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36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
from typing import Dict
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import torch
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from invokeai.backend.patches.layers.base_layer_patch import BaseLayerPatch
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from invokeai.backend.patches.layers.dora_layer import DoRALayer
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from invokeai.backend.patches.layers.full_layer import FullLayer
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from invokeai.backend.patches.layers.ia3_layer import IA3Layer
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from invokeai.backend.patches.layers.loha_layer import LoHALayer
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from invokeai.backend.patches.layers.lokr_layer import LoKRLayer
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from invokeai.backend.patches.layers.lora_layer import LoRALayer
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from invokeai.backend.patches.layers.norm_layer import NormLayer
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def any_lora_layer_from_state_dict(state_dict: Dict[str, torch.Tensor]) -> BaseLayerPatch:
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# Detect layers according to LyCORIS detection logic(`weight_list_det`)
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# https://github.com/KohakuBlueleaf/LyCORIS/tree/8ad8000efb79e2b879054da8c9356e6143591bad/lycoris/modules
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if "dora_scale" in state_dict:
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return DoRALayer.from_state_dict_values(state_dict)
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elif "lora_up.weight" in state_dict:
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# LoRA a.k.a LoCon
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return LoRALayer.from_state_dict_values(state_dict)
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elif "hada_w1_a" in state_dict:
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return LoHALayer.from_state_dict_values(state_dict)
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elif "lokr_w1" in state_dict or "lokr_w1_a" in state_dict:
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return LoKRLayer.from_state_dict_values(state_dict)
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elif "diff" in state_dict:
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# Full a.k.a Diff
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return FullLayer.from_state_dict_values(state_dict)
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elif "on_input" in state_dict:
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return IA3Layer.from_state_dict_values(state_dict)
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elif "w_norm" in state_dict:
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return NormLayer.from_state_dict_values(state_dict)
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else:
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raise ValueError(f"Unsupported lora format: {state_dict.keys()}")
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