notebooks/community/model_garden/model_garden_pytorch_instructpix2pix.ipynb (357 lines of code) (raw):
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"# Copyright 2025 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "2bd716bf3e39"
},
"source": [
"# Vertex AI Model Garden - InstructPix2Pix\n",
"\n",
"<table><tbody><tr>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/workbench/instances\">\n",
" <img alt=\"Workbench logo\" src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" width=\"32px\"><br> Run in Workbench\n",
" </a>\n",
" </td>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_garden%2Fmodel_garden_pytorch_instructpix2pix.ipynb\">\n",
" <img alt=\"Google Cloud Colab Enterprise logo\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" width=\"32px\"><br> Run in Colab Enterprise\n",
" </a>\n",
" </td>\n",
" <td style=\"text-align: center\">\n",
" <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_instructpix2pix.ipynb\">\n",
" <img alt=\"GitHub logo\" src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" width=\"32px\"><br> View on GitHub\n",
" </a>\n",
" </td>\n",
"</tr></tbody></table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "d8cd12648da4"
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"source": [
"## Overview\n",
"\n",
"This notebook demonstrates deploying the [InstructPix2Pix](https://huggingface.co/timbrooks/instruct-pix2pix) model on Vertex AI for online prediction.\n",
"\n",
"### Objective\n",
"\n",
"- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
"- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
"- Run online predictions for text-guided image editing.\n",
"\n",
"### File a bug\n",
"\n",
"File a bug on [GitHub](https://github.com/GoogleCloudPlatform/vertex-ai-samples/issues/new) if you encounter any issue with the notebook.\n",
"\n",
"### Costs\n",
"\n",
"This tutorial uses billable components of Google Cloud:\n",
"\n",
"* Vertex AI\n",
"* Cloud Storage\n",
"\n",
"Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing), [Cloud Storage pricing](https://cloud.google.com/storage/pricing), and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "264c07757582"
},
"source": [
"## Run the notebook"
]
},
{
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"source": [
"# @title Setup Google Cloud project\n",
"\n",
"# @markdown 1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
"\n",
"# @markdown 2. **[Optional]** Set region. If not set, the region will be set automatically according to Colab Enterprise environment.\n",
"\n",
"REGION = \"\" # @param {type:\"string\"}\n",
"\n",
"# @markdown 3. If you want to run predictions with A100 80GB or H100 GPUs, we recommend using the regions listed below. **NOTE:** Make sure you have associated quota in selected regions. Click the links to see your current quota for each GPU type: [Nvidia A100 80GB](https://console.cloud.google.com/iam-admin/quotas?metric=aiplatform.googleapis.com%2Fcustom_model_serving_nvidia_a100_80gb_gpus), [Nvidia H100 80GB](https://console.cloud.google.com/iam-admin/quotas?metric=aiplatform.googleapis.com%2Fcustom_model_serving_nvidia_h100_gpus). You can request for quota following the instructions at [\"Request a higher quota\"](https://cloud.google.com/docs/quota/view-manage#requesting_higher_quota).\n",
"\n",
"# @markdown > | Machine Type | Accelerator Type | Recommended Regions |\n",
"# @markdown | ----------- | ----------- | ----------- |\n",
"# @markdown | a2-ultragpu-1g | 1 NVIDIA_A100_80GB | us-central1, us-east4, europe-west4, asia-southeast1, us-east4 |\n",
"# @markdown | a3-highgpu-2g | 2 NVIDIA_H100_80GB | us-west1, asia-southeast1, europe-west4 |\n",
