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import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def convert_ldm_original(checkpoint_path, config_path, output_path):
config = OmegaConf.load(config_path)
state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
keys = list(state_dict.keys())
# extract state_dict for VQVAE
first_stage_dict = {}
first_stage_key = "first_stage_model."
for key in keys:
if key.startswith(first_stage_key):
first_stage_dict[key.replace(first_stage_key, "")] = state_dict[key]
# extract state_dict for UNetLDM
unet_state_dict = {}
unet_key = "model.diffusion_model."
for key in keys:
if key.startswith(unet_key):
unet_state_dict[key.replace(unet_key, "")] = state_dict[key]
vqvae_init_args = config.model.params.first_stage_config.params
unet_init_args = config.model.params.unet_config.params
vqvae = VQModel(**vqvae_init_args).eval()
vqvae.load_state_dict(first_stage_dict)
unet = UNetLDMModel(**unet_init_args).eval()
unet.load_state_dict(unet_state_dict)
noise_scheduler = DDIMScheduler(
timesteps=config.model.params.timesteps,
beta_schedule="scaled_linear",
beta_start=config.model.params.linear_start,
beta_end=config.model.params.linear_end,
clip_sample=False,
)
pipeline = LDMPipeline(vqvae, unet, noise_scheduler)
pipeline.save_pretrained(output_path)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", type=str, required=True)
parser.add_argument("--config_path", type=str, required=True)
parser.add_argument("--output_path", type=str, required=True)
args = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
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