               "Allows for the experimentation of various standard Networks as the encoder and "
               "takes inspiration from Nvidia's StyleGAN for the Decoder. It is highly recommended "
               "to research to understand the parameters better.")
  
  _ENCODERS = ["densenet121", "densenet169", "densenet201", "inception_resnet_v2", "inception_v3",
               "mobilenet", "mobilenet_v2", "nasnet_large", "nasnet_mobile", "resnet50", "vgg16",
               "vgg19", "xception", "fs_original"]
  if get_backend() != "amd":
      _ENCODERS.extend(["efficientnet_b0", "efficientnet_b1", "efficientnet_b2", "efficientnet_b3",
                        "efficientnet_b4", "efficientnet_b5", "efficientnet_b6", "efficientnet_b7",
                        "efficientnet_v2_b0", "efficientnet_v2_b1", "efficientnet_v2_b2",
                        "efficientnet_v2_b3", "efficientnet_v2_l", "efficientnet_v2_m",
 
</s> remove _HELPTEXT = ("Phaze-A Model by TorzDF, with thanks to BirbFakes.\n"
             "Allows for the experimentation of various standard Networks as the encoder and "
             "takes inspiration from Nvidia's StyleGAN for the Decoder. It is highly recommended "
             "to research to understand the parameters better.")
 </s> add _HELPTEXT: str = (
    "Phaze-A Model by TorzDF, with thanks to BirbFakes.\n"
    "Allows for the experimentation of various standard Networks as the encoder and takes "
    "inspiration from Nvidia's StyleGAN for the Decoder. It is highly recommended to research to "
    "understand the parameters better.")

_ENCODERS: List[str] = [
    "densenet121", "densenet169", "densenet201", "inception_resnet_v2", "inception_v3",
    "mobilenet", "mobilenet_v2", "nasnet_large", "nasnet_mobile", "resnet50", "vgg16", "vgg19",
    "xception", "fs_original"] </s> remove         if get_backend() == "amd" and model.get("no_amd"):
            valid = [k for k, v in _MODEL_MAPPING.items() if not v.get('no_amd')]
 </s> add         if get_backend() == "amd" and model.no_amd:
            valid = [k for k, v in _MODEL_MAPPING.items() if not v.no_amd] </s> remove         tf_min = model.get("tf_min", 2.0)
 </s> add         tf_min = model.tf_min </s> remove         min_max=(64, 2048),
 </s> add         min_max=(16, 2048),
               "Allows for the experimentation of various standard Networks as the encoder and "
               "takes inspiration from Nvidia's StyleGAN for the Decoder. It is highly recommended "
               "to research to understand the parameters better.")
  



  if get_backend() != "amd":
      _ENCODERS.extend(["efficientnet_b0", "efficientnet_b1", "efficientnet_b2", "efficientnet_b3",
                        "efficientnet_b4", "efficientnet_b5", "efficientnet_b6", "efficientnet_b7",
                        "efficientnet_v2_b0", "efficientnet_v2_b1", "efficientnet_v2_b2",
                        "efficientnet_v2_b3", "efficientnet_v2_l", "efficientnet_v2_m",
keep keep keep keep replace replace replace keep keep keep keep keep
 Model updates
- Increase model summary width
- Phaze A updates
  - Update some min/max values
  - Add Decoder Filter Slope Mode
  - Add additional arguments for Upsampling2D
  - Adjust upsampling method for multiple upsamples in FC layers
  - Typing 
plugins/train/model/phaze_a_defaults.py