examples: Stable Diffusion torch.compile

- Tutorial for using torch.compile with Stable Diffusion and
Torch-TensorRT
- Demonstration of output images without need to rerun the whole script
upon initial compilation of the docs

docsrc/index.rst

@@ -110,6 +110,7 @@ Tutorials
    tutorials/_rendered_examples/dynamo/torch_compile_resnet_example
    tutorials/_rendered_examples/dynamo/torch_compile_transformers_example
    tutorials/_rendered_examples/dynamo/torch_compile_advanced_usage
+   tutorials/_rendered_examples/dynamo/torch_compile_stable_diffusion
 
 Python API Documenation
 ------------------------
@@ -206,4 +207,4 @@ Legacy Further Information (TorchScript)
 * `GTC 2021 Fall Talk <https://www.nvidia.com/en-us/on-demand/session/gtcfall21-a31107/>`_
 * `PyTorch Ecosystem Day 2021 <https://assets.pytorch.org/pted2021/posters/I6.png>`_
 * `PyTorch Developer Conference 2021 <https://s3.amazonaws.com/assets.pytorch.org/ptdd2021/posters/D2.png>`_
-* `PyTorch Developer Conference 2022 <https://pytorch.s3.amazonaws.com/posters/ptc2022/C04.pdf>`_
+* `PyTorch Developer Conference 2022 <https://pytorch.s3.amazonaws.com/posters/ptc2022/C04.pdf>`_

examples/dynamo/README.rst

@@ -9,3 +9,4 @@ a number of ways you can leverage this backend to accelerate inference.
 * :ref:`torch_compile_resnet`: Compiling a ResNet model using the Torch Compile Frontend for ``torch_tensorrt.compile``
 * :ref:`torch_compile_transformer`: Compiling a Transformer model using ``torch.compile``
 * :ref:`torch_compile_advanced_usage`: Advanced usage including making a custom backend to use directly with the ``torch.compile`` API
+* :ref:`torch_compile_stable_diffusion`: Compiling a Stable Diffusion model using ``torch.compile``

examples/dynamo/torch_compile_stable_diffusion.py

@@ -0,0 +1,55 @@
+"""
+.. _torch_compile_stable_diffusion:
+
+Torch Compile Stable Diffusion
+======================================================
+
+This interactive script is intended as a sample of the Torch-TensorRT workflow with `torch.compile` on a Stable Diffusion model. A sample output is featured below:
+
+.. image:: /tutorials/images/majestic_castle.png
+   :width: 512px
+   :height: 512px
+   :scale: 50 %
+   :align: right
+"""
+
+# %%
+# Imports and Model Definition
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+import torch
+from diffusers import DiffusionPipeline
+
+import torch_tensorrt
+
+model_id = "CompVis/stable-diffusion-v1-4"
+device = "cuda:0"
+
+# Instantiate Stable Diffusion Pipeline with FP16 weights
+pipe = DiffusionPipeline.from_pretrained(
+    model_id, revision="fp16", torch_dtype=torch.float16
+)
+pipe = pipe.to(device)
+
+backend = "torch_tensorrt"
+
+# Optimize the UNet portion with Torch-TensorRT
+pipe.unet = torch.compile(
+    pipe.unet,
+    backend=backend,
+    options={
+        "truncate_long_and_double": True,
+        "precision": torch.float16,
+    },
+    dynamic=False,
+)
+
+# %%
+# Inference
+# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+prompt = "a majestic castle in the clouds"
+image = pipe(prompt).images[0]
+
+image.save("images/majestic_castle.png")
+image.show()