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# currently only works with flux as support is not quite there yet
import argparse
import os.path
from collections import OrderedDict
parser = argparse.ArgumentParser()
parser.add_argument(
'input_path',
type=str,
help='Path to original sdxl model'
)
parser.add_argument(
'output_path',
type=str,
help='output path'
)
args = parser.parse_args()
args.input_path = os.path.abspath(args.input_path)
args.output_path = os.path.abspath(args.output_path)
from safetensors.torch import load_file, save_file
meta = OrderedDict()
meta['format'] = 'pt'
state_dict = load_file(args.input_path)
# peft doesnt have an alpha so we need to scale the weights
alpha_keys = [
'lora_transformer_single_transformer_blocks_0_attn_to_q.alpha' # flux
]
# keys where the rank is in the first dimension
rank_idx0_keys = [
'lora_transformer_single_transformer_blocks_0_attn_to_q.lora_down.weight'
# 'transformer.single_transformer_blocks.0.attn.to_q.lora_A.weight'
]
alpha = None
rank = None
for key in rank_idx0_keys:
if key in state_dict:
rank = int(state_dict[key].shape[0])
break
if rank is None:
raise ValueError(f'Could not find rank in state dict')
for key in alpha_keys:
if key in state_dict:
alpha = int(state_dict[key])
break
if alpha is None:
# set to rank if not found
alpha = rank
up_multiplier = alpha / rank
new_state_dict = {}
for key, value in state_dict.items():
if key.endswith('.alpha'):
continue
orig_dtype = value.dtype
new_val = value.float() * up_multiplier
new_key = key
new_key = new_key.replace('lora_transformer_', 'transformer.')
for i in range(100):
new_key = new_key.replace(f'transformer_blocks_{i}_', f'transformer_blocks.{i}.')
new_key = new_key.replace('lora_down', 'lora_A')
new_key = new_key.replace('lora_up', 'lora_B')
new_key = new_key.replace('_lora', '.lora')
new_key = new_key.replace('attn_', 'attn.')
new_key = new_key.replace('ff_', 'ff.')
new_key = new_key.replace('context_net_', 'context.net.')
new_key = new_key.replace('0_proj', '0.proj')
new_key = new_key.replace('norm_linear', 'norm.linear')
new_key = new_key.replace('norm_out_linear', 'norm_out.linear')
new_key = new_key.replace('to_out_', 'to_out.')
new_state_dict[new_key] = new_val.to(orig_dtype)
save_file(new_state_dict, args.output_path, meta)
print(f'Saved to {args.output_path}')
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