mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-25 07:38:45 +00:00
5ca1549c96
Ran successfully to completion. But on a repeat run `convert_unclip` didn't pass the hash check for some reason. - fix inpainting model download urls - shows a progress bar for downloads - skips downloading existing files - uses a temporary file to prevent partial downloads - can do a dry run to check if url is valid `DRY_RUN=1 python scripts/prepare_test_weights.py` - displays the downloaded file hash
64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
import argparse
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
from diffusers import T2IAdapter # type: ignore
|
|
from torch import nn
|
|
|
|
from refiners.fluxion.model_converter import ModelConverter
|
|
from refiners.foundationals.latent_diffusion.t2i_adapter import ConditionEncoder, ConditionEncoderXL
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser(description="Convert a pretrained diffusers T2I-Adapter model to refiners")
|
|
parser.add_argument(
|
|
"--from",
|
|
type=str,
|
|
dest="source_path",
|
|
required=True,
|
|
help="Path or repository name of the source model. (e.g.: 'ip-adapter_sd15.bin').",
|
|
)
|
|
parser.add_argument(
|
|
"--to",
|
|
type=str,
|
|
dest="output_path",
|
|
default=None,
|
|
help=(
|
|
"Path to save the converted model (extension will be .safetensors). If not specified, the output path will"
|
|
" be the source path with the extension changed to .safetensors."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--half",
|
|
action="store_true",
|
|
dest="use_half",
|
|
default=False,
|
|
help="Use this flag to save the output file as half precision (default: full precision).",
|
|
)
|
|
parser.add_argument(
|
|
"--verbose",
|
|
action="store_true",
|
|
dest="verbose",
|
|
default=False,
|
|
help="Use this flag to print verbose output during conversion.",
|
|
)
|
|
args = parser.parse_args()
|
|
if args.output_path is None:
|
|
args.output_path = f"{Path(args.source_path).name}.safetensors"
|
|
assert args.output_path is not None
|
|
|
|
sdxl = "xl" in args.source_path
|
|
target = ConditionEncoderXL() if sdxl else ConditionEncoder()
|
|
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
|
|
source: nn.Module = T2IAdapter.from_pretrained( # type: ignore
|
|
pretrained_model_name_or_path=args.source_path,
|
|
low_cpu_mem_usage=False,
|
|
)
|
|
assert isinstance(source, nn.Module), "Source model is not a nn.Module"
|
|
|
|
x = torch.randn(1, 3, 1024, 1024) if sdxl else torch.randn(1, 3, 512, 512)
|
|
converter = ModelConverter(source_model=source, target_model=target, verbose=args.verbose)
|
|
if not converter.run(source_args=(x,)):
|
|
raise RuntimeError("Model conversion failed")
|
|
|
|
converter.save_to_safetensors(path=args.output_path, half=args.use_half)
|