diff options
Diffstat (limited to 'extensions-builtin/Lora')
| -rw-r--r-- | extensions-builtin/Lora/lora.py | 21 | ||||
| -rw-r--r-- | extensions-builtin/Lora/scripts/lora_script.py | 5 |
2 files changed, 24 insertions, 2 deletions
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index cb8f1d36..b7e775ae 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')}"
+ else:
+ 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()
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 2e860160..a385ae94 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -10,6 +10,7 @@ from modules import script_callbacks, ui_extra_networks, extra_networks, shared def unload():
torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora
torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+ torch.nn.modules.linear.NonDynamicallyQuantizableLinear.forward = torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora
def before_ui():
@@ -23,8 +24,12 @@ if not hasattr(torch.nn, 'Linear_forward_before_lora'): if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+if not hasattr(torch.nn, 'NonDynamicallyQuantizableLinear_forward_before_lora'):
+ torch.nn.NonDynamicallyQuantizableLinear_forward_before_lora = torch.nn.modules.linear.NonDynamicallyQuantizableLinear.forward
+
torch.nn.Linear.forward = lora.lora_Linear_forward
torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+torch.nn.modules.linear.NonDynamicallyQuantizableLinear.forward = lora.lora_NonDynamicallyQuantizableLinear_forward
script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
script_callbacks.on_script_unloaded(unload)
|