mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2025-06-08 12:35:30 +08:00
95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
import logging
|
|
from typing import Optional
|
|
|
|
import torch
|
|
import comfy.model_management
|
|
from .base import WeightAdapterBase, weight_decompose
|
|
|
|
|
|
class OFTAdapter(WeightAdapterBase):
|
|
name = "oft"
|
|
|
|
def __init__(self, loaded_keys, weights):
|
|
self.loaded_keys = loaded_keys
|
|
self.weights = weights
|
|
|
|
@classmethod
|
|
def load(
|
|
cls,
|
|
x: str,
|
|
lora: dict[str, torch.Tensor],
|
|
alpha: float,
|
|
dora_scale: torch.Tensor,
|
|
loaded_keys: set[str] = None,
|
|
) -> Optional["OFTAdapter"]:
|
|
if loaded_keys is None:
|
|
loaded_keys = set()
|
|
blocks_name = "{}.oft_blocks".format(x)
|
|
rescale_name = "{}.rescale".format(x)
|
|
|
|
blocks = None
|
|
if blocks_name in lora.keys():
|
|
blocks = lora[blocks_name]
|
|
if blocks.ndim == 3:
|
|
loaded_keys.add(blocks_name)
|
|
|
|
rescale = None
|
|
if rescale_name in lora.keys():
|
|
rescale = lora[rescale_name]
|
|
loaded_keys.add(rescale_name)
|
|
|
|
if blocks is not None:
|
|
weights = (blocks, rescale, alpha, dora_scale)
|
|
return cls(loaded_keys, weights)
|
|
else:
|
|
return None
|
|
|
|
def calculate_weight(
|
|
self,
|
|
weight,
|
|
key,
|
|
strength,
|
|
strength_model,
|
|
offset,
|
|
function,
|
|
intermediate_dtype=torch.float32,
|
|
original_weight=None,
|
|
):
|
|
v = self.weights
|
|
blocks = v[0]
|
|
rescale = v[1]
|
|
alpha = v[2]
|
|
dora_scale = v[3]
|
|
|
|
blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype)
|
|
if rescale is not None:
|
|
rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype)
|
|
|
|
block_num, block_size, *_ = blocks.shape
|
|
|
|
try:
|
|
# Get r
|
|
I = torch.eye(block_size, device=blocks.device, dtype=blocks.dtype)
|
|
# for Q = -Q^T
|
|
q = blocks - blocks.transpose(1, 2)
|
|
normed_q = q
|
|
if alpha > 0: # alpha in oft/boft is for constraint
|
|
q_norm = torch.norm(q) + 1e-8
|
|
if q_norm > alpha:
|
|
normed_q = q * alpha / q_norm
|
|
# use float() to prevent unsupported type in .inverse()
|
|
r = (I + normed_q) @ (I - normed_q).float().inverse()
|
|
r = r.to(original_weight)
|
|
lora_diff = torch.einsum(
|
|
"k n m, k n ... -> k m ...",
|
|
(r * strength) - strength * I,
|
|
original_weight,
|
|
)
|
|
if dora_scale is not None:
|
|
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
|
|
else:
|
|
weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
|
|
except Exception as e:
|
|
logging.error("ERROR {} {} {}".format(self.name, key, e))
|
|
return weight
|