index on master: 91c8d0d Merge pull request #7231 from EllangoK/master

1 files changed, 19 insertions, 2 deletions

diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py
index cb8f1d36..568a7675 100644
--- a/extensions-builtin/Lora/lora.py
+++ b/extensions-builtin/Lora/lora.py
@@ -12,7 +12,7 @@ re_unet_up_blocks = re.compile(r"lora_unet_up_blocks_(\d+)_attentions_(\d+)_(.+)
 re_text_block = re.compile(r"lora_te_text_model_encoder_layers_(\d+)_(.+)")

 

 

-def convert_diffusers_name_to_compvis(key):

+def convert_diffusers_name_to_compvis(key, is_sd2):

     def match(match_list, regex):

         r = re.match(regex, key)

         if not r:

@@ -34,6 +34,14 @@ def convert_diffusers_name_to_compvis(key):
         return f"diffusion_model_output_blocks_{m[0] * 3 + m[1]}_1_{m[2]}"

 

     if match(m, re_text_block):

+        if is_sd2:

+            if 'mlp_fc1' in m[1]:

+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc1', 'mlp_c_fc')}"

+            elif 'mlp_fc2' in m[1]:

+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('mlp_fc2', 'mlp_c_proj')}"

+            elif 'self_attn':

+                return f"model_transformer_resblocks_{m[0]}_{m[1].replace('self_attn', 'attn')}"

+

         return f"transformer_text_model_encoder_layers_{m[0]}_{m[1]}"

 

     return key

@@ -83,9 +91,10 @@ def load_lora(name, filename):
     sd = sd_models.read_state_dict(filename)

 

     keys_failed_to_match = []

+    is_sd2 = 'model_transformer_resblocks' in shared.sd_model.lora_layer_mapping

 

     for key_diffusers, weight in sd.items():

-        fullkey = convert_diffusers_name_to_compvis(key_diffusers)

+        fullkey = convert_diffusers_name_to_compvis(key_diffusers, is_sd2)

         key, lora_key = fullkey.split(".", 1)

 

         sd_module = shared.sd_model.lora_layer_mapping.get(key, None)

@@ -104,9 +113,13 @@ def load_lora(name, filename):
 

         if type(sd_module) == torch.nn.Linear:

             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)

+        elif type(sd_module) == torch.nn.modules.linear.NonDynamicallyQuantizableLinear:

+            module = torch.nn.modules.linear.NonDynamicallyQuantizableLinear(weight.shape[1], weight.shape[0], bias=False)

         elif type(sd_module) == torch.nn.Conv2d:

             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)

         else:

+            print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')

+            continue

             assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'

 

         with torch.no_grad():

@@ -182,6 +195,10 @@ def lora_Conv2d_forward(self, input):
     return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))

 

 

+def lora_NonDynamicallyQuantizableLinear_forward(self, input):

+    return lora_forward(self, input, torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora(self, input))

+

+

 def list_available_loras():

     available_loras.clear()