Add custom vae (diffusers type) to onnx converter (#2325)

scripts/convert_vae_diff_to_onnx.py

+# Copyright 2022 The HuggingFace Team. All rights reserved.
+#
+# 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 OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline, AutoencoderKL
+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, fp16: bool = False):
+    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"
+    output_path = Path(output_path)
+
+    # VAE DECODER
+    vae_decoder =  AutoencoderKL.from_pretrained(model_path + "/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_decoder.decode
+    onnx_export(
+        vae_decoder,
+        model_args=(
+            torch.randn(1, vae_latent_channels, 25, 25).to(device=device, dtype=dtype),
+            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,
+    )
+    del vae_decoder
+
+
+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=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()
+    print(args.output_path)
+    convert_models(args.model_path, args.output_path, args.opset, args.fp16)
+    print("SD: Done: ONNX")