Add a notebook showing how to save graph locally and load from it

examples/notebooks/text_to_image.ipynb

@@ -0,0 +1,135 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54915dac-fca2-410b-999b-710d50ded0f3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from onediff.infer_compiler import oneflow_compile\n",
+    "from onediff.schedulers import EulerDiscreteScheduler\n",
+    "from diffusers import StableDiffusionPipeline\n",
+    "import oneflow as flow\n",
+    "import torch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b8f6655b-a289-4da9-ba24-ea491fa2610d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_id = \"runwayml/stable-diffusion-v1-5\"\n",
+    "prompt = \"photo of a cat\"\n",
+    "height = 512\n",
+    "width = 512\n",
+    "steps = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0b75cd0-2e0f-4d63-b470-95f50c1d6e60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder=\"scheduler\")\n",
+    "pipe = StableDiffusionPipeline.from_pretrained(\n",
+    "    model_id,\n",
+    "    scheduler=scheduler,\n",
+    "    torch_dtype=torch.float16,\n",
+    "    safety_checker=None,\n",
+    ")\n",
+    "pipe = pipe.to(\"cuda\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "686526cc",
+   "metadata": {},
+   "source": [
+    "### Now if you don't want to compile the model each time, you can cache it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad264c64",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipe.unet = oneflow_compile(\n",
+    "    pipe.unet,\n",
+    "    options={\n",
+    "        \"graph_file\": \"/stable-diffusion-v1-5_graph\",\n",
+    "        \"graph_file_device\": \"cuda\",\n",
+    "    }\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a9e81e15",
+   "metadata": {},
+   "source": [
+    "So now when you run your first image generation, it will take a while to compile the model, but then it will be cached at `/stable-diffusion-v1-5_graph`.\n",
+    "Next time you run image generation, it will be much faster."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe1ec1cf",
+   "metadata": {},
+   "source": [
+    "### Or just run without caching:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f1b42a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pipe.unet = oneflow_compile(pipe.unet)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaec58c1-7262-48c6-866e-51fe8bc9c295",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images = pipe(\n",
+    "    prompt, height=height, width=width, num_inference_steps=steps\n",
+    ").images\n",
+    "image = images[0]\n",
+    "image"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}