mirror of
https://github.com/huggingface/diffusers.git
synced 2025-12-06 12:34:13 +08:00
110 lines
4.1 KiB
Python
110 lines
4.1 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 The HuggingFace Inc. team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Conversion script for stable diffusion checkpoints which _only_ contain a controlnet."""
|
|
|
|
import argparse
|
|
|
|
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument(
|
|
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
|
|
)
|
|
parser.add_argument(
|
|
"--original_config_file",
|
|
type=str,
|
|
required=True,
|
|
help="The YAML config file corresponding to the original architecture.",
|
|
)
|
|
parser.add_argument(
|
|
"--num_in_channels",
|
|
default=None,
|
|
type=int,
|
|
help="The number of input channels. If `None` number of input channels will be automatically inferred.",
|
|
)
|
|
parser.add_argument(
|
|
"--image_size",
|
|
default=512,
|
|
type=int,
|
|
help=(
|
|
"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Diffusion v2"
|
|
" Base. Use 768 for Stable Diffusion v2."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--extract_ema",
|
|
action="store_true",
|
|
help=(
|
|
"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
|
|
" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
|
|
" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--upcast_attention",
|
|
action="store_true",
|
|
help=(
|
|
"Whether the attention computation should always be upcasted. This is necessary when running stable"
|
|
" diffusion 2.1."
|
|
),
|
|
)
|
|
parser.add_argument(
|
|
"--from_safetensors",
|
|
action="store_true",
|
|
help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
|
|
)
|
|
parser.add_argument(
|
|
"--to_safetensors",
|
|
action="store_true",
|
|
help="Whether to store pipeline in safetensors format or not.",
|
|
)
|
|
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
|
|
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
|
|
|
|
# small workaround to get argparser to parse a boolean input as either true _or_ false
|
|
def parse_bool(string):
|
|
if string == "True":
|
|
return True
|
|
elif string == "False":
|
|
return False
|
|
else:
|
|
raise ValueError(f"could not parse string as bool {string}")
|
|
|
|
parser.add_argument(
|
|
"--use_linear_projection", help="Override for use linear projection", required=False, type=parse_bool
|
|
)
|
|
|
|
parser.add_argument("--cross_attention_dim", help="Override for cross attention_dim", required=False, type=int)
|
|
|
|
args = parser.parse_args()
|
|
|
|
controlnet = download_controlnet_from_original_ckpt(
|
|
checkpoint_path=args.checkpoint_path,
|
|
original_config_file=args.original_config_file,
|
|
image_size=args.image_size,
|
|
extract_ema=args.extract_ema,
|
|
num_in_channels=args.num_in_channels,
|
|
upcast_attention=args.upcast_attention,
|
|
from_safetensors=args.from_safetensors,
|
|
device=args.device,
|
|
use_linear_projection=args.use_linear_projection,
|
|
cross_attention_dim=args.cross_attention_dim,
|
|
)
|
|
|
|
controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
|