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import os
from tqdm import tqdm
import argparse
from collections import OrderedDict
parser = argparse.ArgumentParser(description="Extract LoRA from Flex")
parser.add_argument("--base", type=str, default="ostris/Flex.1-alpha", help="Base model path")
parser.add_argument("--tuned", type=str, required=True, help="Tuned model path")
parser.add_argument("--output", type=str, required=True, help="Output path for lora")
parser.add_argument("--rank", type=int, default=32, help="LoRA rank for extraction")
parser.add_argument("--gpu", type=int, default=0, help="GPU to process extraction")
parser.add_argument("--full", action="store_true", help="Do a full transformer extraction, not just transformer blocks")
args = parser.parse_args()
if True:
# set cuda environment variable
os.environ["CUDA_VISIBLE_DEVICES"] = str(args.gpu)
import torch
from safetensors.torch import load_file, save_file
from lycoris.utils import extract_linear, extract_conv, make_sparse
from diffusers import FluxTransformer2DModel
base = args.base
tuned = args.tuned
output_path = args.output
dim = args.rank
os.makedirs(os.path.dirname(output_path), exist_ok=True)
state_dict_base = {}
state_dict_tuned = {}
output_dict = {}
@torch.no_grad()
def extract_diff(
base_unet,
db_unet,
mode="fixed",
linear_mode_param=0,
conv_mode_param=0,
extract_device="cpu",
use_bias=False,
sparsity=0.98,
# small_conv=True,
small_conv=False,
):
UNET_TARGET_REPLACE_MODULE = [
"Linear",
"Conv2d",
"LayerNorm",
"GroupNorm",
"GroupNorm32",
"LoRACompatibleLinear",
"LoRACompatibleConv"
]
LORA_PREFIX_UNET = "transformer"
def make_state_dict(
prefix,
root_module: torch.nn.Module,
target_module: torch.nn.Module,
target_replace_modules,
):
loras = {}
temp = {}
for name, module in root_module.named_modules():
if module.__class__.__name__ in target_replace_modules:
temp[name] = module
for name, module in tqdm(
list((n, m) for n, m in target_module.named_modules() if n in temp)
):
weights = temp[name]
lora_name = prefix + "." + name
# lora_name = lora_name.replace(".", "_")
layer = module.__class__.__name__
if 'transformer_blocks' not in lora_name and not args.full:
continue
if layer in {
"Linear",
"Conv2d",
"LayerNorm",
"GroupNorm",
"GroupNorm32",
"Embedding",
"LoRACompatibleLinear",
"LoRACompatibleConv"
}:
root_weight = module.weight
try:
if torch.allclose(root_weight, weights.weight):
continue
except:
continue
else:
continue
module = module.to(extract_device, torch.float32)
weights = weights.to(extract_device, torch.float32)
if mode == "full":
decompose_mode = "full"
elif layer == "Linear":
weight, decompose_mode = extract_linear(
(root_weight - weights.weight),
mode,
linear_mode_param,
device=extract_device,
)
if decompose_mode == "low rank":
extract_a, extract_b, diff = weight
elif layer == "Conv2d":
is_linear = root_weight.shape[2] == 1 and root_weight.shape[3] == 1
weight, decompose_mode = extract_conv(
(root_weight - weights.weight),
mode,
linear_mode_param if is_linear else conv_mode_param,
device=extract_device,
)
if decompose_mode == "low rank":
extract_a, extract_b, diff = weight
if small_conv and not is_linear and decompose_mode == "low rank":
dim = extract_a.size(0)
(extract_c, extract_a, _), _ = extract_conv(
extract_a.transpose(0, 1),
"fixed",
dim,
extract_device,
True,
)
extract_a = extract_a.transpose(0, 1)
extract_c = extract_c.transpose(0, 1)
loras[f"{lora_name}.lora_mid.weight"] = (
extract_c.detach().cpu().contiguous().half()
)
diff = (
(
root_weight
- torch.einsum(
"i j k l, j r, p i -> p r k l",
extract_c,
extract_a.flatten(1, -1),
extract_b.flatten(1, -1),
)
)
.detach()
.cpu()
.contiguous()
)
del extract_c
else:
module = module.to("cpu")
weights = weights.to("cpu")
continue
if decompose_mode == "low rank":
loras[f"{lora_name}.lora_A.weight"] = (
extract_a.detach().cpu().contiguous().half()
)
loras[f"{lora_name}.lora_B.weight"] = (
extract_b.detach().cpu().contiguous().half()
)
# loras[f"{lora_name}.alpha"] = torch.Tensor([extract_a.shape[0]]).half()
if use_bias:
diff = diff.detach().cpu().reshape(extract_b.size(0), -1)
sparse_diff = make_sparse(diff, sparsity).to_sparse().coalesce()
indices = sparse_diff.indices().to(torch.int16)
values = sparse_diff.values().half()
loras[f"{lora_name}.bias_indices"] = indices
loras[f"{lora_name}.bias_values"] = values
loras[f"{lora_name}.bias_size"] = torch.tensor(diff.shape).to(
torch.int16
)
del extract_a, extract_b, diff
elif decompose_mode == "full":
if "Norm" in layer:
w_key = "w_norm"
b_key = "b_norm"
else:
w_key = "diff"
b_key = "diff_b"
weight_diff = module.weight - weights.weight
loras[f"{lora_name}.{w_key}"] = (
weight_diff.detach().cpu().contiguous().half()
)
if getattr(weights, "bias", None) is not None:
bias_diff = module.bias - weights.bias
loras[f"{lora_name}.{b_key}"] = (
bias_diff.detach().cpu().contiguous().half()
)
else:
raise NotImplementedError
module = module.to("cpu", torch.bfloat16)
weights = weights.to("cpu", torch.bfloat16)
return loras
all_loras = {}
all_loras |= make_state_dict(
LORA_PREFIX_UNET,
base_unet,
db_unet,
UNET_TARGET_REPLACE_MODULE,
)
del base_unet, db_unet
if torch.cuda.is_available():
torch.cuda.empty_cache()
all_lora_name = set()
for k in all_loras:
lora_name, weight = k.rsplit(".", 1)
all_lora_name.add(lora_name)
print(len(all_lora_name))
return all_loras
# find all the .safetensors files and load them
print("Loading Base")
base_model = FluxTransformer2DModel.from_pretrained(base, subfolder="transformer", torch_dtype=torch.bfloat16)
print("Loading Tuned")
tuned_model = FluxTransformer2DModel.from_pretrained(tuned, subfolder="transformer", torch_dtype=torch.bfloat16)
output_dict = extract_diff(
base_model,
tuned_model,
mode="fixed",
linear_mode_param=dim,
conv_mode_param=dim,
extract_device="cuda",
use_bias=False,
sparsity=0.98,
small_conv=False,
)
meta = OrderedDict()
meta['format'] = 'pt'
save_file(output_dict, output_path, metadata=meta)
print("Done")
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