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import argparse
import os
import shutil
from pathlib import Path
import onnx
import onnx_graphsurgeon as gs
import torch
from onnx import shape_inference
from packaging import version
from polygraphy.backend.onnx.loader import fold_constants
from torch.onnx import export
from diffusers import (
ControlNetModel,
StableDiffusionControlNetImg2ImgPipeline,
)
from diffusers.models.attention_processor import AttnProcessor
from diffusers.pipelines.controlnet.pipeline_controlnet_sd_xl import StableDiffusionXLControlNetPipeline
is_torch_less_than_1_11 = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
is_torch_2_0_1 = version.parse(version.parse(torch.__version__).base_version) == version.parse("2.0.1")
class Optimizer:
def __init__(self, onnx_graph, verbose=False):
self.graph = gs.import_onnx(onnx_graph)
self.verbose = verbose
def info(self, prefix):
if self.verbose:
print(
f"{prefix} .. {len(self.graph.nodes)} nodes, {len(self.graph.tensors().keys())} tensors, {len(self.graph.inputs)} inputs, {len(self.graph.outputs)} outputs"
)
def cleanup(self, return_onnx=False):
self.graph.cleanup().toposort()
if return_onnx:
return gs.export_onnx(self.graph)
def select_outputs(self, keep, names=None):
self.graph.outputs = [self.graph.outputs[o] for o in keep]
if names:
for i, name in enumerate(names):
self.graph.outputs[i].name = name
def fold_constants(self, return_onnx=False):
onnx_graph = fold_constants(gs.export_onnx(self.graph), allow_onnxruntime_shape_inference=True)
self.graph = gs.import_onnx(onnx_graph)
if return_onnx:
return onnx_graph
def infer_shapes(self, return_onnx=False):
onnx_graph = gs.export_onnx(self.graph)
if onnx_graph.ByteSize() > 2147483648:
raise TypeError("ERROR: model size exceeds supported 2GB limit")
else:
onnx_graph = shape_inference.infer_shapes(onnx_graph)
self.graph = gs.import_onnx(onnx_graph)
if return_onnx:
return onnx_graph
def optimize(onnx_graph, name, verbose):
opt = Optimizer(onnx_graph, verbose=verbose)
opt.info(name + ": original")
opt.cleanup()
opt.info(name + ": cleanup")
opt.fold_constants()
opt.info(name + ": fold constants")
# opt.infer_shapes()
# opt.info(name + ': shape inference')
onnx_opt_graph = opt.cleanup(return_onnx=True)
opt.info(name + ": finished")
return onnx_opt_graph
class UNet2DConditionControlNetModel(torch.nn.Module):
def __init__(
self,
unet,
controlnets: ControlNetModel,
):
super().__init__()
self.unet = unet
self.controlnets = controlnets
def forward(
self,
sample,
timestep,
encoder_hidden_states,
controlnet_conds,
controlnet_scales,
):
for i, (controlnet_cond, conditioning_scale, controlnet) in enumerate(
zip(controlnet_conds, controlnet_scales, self.controlnets)
):
down_samples, mid_sample = controlnet(
sample,
timestep,
encoder_hidden_states=encoder_hidden_states,
controlnet_cond=controlnet_cond,
conditioning_scale=conditioning_scale,
return_dict=False,
)
# merge samples
if i == 0:
down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
else:
down_block_res_samples = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
]
mid_block_res_sample += mid_sample
noise_pred = self.unet(
sample,
timestep,
encoder_hidden_states=encoder_hidden_states,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
return_dict=False,
)[0]
return noise_pred
class UNet2DConditionXLControlNetModel(torch.nn.Module):
def __init__(
self,
unet,
controlnets: ControlNetModel,
):
super().__init__()
self.unet = unet
self.controlnets = controlnets
def forward(
self,
sample,
timestep,
encoder_hidden_states,
controlnet_conds,
controlnet_scales,
text_embeds,
time_ids,
):
added_cond_kwargs = {"text_embeds": text_embeds, "time_ids": time_ids}
for i, (controlnet_cond, conditioning_scale, controlnet) in enumerate(
zip(controlnet_conds, controlnet_scales, self.controlnets)
):
down_samples, mid_sample = controlnet(
sample,
timestep,
encoder_hidden_states=encoder_hidden_states,
controlnet_cond=controlnet_cond,
conditioning_scale=conditioning_scale,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)
# merge samples
if i == 0:
