123 lines
3.9 KiB
Python
123 lines
3.9 KiB
Python
# Copyright 2022 The HuggingFace Team. All rights reserved.
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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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import argparse
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from pathlib import Path
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import torch
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from packaging import version
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from torch.onnx import export
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from diffusers import AutoencoderKL
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is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
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def onnx_export(
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model,
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model_args: tuple,
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output_path: Path,
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ordered_input_names,
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output_names,
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dynamic_axes,
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opset,
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use_external_data_format=False,
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):
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output_path.parent.mkdir(parents=True, exist_ok=True)
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# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
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# so we check the torch version for backwards compatibility
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if is_torch_less_than_1_11:
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export(
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model,
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model_args,
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f=output_path.as_posix(),
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input_names=ordered_input_names,
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output_names=output_names,
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dynamic_axes=dynamic_axes,
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do_constant_folding=True,
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use_external_data_format=use_external_data_format,
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enable_onnx_checker=True,
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opset_version=opset,
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)
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else:
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export(
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model,
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model_args,
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f=output_path.as_posix(),
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input_names=ordered_input_names,
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output_names=output_names,
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dynamic_axes=dynamic_axes,
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do_constant_folding=True,
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opset_version=opset,
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)
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@torch.no_grad()
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def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = False):
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dtype = torch.float16 if fp16 else torch.float32
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if fp16 and torch.cuda.is_available():
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device = "cuda"
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elif fp16 and not torch.cuda.is_available():
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raise ValueError("`float16` model export is only supported on GPUs with CUDA")
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else:
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device = "cpu"
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output_path = Path(output_path)
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# VAE DECODER
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vae_decoder = AutoencoderKL.from_pretrained(model_path + "/vae")
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vae_latent_channels = vae_decoder.config.latent_channels
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# forward only through the decoder part
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vae_decoder.forward = vae_decoder.decode
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onnx_export(
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vae_decoder,
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model_args=(
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torch.randn(1, vae_latent_channels, 25, 25).to(device=device, dtype=dtype),
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False,
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),
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output_path=output_path / "vae_decoder" / "model.onnx",
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ordered_input_names=["latent_sample", "return_dict"],
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output_names=["sample"],
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dynamic_axes={
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"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
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},
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opset=opset,
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)
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del vae_decoder
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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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"--model_path",
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type=str,
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required=True,
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help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
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)
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parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
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parser.add_argument(
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"--opset",
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default=14,
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type=int,
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help="The version of the ONNX operator set to use.",
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)
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parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
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args = parser.parse_args()
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print(args.output_path)
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convert_models(args.model_path, args.output_path, args.opset, args.fp16)
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print("SD: Done: ONNX")
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