply a "
|
|
"Residual block to the final upscaler."
|
|
"\n\tTrue - Don't apply a Residual block to the final upscale."
|
|
"\n\tFalse - Apply a Residual block to all upscale layers."),
|
|
"datatype": bool,
|
|
"group": "decoder",
|
|
"fixed": True},
|
|
|
|
# Weight management
|
|
"freeze_layers": {
|
|
"default": "keras_encoder",
|
|
"info": (
|
|
"If the command line option 'freeze-weights' is enabled, then the layers indicated "
|
|
"here will be frozen the next time the model starts up. NB: Not all architectures "
|
|
"contain all of the layers listed here, so any layers marked for freezing that are "
|
|
"not within your chosen architecture will be ignored. EG:\n If 'split fc' has "
|
|
"been selected, then 'fc_a' and 'fc_b' are available for freezing. If it has "
|
|
"not been selected then 'fc_both' is available for freezing."),
|
|
"datatype": list,
|
|
"choices": ["encoder", "keras_encoder", "fc_a", "fc_b", "fc_both", "fc_shared",
|
|
"fc_gblock", "g_block_a", "g_block_b", "g_block_both", "decoder_a",
|
|
"decoder_b", "decoder_both"],
|
|
"group": "weights",
|
|
"fixed": False},
|
|
"load_layers": {
|
|
"default": "encoder",
|
|
"info": (
|
|
"If the command line option 'load-weights' is populated, then the layers indicated "
|
|
"here will be loaded from the given weights file if starting a new model. NB Not all "
|
|
"architectures contain all of the layers listed here, so any layers marked for "
|
|
"loading that are not within your chosen architecture will be ignored. EG:\n If "
|
|
"'split fc' has been selected, then 'fc_a' and 'fc_b' are available for loading. If "
|
|
"it has not been selected then 'fc_both' is available for loading."),
|
|
"datatype": list,
|
|
"choices": ["encoder", "fc_a", "fc_b", "fc_both", "fc_shared", "fc_gblock", "g_block_a",
|
|
"g_block_b", "g_block_both", "decoder_a", "decoder_b", "decoder_both"],
|
|
"group": "weights",
|
|
"fixed": True},
|
|
|
|
# # SPECIFIC ENCODER SETTINGS # #
|
|
# Faceswap Original
|
|
"fs_original_depth": {
|
|
"default": 4,
|
|
"info": "Faceswap Encoder only: The number of convolutions to perform within the encoder.",
|
|
"datatype": int,
|
|
"min_max": (2, 10),
|
|
"rounding": 1,
|
|
"group": "faceswap encoder configuration",
|
|
"fixed": True},
|
|
"fs_original_min_filters": {
|
|
"default": 128,
|
|
"info": (
|
|
"Faceswap Encoder only: The minumum number of filters to use for encoder "
|
|
"convolutions. (i.e. the number of filters to use for the first encoder layer)."),
|
|
"datatype": int,
|
|
"min_max": (16, 2048),
|
|
"rounding": 64,
|
|
"group": "faceswap encoder configuration",
|
|
"fixed": True},
|
|
"fs_original_max_filters": {
|
|
"default": 1024,
|
|
"info": (
|
|
"Faceswap Encoder only: The maximum number of filters to use for encoder "
|
|
"convolutions. (i.e. the number of filters to use for the final encoder layer)."),
|
|
"datatype": int,
|
|
"min_max": (256, 8192),
|
|
"rounding": 128,
|
|
"group": "faceswap encoder configuration",
|
|
"fixed": True},
|
|
"fs_original_use_alt": {
|
|
"default": False,
|
|
"info": (
|
|
"Use a slightly alternate version of the Faceswap Encoder."
|
|
"\n\tTrue - Use the alternate variation of the Faceswap Encoder."
|
|
"\n\tFalse - Use the original Faceswap Encoder."),
|
|
"datatype": bool,
|
|
"group": "faceswap encoder configuration",
|
|
"fixed": True},
|
|
|
|
# MobileNet
|
|
"mobilenet_width": {
|
|
"default": 1.0,
|
|
"info": (
|
|
"The width multiplier for mobilenet encoders. Controls the width of the "
|
|
"network. Values less than 1.0 proportionally decrease the number of filters within "
|
|
"each layer. Values greater than 1.0 proportionally increase the number of filters "
|
|
"within each layer. 1.0 is the default number of layers used within the paper.\n"
|
|
"NB: This option is ignored for any non-mobilenet encoders.\n"
|
|
"NB: If loading ImageNet weights, then for MobilenetV1 only values of '0.25', "
|
|
"'0.5', '0.75' or '1.0 can be selected. For MobilenetV2 only values of '0.35', "
|
|
"'0.50', '0.75', '1.0', '1.3' or '1.4' can be selected. For mobilenet_v3 only values "
|
|
"of '0.75' or '1.0' can be selected"),
|
|
"datatype": float,
|
|
"min_max": (0.1, 2.0),
|
|
"rounding": 2,
|
|
"group": "mobilenet encoder configuration",
|
|
"fixed": True},
|
|
"mobilenet_depth": {
|
|
"default": 1,
|
|
"info": (
|
|
"The depth multiplier for MobilenetV1 encoder. This is the depth multiplier "
|
|
"for depthwise convolution (known as the resolution multiplier within the original "
|
|
"paper).\n"
|
|
"NB: This option is only used for MobilenetV1 and is ignored for all other "
|
|
"encoders.\n"
|
|
"NB: If loading ImageNet weights, this must be set to 1."),
|
|
"datatype": int,
|
|
"min_max": (1, 10),
|
|
"rounding": 1,
|
|
"group": "mobilenet encoder configuration",
|
|
"fixed": True},
|
|
"mobilenet_dropout": {
|
|
"default": 0.001,
|
|
"info": (
|
|
"The dropout rate for MobilenetV1 encoder.\n"
|
|
"NB: This option is only used for MobilenetV1 and is ignored for all other "
|
|
"encoders."),
|
|
"datatype": float,
|
|
"min_max": (0.001, 2.0),
|
|
"rounding": 3,
|
|
"group": "mobilenet encoder configuration",
|
|
"fixed": True},
|
|
"mobilenet_minimalistic": {
|
|
"default": False,
|
|
"info": (
|
|
"Use a minimilist version of MobilenetV3.\n"
|
|
"In addition to large and small models MobilenetV3 also contains so-called "
|
|
"minimalistic models, these models have the same per-layer dimensions characteristic "
|
|
"as MobilenetV3 however, they don't utilize any of the advanced blocks "
|
|
"(squeeze-and-excite units, hard-swish, and 5x5 convolutions). While these models "
|
|
"are less efficient on CPU, they are much more performant on GPU/DSP.\n"
|
|
"NB: This option is only used for MobilenetV3 and is ignored for all other "
|
|
"encoders.\n"),
|
|
"datatype": bool,
|
|
"group": "mobilenet encoder configuration",
|
|
"fixed": True},
|
|
}
|