style: formatting

extensions-builtin/Lora/network_oft.py

@@ -37,7 +37,7 @@ class NetworkModuleOFT(network.NetworkModule):
     def apply_to(self):
         self.org_forward = self.org_module[0].forward
         self.org_module[0].forward = self.forward
-    
+
     def get_weight(self, oft_blocks, multiplier=None):
         block_Q = oft_blocks - oft_blocks.transpose(1, 2)
         norm_Q = torch.norm(block_Q.flatten())
@@ -66,7 +66,7 @@ class NetworkModuleOFT(network.NetworkModule):
         output_shape = self.oft_blocks.shape
 
         return self.finalize_updown(updown, orig_weight, output_shape)
-    
+
     def forward(self, x, y=None):
         x = self.org_forward(x)
         if self.multiplier() == 0.0:

extensions-builtin/Lora/networks.py

@@ -169,10 +169,6 @@ def load_network(name, network_on_disk):
             else:
                 emb_dict[vec_name] = weight
             bundle_embeddings[emb_name] = emb_dict
-        
-        #if key_network_without_network_parts == "oft_unet":
-        #    print(key_network_without_network_parts)
-        #    pass
 
         key = convert_diffusers_name_to_compvis(key_network_without_network_parts, is_sd2)
         sd_module = shared.sd_model.network_layer_mapping.get(key, None)
@@ -196,31 +192,8 @@ def load_network(name, network_on_disk):
                 sd_module = shared.sd_model.network_layer_mapping.get(key, None)
 
         elif sd_module is None and "oft_unet" in key_network_without_network_parts:
-        #    UNET_TARGET_REPLACE_MODULE_ALL_LINEAR = ["Transformer2DModel"]
-        #    UNET_TARGET_REPLACE_MODULE_CONV2D_3X3 = ["ResnetBlock2D", "Downsample2D", "Upsample2D"]
-            UNET_TARGET_REPLACE_MODULE_ATTN_ONLY = ["CrossAttention"]
-            # TODO: Change matchedm odules based on whether all linear, conv, etc
-
             key = key_network_without_network_parts.replace("oft_unet", "diffusion_model")
             sd_module = shared.sd_model.network_layer_mapping.get(key, None)
-            #key_no_suffix = key.rsplit("_to_", 1)[0]
-            ## Match all modules of class CrossAttention
-            #replace_module_list = []
-            #for module_type in UNET_TARGET_REPLACE_MODULE_ATTN_ONLY:
-            #    replace_module_list += [module for k, module in shared.sd_model.network_layer_mapping.items() if module_type in module.__class__.__name__]
-
-            #matched_module = replace_module_list.get(key_no_suffix, None)
-            #if key.endswith('to_q'):
-            #    sd_module = matched_module.to_q or None
-            #if key.endswith('to_k'):
-            #    sd_module = matched_module.to_k or None
-            #if key.endswith('to_v'):
-            #    sd_module = matched_module.to_v or None
-            #if key.endswith('to_out_0'):
-            #    sd_module = matched_module.to_out[0] or None
-            #if key.endswith('to_out_1'):
-            #    sd_module = matched_module.to_out[1] or None
-
 
         if sd_module is None:
             keys_failed_to_match[key_network] = key
@@ -242,14 +215,6 @@ def load_network(name, network_on_disk):
             raise AssertionError(f"Could not find a module type (out of {', '.join([x.__class__.__name__ for x in module_types])}) that would accept those keys: {', '.join(weights.w)}")
 
         net.modules[key] = net_module
-    
-    # replaces forward method of original Linear
-    # applied_to_count = 0
-    #for key, created_module in net.modules.items():
-    #    if isinstance(created_module, network_oft.NetworkModuleOFT):
-    #        net_module.apply_to()
-            #applied_to_count += 1
-    # print(f'Applied OFT modules: {applied_to_count}')
 
     embeddings = {}
     for emb_name, data in bundle_embeddings.items():