116 lines
4.4 KiB
Python
116 lines
4.4 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Conversion script for the LDM checkpoints. """
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import argparse
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from diffusers.pipelines.stable_diffusion.convert_from_ckpt import load_pipeline_from_original_stable_diffusion_ckpt
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
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)
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# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
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parser.add_argument(
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"--original_config_file",
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default=None,
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type=str,
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help="The YAML config file corresponding to the original architecture.",
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)
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parser.add_argument(
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"--num_in_channels",
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default=None,
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type=int,
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help="The number of input channels. If `None` number of input channels will be automatically inferred.",
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)
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parser.add_argument(
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"--scheduler_type",
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default="pndm",
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type=str,
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help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
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)
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parser.add_argument(
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"--pipeline_type",
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default=None,
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type=str,
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help="The pipeline type. If `None` pipeline will be automatically inferred.",
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)
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parser.add_argument(
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"--image_size",
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default=None,
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type=int,
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help=(
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"The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
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" Base. Use 768 for Stable Diffusion v2."
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),
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)
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parser.add_argument(
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"--prediction_type",
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default=None,
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type=str,
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help=(
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"The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
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" Siffusion v2 Base. Use 'v-prediction' for Stable Diffusion v2."
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),
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)
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parser.add_argument(
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"--extract_ema",
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action="store_true",
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help=(
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"Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
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" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
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" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
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),
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)
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parser.add_argument(
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"--upcast_attention",
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default=False,
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type=bool,
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help=(
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"Whether the attention computation should always be upcasted. This is necessary when running stable"
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" diffusion 2.1."
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),
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)
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parser.add_argument(
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"--from_safetensors",
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action="store_true",
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help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
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)
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parser.add_argument(
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"--to_safetensors",
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action="store_true",
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help="Whether to store pipeline in safetensors format or not.",
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)
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parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
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parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
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args = parser.parse_args()
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pipe = load_pipeline_from_original_stable_diffusion_ckpt(
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checkpoint_path=args.checkpoint_path,
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original_config_file=args.original_config_file,
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image_size=args.image_size,
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prediction_type=args.prediction_type,
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model_type=args.pipeline_type,
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extract_ema=args.extract_ema,
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scheduler_type=args.scheduler_type,
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num_in_channels=args.num_in_channels,
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upcast_attention=args.upcast_attention,
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from_safetensors=args.from_safetensors,
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)
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pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
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