diff options
| -rw-r--r-- | extensions-builtin/Lora/lora.py | 72 | ||||
| -rw-r--r-- | extensions-builtin/Lora/scripts/lora_script.py | 12 |
2 files changed, 66 insertions, 18 deletions
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py index 7c371deb..a737fec3 100644 --- a/extensions-builtin/Lora/lora.py +++ b/extensions-builtin/Lora/lora.py @@ -131,7 +131,7 @@ def load_lora(name, filename): with torch.no_grad():
module.weight.copy_(weight)
- module.to(device=devices.device, dtype=devices.dtype)
+ module.to(device=devices.cpu, dtype=devices.dtype)
if lora_key == "lora_up.weight":
lora_module.up = module
@@ -177,29 +177,69 @@ def load_loras(names, multipliers=None): loaded_loras.append(lora)
-def lora_forward(module, input, res):
- input = devices.cond_cast_unet(input)
- if len(loaded_loras) == 0:
- return res
+def lora_apply_weights(self: torch.nn.Conv2d | torch.nn.Linear):
+ """
+ Applies the currently selected set of Loras to the weight of torch layer self.
+ If weights already have this particular set of loras applied, does nothing.
+ If not, restores orginal weights from backup and alters weights according to loras.
+ """
- lora_layer_name = getattr(module, 'lora_layer_name', None)
- for lora in loaded_loras:
- module = lora.modules.get(lora_layer_name, None)
- if module is not None:
- if shared.opts.lora_apply_to_outputs and res.shape == input.shape:
- res = res + module.up(module.down(res)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
- else:
- res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+ current_names = getattr(self, "lora_current_names", ())
+ wanted_names = tuple((x.name, x.multiplier) for x in loaded_loras)
+
+ weights_backup = getattr(self, "lora_weights_backup", None)
+ if weights_backup is None:
+ weights_backup = self.weight.to(devices.cpu, copy=True)
+ self.lora_weights_backup = weights_backup
+
+ if current_names != wanted_names:
+ if weights_backup is not None:
+ self.weight.copy_(weights_backup)
+
+ lora_layer_name = getattr(self, 'lora_layer_name', None)
+ for lora in loaded_loras:
+ module = lora.modules.get(lora_layer_name, None)
+ if module is None:
+ continue
- return res
+ with torch.no_grad():
+ up = module.up.weight.to(self.weight.device, dtype=self.weight.dtype)
+ down = module.down.weight.to(self.weight.device, dtype=self.weight.dtype)
+
+ if up.shape[2:] == (1, 1) and down.shape[2:] == (1, 1):
+ updown = (up.squeeze(2).squeeze(2) @ down.squeeze(2).squeeze(2)).unsqueeze(2).unsqueeze(3)
+ else:
+ updown = up @ down
+
+ self.weight += updown * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
+
+ setattr(self, "lora_current_names", wanted_names)
def lora_Linear_forward(self, input):
- return lora_forward(self, input, torch.nn.Linear_forward_before_lora(self, input))
+ lora_apply_weights(self)
+
+ return torch.nn.Linear_forward_before_lora(self, input)
+
+
+def lora_Linear_load_state_dict(self: torch.nn.Linear, *args, **kwargs):
+ setattr(self, "lora_current_names", ())
+ setattr(self, "lora_weights_backup", None)
+
+ return torch.nn.Linear_load_state_dict_before_lora(self, *args, **kwargs)
def lora_Conv2d_forward(self, input):
- return lora_forward(self, input, torch.nn.Conv2d_forward_before_lora(self, input))
+ lora_apply_weights(self)
+
+ return torch.nn.Conv2d_forward_before_lora(self, input)
+
+
+def lora_Conv2d_load_state_dict(self: torch.nn.Conv2d, *args, **kwargs):
+ setattr(self, "lora_current_names", ())
+ setattr(self, "lora_weights_backup", None)
+
+ return torch.nn.Conv2d_load_state_dict_before_lora(self, *args, **kwargs)
def list_available_loras():
diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py index 2e860160..dc329e81 100644 --- a/extensions-builtin/Lora/scripts/lora_script.py +++ b/extensions-builtin/Lora/scripts/lora_script.py @@ -9,7 +9,9 @@ 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.Linear._load_from_state_dict = torch.nn.Linear_load_state_dict_before_lora
torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora
+ torch.nn.Conv2d._load_from_state_dict = torch.nn.Conv2d_load_state_dict_before_lora
def before_ui():
@@ -20,11 +22,19 @@ def before_ui(): if not hasattr(torch.nn, 'Linear_forward_before_lora'):
torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward
+if not hasattr(torch.nn, 'Linear_load_state_dict_before_lora'):
+ torch.nn.Linear_load_state_dict_before_lora = torch.nn.Linear._load_from_state_dict
+
if not hasattr(torch.nn, 'Conv2d_forward_before_lora'):
torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward
+if not hasattr(torch.nn, 'Conv2d_load_state_dict_before_lora'):
+ torch.nn.Conv2d_load_state_dict_before_lora = torch.nn.Conv2d._load_from_state_dict
+
torch.nn.Linear.forward = lora.lora_Linear_forward
+torch.nn.Linear._load_from_state_dict = lora.lora_Linear_load_state_dict
torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
+torch.nn.Conv2d._load_from_state_dict = lora.lora_Conv2d_load_state_dict
script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
script_callbacks.on_script_unloaded(unload)
@@ -33,6 +43,4 @@ script_callbacks.on_before_ui(before_ui) shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
"sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),
- "lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),
-
}))
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