Squashed commit of fixing dropout silently

fix dropouts for future hypernetworks

add kwargs for Hypernetwork class

hypernet UI for gradio input

add recommended options

remove as options

revert adding options in ui

modules/hypernetworks/hypernetwork.py

@@ -34,7 +34,8 @@ class HypernetworkModule(torch.nn.Module):
     }
     activation_dict.update({cls_name.lower(): cls_obj for cls_name, cls_obj in inspect.getmembers(torch.nn.modules.activation) if inspect.isclass(cls_obj) and cls_obj.__module__ == 'torch.nn.modules.activation'})
 
-    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal', add_layer_norm=False, use_dropout=False, activate_output=False):
+    def __init__(self, dim, state_dict=None, layer_structure=None, activation_func=None, weight_init='Normal',
+                 add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
         super().__init__()
 
         assert layer_structure is not None, "layer_structure must not be None"
@@ -60,7 +61,7 @@ class HypernetworkModule(torch.nn.Module):
                 linears.append(torch.nn.LayerNorm(int(dim * layer_structure[i+1])))
 
             # Add dropout except last layer
-            if use_dropout and i < len(layer_structure) - 3:
+            if 'last_layer_dropout' in kwargs and kwargs['last_layer_dropout'] and use_dropout and i < len(layer_structure) - 2:
                 linears.append(torch.nn.Dropout(p=0.3))
 
         self.linear = torch.nn.Sequential(*linears)
@@ -126,7 +127,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False):
+    def __init__(self, name=None, enable_sizes=None, layer_structure=None, activation_func=None, weight_init=None, add_layer_norm=False, use_dropout=False, activate_output=False, **kwargs):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -139,11 +140,14 @@ class Hypernetwork:
         self.add_layer_norm = add_layer_norm
         self.use_dropout = use_dropout
         self.activate_output = activate_output
+        self.last_layer_dropout = kwargs['last_layer_dropout'] if 'last_layer_dropout' in kwargs else True
 
         for size in enable_sizes or []:
             self.layers[size] = (
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
-                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+                HypernetworkModule(size, None, self.layer_structure, self.activation_func, self.weight_init,
+                                   self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
             )
 
     def weights(self):
@@ -172,7 +176,8 @@ class Hypernetwork:
         state_dict['sd_checkpoint'] = self.sd_checkpoint
         state_dict['sd_checkpoint_name'] = self.sd_checkpoint_name
         state_dict['activate_output'] = self.activate_output
-
+        state_dict['last_layer_dropout'] = self.last_layer_dropout
+        
         torch.save(state_dict, filename)
 
     def load(self, filename):
@@ -193,12 +198,16 @@ class Hypernetwork:
         self.use_dropout = state_dict.get('use_dropout', False)
         print(f"Dropout usage is set to {self.use_dropout}" )
         self.activate_output = state_dict.get('activate_output', True)
+        print(f"Activate last layer is set to {self.activate_output}")
+        self.last_layer_dropout = state_dict.get('last_layer_dropout', False)
 
         for size, sd in state_dict.items():
             if type(size) == int:
                 self.layers[size] = (
-                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
-                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init, self.add_layer_norm, self.use_dropout, self.activate_output),
+                    HypernetworkModule(size, sd[0], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
+                    HypernetworkModule(size, sd[1], self.layer_structure, self.activation_func, self.weight_init,
+                                       self.add_layer_norm, self.use_dropout, self.activate_output, last_layer_dropout=self.last_layer_dropout),
                 )
 
         self.name = state_dict.get('name', self.name)

modules/ui.py

@@ -1238,8 +1238,8 @@ def create_ui(wrap_gradio_gpu_call):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
                     new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
-                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork", choices=modules.hypernetworks.ui.keys)
-                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
+                    new_hypernetwork_activation_func = gr.Dropdown(value="relu", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys)
+                    new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Normal is default, for experiments, relu-like - Kaiming, sigmoid-like - Xavier is recommended", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"])
                     new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
                     new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout")
                     overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")