fixes related to merge

1 files changed, 45 insertions, 29 deletions

diff --git a/modules/hypernetwork/hypernetwork.py b/modules/hypernetwork/hypernetwork.py
index a3d6a47e..aa701bda 100644
--- a/modules/hypernetwork/hypernetwork.py
+++ b/modules/hypernetwork/hypernetwork.py
@@ -26,10 +26,11 @@ class HypernetworkModule(torch.nn.Module):
         if state_dict is not None:

             self.load_state_dict(state_dict, strict=True)

         else:

-            self.linear1.weight.data.fill_(0.0001)

-            self.linear1.bias.data.fill_(0.0001)

-            self.linear2.weight.data.fill_(0.0001)

-            self.linear2.bias.data.fill_(0.0001)

+

+            self.linear1.weight.data.normal_(mean=0.0, std=0.01)

+            self.linear1.bias.data.zero_()

+            self.linear2.weight.data.normal_(mean=0.0, std=0.01)

+            self.linear2.bias.data.zero_()

 

         self.to(devices.device)

 

@@ -92,41 +93,54 @@ class Hypernetwork:
         self.sd_checkpoint_name = state_dict.get('sd_checkpoint_name', None)

 

 

-def load_hypernetworks(path):

+def list_hypernetworks(path):

     res = {}

+    for filename in glob.iglob(os.path.join(path, '**/*.pt'), recursive=True):

+        name = os.path.splitext(os.path.basename(filename))[0]

+        res[name] = filename

+    return res

 

-    for filename in glob.iglob(path + '**/*.pt', recursive=True):

+

+def load_hypernetwork(filename):

+    path = shared.hypernetworks.get(filename, None)

+    if path is not None:

+        print(f"Loading hypernetwork {filename}")

         try:

-            hn = Hypernetwork()

-            hn.load(filename)

-            res[hn.name] = hn

+            shared.loaded_hypernetwork = Hypernetwork()

+            shared.loaded_hypernetwork.load(path)

+

         except Exception:

-            print(f"Error loading hypernetwork {filename}", file=sys.stderr)

+            print(f"Error loading hypernetwork {path}", file=sys.stderr)

             print(traceback.format_exc(), file=sys.stderr)

+    else:

+        if shared.loaded_hypernetwork is not None:

+            print(f"Unloading hypernetwork")

 

-    return res

+        shared.loaded_hypernetwork = None

 

 

-def attention_CrossAttention_forward(self, x, context=None, mask=None):

-    h = self.heads

+def apply_hypernetwork(hypernetwork, context, layer=None):

+    hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)

 

-    q = self.to_q(x)

-    context = default(context, x)

+    if hypernetwork_layers is None:

+        return context, context

 

-    hypernetwork_layers = (shared.hypernetwork.layers if shared.hypernetwork is not None else {}).get(context.shape[2], None)

+    if layer is not None:

+        layer.hyper_k = hypernetwork_layers[0]

+        layer.hyper_v = hypernetwork_layers[1]

 

-    if hypernetwork_layers is not None:

-        hypernetwork_k, hypernetwork_v = hypernetwork_layers

+    context_k = hypernetwork_layers[0](context)

+    context_v = hypernetwork_layers[1](context)

+    return context_k, context_v

 

-        self.hypernetwork_k = hypernetwork_k

-        self.hypernetwork_v = hypernetwork_v

 

-        context_k = hypernetwork_k(context)

-        context_v = hypernetwork_v(context)

-    else:

-        context_k = context

-        context_v = context

+def attention_CrossAttention_forward(self, x, context=None, mask=None):

+    h = self.heads

+

+    q = self.to_q(x)

+    context = default(context, x)

 

+    context_k, context_v = apply_hypernetwork(shared.loaded_hypernetwork, context, self)

     k = self.to_k(context_k)

     v = self.to_v(context_v)

 

@@ -151,7 +165,9 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
 def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):

     assert hypernetwork_name, 'embedding not selected'

 

-    shared.hypernetwork = shared.hypernetworks[hypernetwork_name]

+    path = shared.hypernetworks.get(hypernetwork_name, None)

+    shared.loaded_hypernetwork = Hypernetwork()

+    shared.loaded_hypernetwork.load(path)

 

     shared.state.textinfo = "Initializing hypernetwork training..."

     shared.state.job_count = steps

@@ -176,9 +192,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 

     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

     with torch.autocast("cuda"):

-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=512, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)

+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)

 

-    hypernetwork = shared.hypernetworks[hypernetwork_name]

+    hypernetwork = shared.loaded_hypernetwork

     weights = hypernetwork.weights()

     for weight in weights:

         weight.requires_grad = True

@@ -194,7 +210,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     if ititial_step > steps:

         return hypernetwork, filename

 

-    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)

+    pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)

     for i, (x, text) in pbar:

         hypernetwork.step = i + ititial_step