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# coding=utf-8

# 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)

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