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# 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.

import argparse

import os

import shutil

from pathlib import Path

import torch

from torch.onnx import export

import onnx

from diffusers import StableDiffusionOnnxPipeline, StableDiffusionPipeline

from diffusers.onnx_utils import OnnxRuntimeModel

from packaging import version

is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")

def onnx_export(

model,

model_args: tuple,

output_path: Path,

ordered_input_names,

output_names,

dynamic_axes,

opset,

use_external_data_format=False,

output_path.parent.mkdir(parents=True, exist_ok=True)

# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,

# so we check the torch version for backwards compatibility

if is_torch_less_than_1_11:

export(

model,

model_args,

f=output_path.as_posix(),

input_names=ordered_input_names,

output_names=output_names,

dynamic_axes=dynamic_axes,

do_constant_folding=True,

use_external_data_format=use_external_data_format,

enable_onnx_checker=True,

opset_version=opset,

)

else:

export(

model,

model_args,

f=output_path.as_posix(),

input_names=ordered_input_names,

output_names=output_names,

dynamic_axes=dynamic_axes,

do_constant_folding=True,

opset_version=opset,

)

@torch.no_grad()

def convert_models(model_path: str, output_path: str, opset: int):

pipeline = StableDiffusionPipeline.from_pretrained(model_path, use_auth_token=True)

output_path = Path(output_path)

# TEXT ENCODER

text_input = pipeline.tokenizer(

"A sample prompt",

padding="max_length",

max_length=pipeline.tokenizer.model_max_length,

truncation=True,

return_tensors="pt",

)

onnx_export(

pipeline.text_encoder,

# casting to torch.int32 until the CLIP fix is released: https://github.com/huggingface/transformers/pull/18515/files

model_args=(text_input.input_ids.to(torch.int32)),

output_path=output_path / "text_encoder" / "model.onnx",

ordered_input_names=["input_ids"],

output_names=["last_hidden_state", "pooler_output"],

dynamic_axes={

"input_ids": {0: "batch", 1: "sequence"},

opset=opset,

)

# UNET

unet_path = output_path / "unet" / "model.onnx"

onnx_export(

pipeline.unet,

model_args=(torch.randn(2, 4, 64, 64), torch.LongTensor([0, 1]), torch.randn(2, 77, 768), False),

output_path=unet_path,

ordered_input_names=["sample", "timestep", "encoder_hidden_states", "return_dict"],

output_names=["out_sample"], # has to be different from "sample" for correct tracing

dynamic_axes={

"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},

"timestep": {0: "batch"},

"encoder_hidden_states": {0: "batch", 1: "sequence"},

opset=opset,

use_external_data_format=True, # UNet is > 2GB, so the weights need to be split

)

unet_model_path = str(unet_path.absolute().as_posix())

unet_dir = os.path.dirname(unet_model_path)

unet = onnx.load(unet_model_path)

# clean up existing tensor files

shutil.rmtree(unet_dir)

os.mkdir(unet_dir)

# collate external tensor files into one

onnx.save_model(

unet,

unet_model_path,

save_as_external_data=True,

all_tensors_to_one_file=True,

location="weights.pb",

convert_attribute=False,

)

# VAE ENCODER

vae_encoder = pipeline.vae

# need to get the raw tensor output (sample) from the encoder

vae_encoder.forward = lambda sample, return_dict: vae_encoder.encode(sample, return_dict)[0].sample()

onnx_export(

vae_encoder,

model_args=(torch.randn(1, 3, 512, 512), False),

output_path=output_path / "vae_encoder" / "model.onnx",

ordered_input_names=["sample", "return_dict"],

output_names=["latent_sample"],

dynamic_axes={

"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},

opset=opset,

)

# VAE DECODER

vae_decoder = pipeline.vae

# forward only through the decoder part

vae_decoder.forward = vae_encoder.decode

onnx_export(

vae_decoder,

model_args=(torch.randn(1, 4, 64, 64), False),

output_path=output_path / "vae_decoder" / "model.onnx",

ordered_input_names=["latent_sample", "return_dict"],

output_names=["sample"],

dynamic_axes={

"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},

opset=opset,

)

# SAFETY CHECKER

safety_checker = pipeline.safety_checker

safety_checker.forward = safety_checker.forward_onnx

onnx_export(

pipeline.safety_checker,

model_args=(torch.randn(1, 3, 224, 224), torch.randn(1, 512, 512, 3)),

output_path=output_path / "safety_checker" / "model.onnx",

ordered_input_names=["clip_input", "images"],

output_names=["out_images", "has_nsfw_concepts"],

dynamic_axes={

"clip_input": {0: "batch", 1: "channels", 2: "height", 3: "width"},

"images": {0: "batch", 1: "channels", 2: "height", 3: "width"},

opset=opset,

)

onnx_pipeline = StableDiffusionOnnxPipeline(

vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_decoder"),

text_encoder=OnnxRuntimeModel.from_pretrained(output_path / "text_encoder"),

tokenizer=pipeline.tokenizer,

unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),

scheduler=pipeline.scheduler,

safety_checker=OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"),

feature_extractor=pipeline.feature_extractor,

)

onnx_pipeline.save_pretrained(output_path)

print("ONNX pipeline saved to", output_path)

_ = StableDiffusionOnnxPipeline.from_pretrained(output_path, provider="CPUExecutionProvider")

print("ONNX pipeline is loadable")

if __name__ == "__main__":

parser = argparse.ArgumentParser()

parser.add_argument(

"--model_path",

type=str,

required=True,

help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",

)

parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")

parser.add_argument(

"--opset",

default=14,

type=str,

help="The version of the ONNX operator set to use.",

)

args = parser.parse_args()

convert_models(args.model_path, args.output_path, args.opset)

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