down_block_res_samples, mid_block_res_sample = down_samples, mid_sample
else:
down_block_res_samples = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(down_block_res_samples, down_samples)
]
mid_block_res_sample += mid_sample
noise_pred = self.unet(
sample,
timestep,
encoder_hidden_states=encoder_hidden_states,
down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample,
added_cond_kwargs=added_cond_kwargs,
return_dict=False,
)[0]
return noise_pred
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
with torch.inference_mode(), torch.autocast("cuda"):
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, controlnet_path: list, output_path: str, opset: int, fp16: bool = False, sd_xl: bool = False
):
"""
Function to convert models in stable diffusion controlnet pipeline into ONNX format
Example:
python convert_stable_diffusion_controlnet_to_onnx.py
--model_path danbrown/RevAnimated-v1-2-2
--controlnet_path lllyasviel/control_v11f1e_sd15_tile ioclab/brightness-controlnet
--output_path path-to-models-stable_diffusion/RevAnimated-v1-2-2
--fp16
Example for SD XL:
python convert_stable_diffusion_controlnet_to_onnx.py
--model_path stabilityai/stable-diffusion-xl-base-1.0
--controlnet_path SargeZT/sdxl-controlnet-seg
--output_path path-to-models-stable_diffusion/stable-diffusion-xl-base-1.0
--fp16
--sd_xl
Returns:
create 4 onnx models in output path
text_encoder/model.onnx
unet/model.onnx + unet/weights.pb
vae_encoder/model.onnx
vae_decoder/model.onnx
run test script in diffusers/examples/community
python test_onnx_controlnet.py
--sd_model danbrown/RevAnimated-v1-2-2
--onnx_model_dir path-to-models-stable_diffusion/RevAnimated-v1-2-2
--qr_img_path path-to-qr-code-image
"""
dtype = torch.float16 if fp16 else torch.float32
if fp16 and torch.cuda.is_available():
device = "cuda"
elif fp16 and not torch.cuda.is_available():
raise ValueError("`float16` model export is only supported on GPUs with CUDA")
else:
device = "cpu"
# init controlnet
controlnets = []
for path in controlnet_path:
controlnet = ControlNetModel.from_pretrained(path, torch_dtype=dtype).to(device)
if is_torch_2_0_1:
controlnet.set_attn_processor(AttnProcessor())
controlnets.append(controlnet)
if sd_xl:
if len(controlnets) == 1:
controlnet = controlnets[0]
else:
raise ValueError("MultiControlNet is not yet supported.")
pipeline = StableDiffusionXLControlNetPipeline.from_pretrained(
model_path, controlnet=controlnet, torch_dtype=dtype, variant="fp16", use_safetensors=True
).to(device)
else:
pipeline = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
model_path, controlnet=controlnets, torch_dtype=dtype
).to(device)
output_path = Path(output_path)
if is_torch_2_0_1:
pipeline.unet.set_attn_processor(AttnProcessor())
pipeline.vae.set_attn_processor(AttnProcessor())
# # TEXT ENCODER
num_tokens = pipeline.text_encoder.config.max_position_embeddings
text_hidden_size = pipeline.text_encoder.config.hidden_size
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(device=device, dtype=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,
)
del pipeline.text_encoder
# # UNET
if sd_xl:
controlnets = torch.nn.ModuleList(controlnets)
unet_controlnet = UNet2DConditionXLControlNetModel(pipeline.unet, controlnets)
unet_in_channels = pipeline.unet.config.in_channels
unet_sample_size = pipeline.unet.config.sample_size
text_hidden_size = 2048
img_size = 8 * unet_sample_size
unet_path = output_path / "unet" / "model.onnx"
onnx_export(
unet_controlnet,
model_args=(
torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
torch.tensor([1.0]).to(device=device, dtype=dtype),
torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
torch.randn(len(controlnets), 2, 3, img_size, img_size).to(device=device, dtype=dtype),
torch.randn(len(controlnets), 1).to(device=device, dtype=dtype),
torch.randn(2, 1280).to(device=device, dtype=dtype),
torch.rand(2, 6).to(device=device, dtype=dtype),
),
output_path=unet_path,
ordered_input_names=[
"sample",
"timestep",
"encoder_hidden_states",
"controlnet_conds",
"conditioning_scales",
"text_embeds",
