Spaces:
Runtime error
Runtime error
File size: 4,876 Bytes
22a452a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
"""
This script demonstrates how to extract a LoRA checkpoint from a fully finetuned model with the CogVideoX model.
To make it work for other models:
* Change the model class. Here we use `CogVideoXTransformer3DModel`. For Flux, it would be `FluxTransformer2DModel`,
for example. (TODO: more reason to add `AutoModel`).
* Spply path to the base checkpoint via `base_ckpt_path`.
* Supply path to the fully fine-tuned checkpoint via `--finetune_ckpt_path`.
* Change the `--rank` as needed.
Example usage:
```bash
python extract_lora_from_model.py \
--base_ckpt_path=THUDM/CogVideoX-5b \
--finetune_ckpt_path=finetrainers/cakeify-v0 \
--lora_out_path=cakeify_lora.safetensors
```
Script is adapted from
https://github.com/Stability-AI/stability-ComfyUI-nodes/blob/001154622564b17223ce0191803c5fff7b87146c/control_lora_create.py
"""
import argparse
import torch
from safetensors.torch import save_file
from tqdm.auto import tqdm
from diffusers import CogVideoXTransformer3DModel
RANK = 64
CLAMP_QUANTILE = 0.99
# Comes from
# https://github.com/Stability-AI/stability-ComfyUI-nodes/blob/001154622564b17223ce0191803c5fff7b87146c/control_lora_create.py#L9
def extract_lora(diff, rank):
# Important to use CUDA otherwise, very slow!
if torch.cuda.is_available():
diff = diff.to("cuda")
is_conv2d = len(diff.shape) == 4
kernel_size = None if not is_conv2d else diff.size()[2:4]
is_conv2d_3x3 = is_conv2d and kernel_size != (1, 1)
out_dim, in_dim = diff.size()[0:2]
rank = min(rank, in_dim, out_dim)
if is_conv2d:
if is_conv2d_3x3:
diff = diff.flatten(start_dim=1)
else:
diff = diff.squeeze()
U, S, Vh = torch.linalg.svd(diff.float())
U = U[:, :rank]
S = S[:rank]
U = U @ torch.diag(S)
Vh = Vh[:rank, :]
dist = torch.cat([U.flatten(), Vh.flatten()])
hi_val = torch.quantile(dist, CLAMP_QUANTILE)
low_val = -hi_val
U = U.clamp(low_val, hi_val)
Vh = Vh.clamp(low_val, hi_val)
if is_conv2d:
U = U.reshape(out_dim, rank, 1, 1)
Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
return (U.cpu(), Vh.cpu())
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--base_ckpt_path",
default=None,
type=str,
required=True,
help="Base checkpoint path from which the model was finetuned. Can be a model ID on the Hub.",
)
parser.add_argument(
"--base_subfolder",
default="transformer",
type=str,
help="subfolder to load the base checkpoint from if any.",
)
parser.add_argument(
"--finetune_ckpt_path",
default=None,
type=str,
required=True,
help="Fully fine-tuned checkpoint path. Can be a model ID on the Hub.",
)
parser.add_argument(
"--finetune_subfolder",
default=None,
type=str,
help="subfolder to load the fulle finetuned checkpoint from if any.",
)
parser.add_argument("--rank", default=64, type=int)
parser.add_argument("--lora_out_path", default=None, type=str, required=True)
args = parser.parse_args()
if not args.lora_out_path.endswith(".safetensors"):
raise ValueError("`lora_out_path` must end with `.safetensors`.")
return args
@torch.no_grad()
def main(args):
model_finetuned = CogVideoXTransformer3DModel.from_pretrained(
args.finetune_ckpt_path, subfolder=args.finetune_subfolder, torch_dtype=torch.bfloat16
)
state_dict_ft = model_finetuned.state_dict()
# Change the `subfolder` as needed.
base_model = CogVideoXTransformer3DModel.from_pretrained(
args.base_ckpt_path, subfolder=args.base_subfolder, torch_dtype=torch.bfloat16
)
state_dict = base_model.state_dict()
output_dict = {}
for k in tqdm(state_dict, desc="Extracting LoRA..."):
original_param = state_dict[k]
finetuned_param = state_dict_ft[k]
if len(original_param.shape) >= 2:
diff = finetuned_param.float() - original_param.float()
out = extract_lora(diff, RANK)
name = k
if name.endswith(".weight"):
name = name[: -len(".weight")]
down_key = "{}.lora_A.weight".format(name)
up_key = "{}.lora_B.weight".format(name)
output_dict[up_key] = out[0].contiguous().to(finetuned_param.dtype)
output_dict[down_key] = out[1].contiguous().to(finetuned_param.dtype)
prefix = "transformer" if "transformer" in base_model.__class__.__name__.lower() else "unet"
output_dict = {f"{prefix}.{k}": v for k, v in output_dict.items()}
save_file(output_dict, args.lora_out_path)
print(f"LoRA saved and it contains {len(output_dict)} keys.")
if __name__ == "__main__":
args = parse_args()
main(args)
|