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35 lines
1.1 KiB
Python
35 lines
1.1 KiB
Python
from typing import Dict, Optional
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import torch
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from invokeai.backend.patches.layers.lora_layer_base import LoRALayerBase
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from invokeai.backend.util.calc_tensor_size import calc_tensor_size
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class FullLayer(LoRALayerBase):
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def __init__(self, weight: torch.Tensor, bias: Optional[torch.Tensor]):
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super().__init__(alpha=None, bias=bias)
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self.weight = torch.nn.Parameter(weight)
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@classmethod
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def from_state_dict_values(
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cls,
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values: Dict[str, torch.Tensor],
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):
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layer = cls(weight=values["diff"], bias=values.get("diff_b", None))
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cls.warn_on_unhandled_keys(values=values, handled_keys={"diff", "diff_b"})
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return layer
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def _rank(self) -> int | None:
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return None
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def get_weight(self, orig_weight: torch.Tensor) -> torch.Tensor:
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return self.weight
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def to(self, device: torch.device | None = None, dtype: torch.dtype | None = None):
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super().to(device=device, dtype=dtype)
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self.weight = self.weight.to(device=device, dtype=dtype)
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def calc_size(self) -> int:
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return super().calc_size() + calc_tensor_size(self.weight)
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