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[ONNX] Improve ONNXPipeline scheduler compatibility, fix safety_checker (#1173)

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* [ONNX] Improve ONNX scheduler compatibility, fix safety_checker

* typo
This commit is contained in:
Anton Lozhkov
2022-11-08 14:39:11 +01:00
committed by GitHub
parent 555203e1fa
commit 11f7d6f3cc
10 changed files with 349 additions and 92 deletions
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73
scripts/convert_stable_diffusion_checkpoint_to_onnx.py
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@@ -81,6 +81,8 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
output_path = Path(output_path)
# 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",
@@ -103,13 +105,15 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
del pipeline.text_encoder
# UNET
unet_in_channels = pipeline.unet.config.in_channels
unet_sample_size = pipeline.unet.config.sample_size
unet_path = output_path / "unet" / "model.onnx"
onnx_export(
pipeline.unet,
model_args=(
torch.randn(2, pipeline.unet.in_channels, 64, 64).to(device=device, dtype=dtype),
torch.LongTensor([0, 1]).to(device=device),
torch.randn(2, 77, 768).to(device=device, dtype=dtype),
torch.randn(2, unet_in_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
torch.randn(2).to(device=device, dtype=dtype),
torch.randn(2, num_tokens, text_hidden_size).to(device=device, dtype=dtype),
False,
),
output_path=unet_path,
@@ -142,11 +146,16 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
# 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, return_dict: vae_encoder.encode(sample, return_dict)[0].sample()
onnx_export(
vae_encoder,
model_args=(torch.randn(1, 3, 512, 512).to(device=device, dtype=dtype), False),
model_args=(
torch.randn(1, vae_in_channels, vae_sample_size, vae_sample_size).to(device=device, dtype=dtype),
False,
),
output_path=output_path / "vae_encoder" / "model.onnx",
ordered_input_names=["sample", "return_dict"],
output_names=["latent_sample"],
@@ -158,11 +167,16 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
# VAE DECODER
vae_decoder = pipeline.vae
vae_latent_channels = vae_decoder.config.latent_channels
vae_out_channels = vae_decoder.config.out_channels
# forward only through the decoder part
vae_decoder.forward = vae_encoder.decode
onnx_export(
vae_decoder,
model_args=(torch.randn(1, 4, 64, 64).to(device=device, dtype=dtype), False),
model_args=(
torch.randn(1, vae_latent_channels, unet_sample_size, unet_sample_size).to(device=device, dtype=dtype),
False,
),
output_path=output_path / "vae_decoder" / "model.onnx",
ordered_input_names=["latent_sample", "return_dict"],
output_names=["sample"],
@@ -174,24 +188,35 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
del pipeline.vae
# 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).to(device=device, dtype=dtype),
torch.randn(1, 512, 512, 3).to(device=device, dtype=dtype),
),
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: "height", 2: "width", 3: "channels"},
},
opset=opset,
)
del pipeline.safety_checker
if pipeline.safety_checker is not None:
safety_checker = pipeline.safety_checker
clip_num_channels = safety_checker.config.vision_config.num_channels
clip_image_size = safety_checker.config.vision_config.image_size
safety_checker.forward = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker,
model_args=(
torch.randn(
1,
clip_num_channels,
clip_image_size,
clip_image_size,
).to(device=device, dtype=dtype),
torch.randn(1, vae_sample_size, vae_sample_size, vae_out_channels).to(device=device, dtype=dtype),
),
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: "height", 2: "width", 3: "channels"},
},
opset=opset,
)
del pipeline.safety_checker
safety_checker = OnnxRuntimeModel.from_pretrained(output_path / "safety_checker")
else:
safety_checker = None
onnx_pipeline = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / "vae_encoder"),
@@ -200,7 +225,7 @@ def convert_models(model_path: str, output_path: str, opset: int, fp16: bool = F
tokenizer=pipeline.tokenizer,
unet=OnnxRuntimeModel.from_pretrained(output_path / "unet"),
scheduler=pipeline.scheduler,
safety_checker=OnnxRuntimeModel.from_pretrained(output_path / "safety_checker"),
safety_checker=safety_checker,
feature_extractor=pipeline.feature_extractor,
)