"# @markdown | a3-highgpu-4g | 4 NVIDIA_H100_80GB | us-west1, asia-southeast1, europe-west4 |\n",
"# @markdown | a3-highgpu-8g | 8 NVIDIA_H100_80GB | us-central1, europe-west4, us-west1, asia-southeast1 |\n",
"\n",
"# Upgrade Vertex AI SDK.\n",
"! pip3 install --upgrade --quiet 'google-cloud-aiplatform>=1.84.0'\n",
"! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
"\n",
"# Import the necessary packages\n",
"import importlib\n",
"import os\n",
"\n",
"from google.cloud import aiplatform\n",
"\n",
"if os.environ.get(\"VERTEX_PRODUCT\") != \"COLAB_ENTERPRISE\":\n",
" ! pip install --upgrade tensorflow\n",
"! git clone https://github.com/GoogleCloudPlatform/vertex-ai-samples.git\n",
"\n",
"common_util = importlib.import_module(\n",
" \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
")\n",
"\n",
"LABEL = \"diffusers_gpu\"\n",
"models, endpoints = {}, {}\n",
"\n",
"\n",
"# Get the default cloud project id.\n",
"PROJECT_ID = os.environ[\"GOOGLE_CLOUD_PROJECT\"]\n",
"\n",
"# Get the default region for launching jobs.\n",
"if not REGION:\n",
" REGION = os.environ[\"GOOGLE_CLOUD_REGION\"]\n",
"\n",
"# Initialize Vertex AI API.\n",
"print(\"Initializing Vertex AI API.\")\n",
"aiplatform.init(project=PROJECT_ID, location=REGION)\n",
"\n",
"! gcloud config set project $PROJECT_ID\n",
"import vertexai\n",
"\n",
"vertexai.init(\n",
" project=PROJECT_ID,\n",
" location=REGION,\n",
")"
]
},
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"source": [
"# @title Set the model parameters\n",
"\n",
"# The pre-built serving docker image. It contains serving scripts and models.\n",
"SERVE_DOCKER_URI = \"us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-diffusers-serve-opt:20240605_1400_RC00\"\n",
"\n",
"VERSION_ID = \"instruct-pix2pix\"\n",
"PUBLISHER_MODEL_NAME = f\"publishers/timbrooks/models/instruct-pix2pix@{VERSION_ID}\"\n",
"\n",
"# The machine and accelerator specs for model deployment.\n",
"accelerator_type = \"NVIDIA_L4\"\n",
"machine_type = \"g2-standard-12\"\n",
"accelerator_count = 1\n",
"\n",
"# @markdown Set use_dedicated_endpoint to False if you don't want to use [dedicated endpoint](https://cloud.google.com/vertex-ai/docs/general/deployment#create-dedicated-endpoint). Note that [dedicated endpoint does not support VPC Service Controls](https://cloud.google.com/vertex-ai/docs/predictions/choose-endpoint-type), uncheck the box if you are using VPC-SC.\n",
"use_dedicated_endpoint = True # @param {type:\"boolean\"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"outputs": [],
"source": [
"# @title [Option 1] Deploy with Model Garden SDK\n",
"\n",
"# @markdown Deploy with Gen AI model-centric SDK. This section uploads the prebuilt model to Model Registry and deploys it to a Vertex AI Endpoint. It takes 15 minutes to 1 hour to finish depending on the size of the model. See [use open models with Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/open-models/use-open-models) for documentation on other use cases.\n",
"from vertexai.preview import model_garden\n",
"\n",
"model = model_garden.OpenModel(PUBLISHER_MODEL_NAME)\n",
"endpoints[LABEL] = model.deploy(\n",
" machine_type=machine_type,\n",
" accelerator_type=accelerator_type,\n",
" accelerator_count=accelerator_count,\n",
" use_dedicated_endpoint=use_dedicated_endpoint,\n",
" accept_eula=True, # Accept the End User License Agreement (EULA) on the model card before deploy. Otherwise, the deployment will be forbidden.\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"id": "J7WFlBNXwvg1"
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"outputs": [],
"source": [
"# @title [Option 2] Deploy with customized configs\n",
"\n",
"# @markdown This section deploys the InstructPix2Pix model for the text-guided image-to-image task.\n",
