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https://github.com/comfyanonymous/ComfyUI.git
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66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
import logging
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import torch
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import comfy.utils
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import comfy.model_management
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import comfy.model_base
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from comfy.lora import weight_decompose, pad_tensor_to_shape
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from .base import WeightAdapterBase
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class LoHaAdapter(WeightAdapterBase):
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name = "loha"
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def __init__(self, loaded_keys, weights):
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self.loaded_keys = loaded_keys
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self.weights = weights
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@classmethod
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def load(
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cls,
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x: str,
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lora: dict[str, torch.Tensor],
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alpha: float,
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dora_scale: torch.Tensor,
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loaded_keys: set[str] = None,
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) -> "LoHaAdapter" | None:
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if loaded_keys is None:
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loaded_keys = set()
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hada_w1_a_name = "{}.hada_w1_a".format(x)
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hada_w1_b_name = "{}.hada_w1_b".format(x)
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hada_w2_a_name = "{}.hada_w2_a".format(x)
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hada_w2_b_name = "{}.hada_w2_b".format(x)
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hada_t1_name = "{}.hada_t1".format(x)
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hada_t2_name = "{}.hada_t2".format(x)
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if hada_w1_a_name in lora.keys():
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hada_t1 = None
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hada_t2 = None
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if hada_t1_name in lora.keys():
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hada_t1 = lora[hada_t1_name]
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hada_t2 = lora[hada_t2_name]
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loaded_keys.add(hada_t1_name)
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loaded_keys.add(hada_t2_name)
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weights = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale))
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loaded_keys.add(hada_w1_a_name)
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loaded_keys.add(hada_w1_b_name)
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loaded_keys.add(hada_w2_a_name)
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loaded_keys.add(hada_w2_b_name)
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return cls(loaded_keys, weights)
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else:
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return None
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def calculate_weight(
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self,
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weight,
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key,
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strength,
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strength_model,
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offset,
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function,
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intermediate_dtype=torch.float32,
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original_weight=None,
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):
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pass
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