mirror of
https://github.com/comfyanonymous/ComfyUI.git
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2ab9618732
* Correct calculate_weight and load for OFT * Correct calculate_weight and loading for BOFT
97 lines
3.0 KiB
Python
97 lines
3.0 KiB
Python
import logging
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from typing import Optional
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import torch
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import comfy.model_management
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from .base import WeightAdapterBase, weight_decompose
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class OFTAdapter(WeightAdapterBase):
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name = "oft"
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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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) -> Optional["OFTAdapter"]:
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if loaded_keys is None:
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loaded_keys = set()
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blocks_name = "{}.oft_blocks".format(x)
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rescale_name = "{}.rescale".format(x)
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blocks = None
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if blocks_name in lora.keys():
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blocks = lora[blocks_name]
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if blocks.ndim == 3:
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loaded_keys.add(blocks_name)
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else:
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blocks = None
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if blocks is None:
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return None
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rescale = None
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if rescale_name in lora.keys():
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rescale = lora[rescale_name]
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loaded_keys.add(rescale_name)
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weights = (blocks, rescale, alpha, dora_scale)
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return cls(loaded_keys, weights)
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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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v = self.weights
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blocks = v[0]
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rescale = v[1]
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alpha = v[2]
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dora_scale = v[3]
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blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype)
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if rescale is not None:
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rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype)
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block_num, block_size, *_ = blocks.shape
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try:
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# Get r
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I = torch.eye(block_size, device=blocks.device, dtype=blocks.dtype)
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# for Q = -Q^T
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q = blocks - blocks.transpose(1, 2)
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normed_q = q
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if alpha > 0: # alpha in oft/boft is for constraint
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q_norm = torch.norm(q) + 1e-8
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if q_norm > alpha:
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normed_q = q * alpha / q_norm
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# use float() to prevent unsupported type in .inverse()
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r = (I + normed_q) @ (I - normed_q).float().inverse()
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r = r.to(weight)
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_, *shape = weight.shape
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lora_diff = torch.einsum(
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"k n m, k n ... -> k m ...",
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(r * strength) - strength * I,
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weight.view(block_num, block_size, *shape),
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).view(-1, *shape)
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if dora_scale is not None:
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weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
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else:
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weight += function((strength * lora_diff).type(weight.dtype))
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except Exception as e:
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logging.error("ERROR {} {} {}".format(self.name, key, e))
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return weight
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