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import argparse
from collections import OrderedDict
import torch
from toolkit.config_modules import ModelConfig
from toolkit.stable_diffusion_model import StableDiffusion
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'
)
parser.add_argument('--sdxl', action='store_true', help='is sdxl model')
parser.add_argument('--refiner', action='store_true', help='is refiner model')
parser.add_argument('--ssd', action='store_true', help='is ssd model')
parser.add_argument('--sd2', action='store_true', help='is sd 2 model')
args = parser.parse_args()
device = torch.device('cpu')
dtype = torch.float32
print(f"Loading model from {args.input_path}")
if args.sdxl:
adapter_id = "latent-consistency/lcm-lora-sdxl"
if args.refiner:
adapter_id = "latent-consistency/lcm-lora-sdxl"
elif args.ssd:
adapter_id = "latent-consistency/lcm-lora-ssd-1b"
else:
adapter_id = "latent-consistency/lcm-lora-sdv1-5"
diffusers_model_config = ModelConfig(
name_or_path=args.input_path,
is_xl=args.sdxl,
is_v2=args.sd2,
is_ssd=args.ssd,
dtype=dtype,
)
diffusers_sd = StableDiffusion(
model_config=diffusers_model_config,
device=device,
dtype=dtype,
)
diffusers_sd.load_model()
print(f"Loaded model from {args.input_path}")
diffusers_sd.pipeline.load_lora_weights(adapter_id)
diffusers_sd.pipeline.fuse_lora()
meta = OrderedDict()
diffusers_sd.save(args.output_path, meta=meta)
print(f"Saved to {args.output_path}")
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