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add support for loras trained on kohya's scripts 0.4.0 (alphas)
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@ -92,6 +92,15 @@ def load_lora(name, filename):
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keys_failed_to_match.append(key_diffusers)
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continue
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lora_module = lora.modules.get(key, None)
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if lora_module is None:
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lora_module = LoraUpDownModule()
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lora.modules[key] = lora_module
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if lora_key == "alpha":
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lora_module.alpha = weight.item()
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continue
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if type(sd_module) == torch.nn.Linear:
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module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
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elif type(sd_module) == torch.nn.Conv2d:
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@ -104,17 +113,12 @@ def load_lora(name, filename):
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module.to(device=devices.device, dtype=devices.dtype)
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lora_module = lora.modules.get(key, None)
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if lora_module is None:
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lora_module = LoraUpDownModule()
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lora.modules[key] = lora_module
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if lora_key == "lora_up.weight":
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lora_module.up = module
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elif lora_key == "lora_down.weight":
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lora_module.down = module
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else:
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assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight or lora_down.weight'
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assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'
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if len(keys_failed_to_match) > 0:
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print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")
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@ -161,7 +165,7 @@ def lora_forward(module, input, res):
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for lora in loaded_loras:
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module = lora.modules.get(lora_layer_name, None)
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if module is not None:
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res = res + module.up(module.down(input)) * lora.multiplier
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res = res + module.up(module.down(input)) * lora.multiplier * module.alpha / module.up.weight.shape[1]
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return res
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