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fix typing for informative drawings convert script

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limiteinductive 2023-08-17 12:32:08 +02:00 committed by Benjamin Trom
parent 0fd46f9ec4
commit 9da00e6fcf
1 changed files with 12 additions and 15 deletions
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27
scripts/convert-informative-drawings-weights.py
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@ -5,32 +5,29 @@
import torch
from safetensors.torch import save_file
from refiners.fluxion.utils import (
create_state_dict_mapping,
convert_state_dict,
)
from refiners.fluxion.utils import create_state_dict_mapping, convert_state_dict, save_to_safetensors
from refiners.foundationals.latent_diffusion.preprocessors.informative_drawings import InformativeDrawings
from model import Generator
from model import Generator # type: ignore
@torch.no_grad()
def convert(checkpoint: str, device: torch.device) -> dict[str, torch.Tensor]:
src_model = Generator(3, 1, 3)
src_model.load_state_dict(torch.load(checkpoint, map_location=device))
src_model.eval()
def convert(checkpoint: str, device: torch.device | str) -> dict[str, torch.Tensor]:
src_model = Generator(3, 1, 3) # type: ignore
src_model.load_state_dict(torch.load(checkpoint, map_location=device)) # type: ignore
src_model.eval() # type: ignore
dst_model = InformativeDrawings()
x = torch.randn(1, 3, 512, 512)
mapping = create_state_dict_mapping(src_model, dst_model, [x])
state_dict = convert_state_dict(src_model.state_dict(), dst_model.state_dict(), mapping)
mapping = create_state_dict_mapping(source_model=src_model, target_model=dst_model, source_args=[x]) # type: ignore
assert mapping is not None, "Model conversion failed"
state_dict = convert_state_dict(source_state_dict=src_model.state_dict(), target_state_dict=dst_model.state_dict(), state_dict_mapping=mapping) # type: ignore
return {k: v.half() for k, v in state_dict.items()}
def main():
def main() -> None:
import argparse
parser = argparse.ArgumentParser()
@ -52,8 +49,8 @@ def main():
args = parser.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
tensors = convert(args.source, device)
save_file(tensors, args.output_file)
tensors = convert(checkpoint=args.source, device=device)
save_to_safetensors(path=args.output_file, tensors=tensors)
if __name__ == "__main__":