"time_ids",
],
output_names=["noise_pred"], # has to be different from "sample" for correct tracing
dynamic_axes={
"sample": {0: "2B", 2: "H", 3: "W"},
"encoder_hidden_states": {0: "2B"},
"controlnet_conds": {1: "2B", 3: "8H", 4: "8W"},
"text_embeds": {0: "2B"},
"time_ids": {0: "2B"},
},
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)
# optimize onnx
shape_inference.infer_shapes_path(unet_model_path, unet_model_path)
unet_opt_graph = optimize(onnx.load(unet_model_path), name="Unet", verbose=True)
# clean up existing tensor files
shutil.rmtree(unet_dir)
os.mkdir(unet_dir)
# collate external tensor files into one
onnx.save_model(
unet_opt_graph,
unet_model_path,
save_as_external_data=True,
all_tensors_to_one_file=True,
location="weights.pb",
convert_attribute=False,
)
del pipeline.unet
else:
controlnets = torch.nn.ModuleList(controlnets)
unet_controlnet = UNet2DConditionControlNetModel(pipeline.unet, controlnets)
unet_in_channels = pipeline.unet.config.in_channels
unet_sample_size = pipeline.unet.config.sample_size
img_size = 8 * unet_sample_size
unet_path = output_path / "unet" / "model.onnx"
onnx_export(
unet_controlnet,
model_args=(
torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
torch.tensor([1.0]).to(device=device, dtype=dtype),
torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
torch.randn(len(controlnets), 2, 3, img_size, img_size).to(device=device, dtype=dtype),
torch.randn(len(controlnets), 1).to(device=device, dtype=dtype),
),
output_path=unet_path,
ordered_input_names=[
"sample",
"timestep",
"encoder_hidden_states",
"controlnet_conds",
"conditioning_scales",
],
output_names=["noise_pred"], # has to be different from "sample" for correct tracing
dynamic_axes={
"sample": {0: "2B", 2: "H", 3: "W"},
"encoder_hidden_states": {0: "2B"},
"controlnet_conds": {1: "2B", 3: "8H", 4: "8W"},
},
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)
# optimize onnx
shape_inference.infer_shapes_path(unet_model_path, unet_model_path)
unet_opt_graph = optimize(onnx.load(unet_model_path), name="Unet", verbose=True)
# clean up existing tensor files
shutil.rmtree(unet_dir)
os.mkdir(unet_dir)
# collate external tensor files into one
onnx.save_model(
unet_opt_graph,
unet_model_path,
save_as_external_data=True,
all_tensors_to_one_file=True,
location="weights.pb",
convert_attribute=False,
)
del pipeline.unet
# VAE ENCODER
vae_encoder = pipeline.vae
vae_in_channels = vae_encoder.config.in_channels
vae_sample_size = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
vae_encoder.forward = lambda sample: vae_encoder.encode(sample).latent_dist.sample()
onnx_export(
vae_encoder,
model_args=(torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),),
output_path=output_path / "vae_encoder" / "model.onnx",
ordered_input_names=["sample"],
output_names=["latent_sample"],
dynamic_axes={
"sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
},
opset=opset,
)
# VAE DECODER
vae_decoder = pipeline.vae
vae_latent_channels = vae_decoder.config.latent_channels
# forward only through the decoder part
vae_decoder.forward = vae_encoder.decode
onnx_export(
vae_decoder,
model_args=(
torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
),
output_path=output_path / "vae_decoder" / "model.onnx",
ordered_input_names=["latent_sample"],
output_names=["sample"],
dynamic_axes={
"latent_sample": {0: "batch", 1: "channels", 2: "height", 3: "width"},
},
opset=opset,
)
del pipeline.vae
del pipeline
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--sd_xl", action="store_true", default=False, help="SD XL pipeline")
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(
"--controlnet_path",
nargs="+",
required=True,
help="Path to the `controlnet` 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=int,
help="The version of the ONNX operator set to use.",
)
parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")
args = parser.parse_args()
convert_models(args.model_path, args.controlnet_path, args.output_path, args.opset, args.fp16, args.sd_xl)
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