"\n",
"# @markdown The model deployment step will take ~15 minutes to complete.\n",
"\n",
"\n",
"def deploy_model(\n",
" model_id: str,\n",
" task: str,\n",
" machine_type: str,\n",
" accelerator_type: str,\n",
" accelerator_count: int,\n",
" use_dedicated_endpoint: bool = False,\n",
"):\n",
" model_name = \"instruct-pix2pix\"\n",
" endpoint = aiplatform.Endpoint.create(\n",
" display_name=f\"{model_name}-endpoint\",\n",
" dedicated_endpoint_enabled=use_dedicated_endpoint,\n",
" )\n",
" serving_env = {\n",
" \"MODEL_ID\": model_id,\n",
" \"TASK\": task,\n",
" \"DEPLOY_SOURCE\": \"notebook\",\n",
" }\n",
"\n",
" model = aiplatform.Model.upload(\n",
" display_name=model_name,\n",
" serving_container_image_uri=SERVE_DOCKER_URI,\n",
" serving_container_ports=[7080],\n",
" serving_container_predict_route=\"/predictions/diffusers_serving\",\n",
" serving_container_health_route=\"/ping\",\n",
" serving_container_environment_variables=serving_env,\n",
" model_garden_source_model_name=\"publishers/timbrooks/models/instruct-pix2pix\",\n",
" )\n",
" model.deploy(\n",
" endpoint=endpoint,\n",
" machine_type=machine_type,\n",
" accelerator_type=accelerator_type,\n",
" accelerator_count=accelerator_count,\n",
" deploy_request_timeout=1800,\n",
" system_labels={\n",
" \"NOTEBOOK_NAME\": \"model_garden_pytorch_instructpix2pix.ipynb\",\n",
" \"NOTEBOOK_ENVIRONMENT\": common_util.get_deploy_source(),\n",
" },\n",
" )\n",
" return model, endpoint\n",
"\n",
"\n",
"common_util.check_quota(\n",
" project_id=PROJECT_ID,\n",
" region=REGION,\n",
" accelerator_type=accelerator_type,\n",
" accelerator_count=accelerator_count,\n",
" is_for_training=False,\n",
")\n",
"\n",
"models[LABEL], endpoints[LABEL] = deploy_model(\n",
" model_id=\"timbrooks/instruct-pix2pix\",\n",
" task=\"instruct-pix2pix\",\n",
" machine_type=machine_type,\n",
" accelerator_type=accelerator_type,\n",
" accelerator_count=accelerator_count,\n",
" use_dedicated_endpoint=use_dedicated_endpoint,\n",
")"
]
},
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"source": [
"# @title Predict\n",
"\n",
"# @markdown Once deployment succeeds, you can send text prompt and image to the endpoint.\n",
"\n",
"# @markdown When deployed on one L4 GPU (default), the averaged inference time of a request is ~3 seconds.\n",
"\n",
"prompt = \"Add fire to the mountain\" # @param {type: \"string\"}\n",
"image = \"https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png\" # @param {type: \"string\"}\n",
"num_inference_steps = 25 # @param {type:\"number\"}\n",
"\n",
"init_image = common_util.download_image(image)\n",
"instances = [\n",
" {\n",
" \"prompt\": prompt,\n",
" \"image\": common_util.image_to_base64(init_image),\n",
" \"num_inference_steps\": num_inference_steps,\n",
" },\n",
"]\n",
"response = endpoints[LABEL].predict(\n",
" instances=instances, use_dedicated_endpoint=use_dedicated_endpoint\n",
")\n",
"images = [common_util.base64_to_image(image) for image in response.predictions]\n",
"common_util.image_grid([init_image, images[0]], rows=1, cols=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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"source": [
"# @title Clean up resources\n",
"\n",
"# @markdown Delete the experiment models and endpoints to recycle the resources\n",
"# @markdown and avoid unnecessary continuous charges that may incur.\n",
"\n",
"# Undeploy model and delete endpoint.\n",
"for endpoint in endpoints.values():\n",
" endpoint.delete(force=True)\n",
"\n",
"# Delete models.\n",
"for model in models.values():\n",
" model.delete()"
]
}
],
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"colab": {
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