mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-09 23:12:02 +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
100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
import argparse
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
from diffusers import UNet2DConditionModel # type: ignore
|
|
from torch import nn
|
|
|
|
from refiners.fluxion.model_converter import ModelConverter
|
|
from refiners.foundationals.latent_diffusion import SD1UNet, SDXLUNet
|
|
|
|
|
|
class Args(argparse.Namespace):
|
|
source_path: str
|
|
output_path: str | None
|
|
half: bool
|
|
verbose: bool
|
|
|
|
|
|
def setup_converter(args: Args) -> ModelConverter:
|
|
# low_cpu_mem_usage=False stops some annoying console messages us to `pip install accelerate`
|
|
source: nn.Module = UNet2DConditionModel.from_pretrained( # type: ignore
|
|
pretrained_model_name_or_path=args.source_path,
|
|
subfolder="unet",
|
|
low_cpu_mem_usage=False,
|
|
)
|
|
source_in_channels: int = source.config.in_channels # type: ignore
|
|
source_clip_embedding_dim: int = source.config.cross_attention_dim # type: ignore
|
|
source_has_time_ids: bool = source.config.addition_embed_type == "text_time" # type: ignore
|
|
target = (
|
|
SDXLUNet(in_channels=source_in_channels) if source_has_time_ids else SD1UNet(in_channels=source_in_channels)
|
|
)
|
|
|
|
x = torch.randn(1, source_in_channels, 32, 32)
|
|
timestep = torch.tensor(data=[0])
|
|
clip_text_embeddings = torch.randn(1, 77, source_clip_embedding_dim)
|
|
|
|
target.set_timestep(timestep=timestep)
|
|
target.set_clip_text_embedding(clip_text_embedding=clip_text_embeddings)
|
|
added_cond_kwargs = {}
|
|
if source_has_time_ids:
|
|
added_cond_kwargs = {"text_embeds": torch.randn(1, 1280), "time_ids": torch.randn(1, 6)}
|
|
target.set_time_ids(time_ids=added_cond_kwargs["time_ids"])
|
|
target.set_pooled_text_embedding(pooled_text_embedding=added_cond_kwargs["text_embeds"])
|
|
|
|
target_args = (x,)
|
|
source_args = {
|
|
"positional": (x, timestep, clip_text_embeddings),
|
|
"keyword": {"added_cond_kwargs": added_cond_kwargs} if source_has_time_ids else {},
|
|
}
|
|
|
|
converter = ModelConverter(source_model=source, target_model=target, skip_output_check=True, verbose=args.verbose)
|
|
if not converter.run(
|
|
source_args=source_args,
|
|
target_args=target_args,
|
|
):
|
|
raise RuntimeError("Model conversion failed")
|
|
return converter
|
|
|
|
|
|
def main() -> None:
|
|
parser = argparse.ArgumentParser(
|
|
description="Converts a Diffusion UNet model to a Refiners SD1UNet or SDXLUNet model"
|
|
)
|
|
parser.add_argument(
|
|
"--from",
|
|
type=str,
|
|
dest="source_path",
|
|
default="runwayml/stable-diffusion-v1-5",
|
|
help=(
|
|
"Can be a path to a .bin file, a .safetensors file or a model name from the HuggingFace Hub. Default:"
|
|
" runwayml/stable-diffusion-v1-5"
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--to",
|
|
type=str,
|
|
dest="output_path",
|
|
default=None,
|
|
help=(
|
|
"Output path (.safetensors) for converted model. If not provided, the output path will be the same as the"
|
|
" source path."
|
|
),
|
|
)
|
|
parser.add_argument("--half", action="store_true", help="Convert to half precision. Default: True")
|
|
parser.add_argument(
|
|
"--verbose",
|
|
action="store_true",
|
|
default=False,
|
|
help="Prints additional information during conversion. Default: False",
|
|
)
|
|
args = parser.parse_args(namespace=Args())
|
|
if args.output_path is None:
|
|
args.output_path = f"{Path(args.source_path).stem}-unet.safetensors"
|
|
converter = setup_converter(args=args)
|
|
converter.save_to_safetensors(path=args.output_path, half=args.half)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|