Update model id for stable diffusion in jumpstart (#3669)

* Update model id for stable diffusion in jumpstart

* changed model id to model-

* dummy change to retrigger CI test

Author: vivekmadan2

introduction_to_amazon_algorithms/jumpstart_text_to_image/Amazon_JumpStart_Text_To_Image.ipynb

@@ -16,7 +16,7 @@
     "---\n",
     "Welcome to Amazon [SageMaker JumpStart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html)! You can use JumpStart to solve many Machine Learning tasks through one-click in SageMaker Studio, or through [SageMaker JumpStart API](https://sagemaker.readthedocs.io/en/stable/overview.html#use-prebuilt-models-with-sagemaker-jumpstart). \n",
     "\n",
-    "In this demo notebook, we demonstrate how to use the JumpStart API for Text-to-Image. Text-to-Image is the task of generating realistic image given any text input. Here, we show how to use state-of-the-art pre-trained Stable Diffusion models for generating image from text.\n",
+    "In this demo notebook, we demonstrate how to use the JumpStart API for Text-to-Image. Text-to-Image is the task of generating realistic images given any text input. Here, we show how to use state-of-the-art pre-trained Stable Diffusion models for generating image from text.\n",
     "\n",
     "---"
    ]
@@ -27,11 +27,10 @@
    "metadata": {},
    "source": [
     "1. [Set Up](#1.-Set-Up)\n",
-    "2. [Select a model](#2.-Select-a-model)\n",
-    "3. [Retrieve JumpStart Artifacts & Deploy an Endpoint](#3.-Retrieve-JumpStart-Artifacts-&-Deploy-an-Endpoint)\n",
-    "4. [Query endpoint and parse response](#4.-Query-endpoint-and-parse-response)\n",
-    "5. [Advanced features](#5.-Advanced-features)\n",
-    "6. [Clean up the endpoint](#6.-Clean-up-the-endpoint)"
+    "3. [Retrieve JumpStart Artifacts & Deploy an Endpoint](#2.-Retrieve-JumpStart-Artifacts-&-Deploy-an-Endpoint)\n",
+    "4. [Query endpoint and parse response](#3.-Query-endpoint-and-parse-response)\n",
+    "5. [Advanced features](#4.-Advanced-features)\n",
+    "6. [Clean up the endpoint](#5.-Clean-up-the-endpoint)"
    ]
   },
   {
@@ -65,7 +64,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "25293522",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "!pip install sagemaker ipywidgets --upgrade --quiet"
@@ -88,7 +89,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "90518e45",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import sagemaker, boto3, json\n",
@@ -99,86 +102,12 @@
     "sess = sagemaker.Session()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "d2c1a623",
-   "metadata": {},
-   "source": [
-    "### 2. Select a model\n",
-    "\n",
-    "***\n",
-    "Here, we download jumpstart model_manifest file from the jumpstart s3 bucket, filter-out all the Text Generation models and select a model for inference. \n",
-    "***"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "deecb929",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from ipywidgets import Dropdown\n",
-    "\n",
-    "# download JumpStart model_manifest file.\n",
-    "boto3.client(\"s3\").download_file(\n",
-    "    f\"jumpstart-cache-prod-{aws_region}\", \"models_manifest.json\", \"models_manifest.json\"\n",
-    ")\n",
-    "with open(\"models_manifest.json\", \"rb\") as json_file:\n",
-    "    model_list = json.load(json_file)\n",
-    "\n",
-    "# filter-out all the Text Generation models from the manifest list.\n",
-    "txt2img_models = []\n",
-    "for model in model_list:\n",
-    "    model_id = model[\"model_id\"]\n",
-    "    if \"-txt2img-\" in model_id and model_id not in txt2img_models:\n",
-    "        txt2img_models.append(model_id)\n",
-    "\n",
-    "# display the model-ids in a dropdown to select a model for inference.\n",
-    "model_dropdown = Dropdown(\n",
-    "    options=txt2img_models,\n",
-    "    value=\"huggingface-txt2img-stable-diffusion-v1-4\",\n",
-    "    description=\"Select a model\",\n",
-    "    style={\"description_width\": \"initial\"},\n",
-    "    layout={\"width\": \"max-content\"},\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a821a4cf",
-   "metadata": {},
-   "source": [
-    "#### Chose a model for Inference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "01cc6c00",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "display(model_dropdown)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2ff82d42",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# model_version=\"*\" fetches the latest version of the model\n",
-    "model_id, model_version = model_dropdown.value, \"*\""
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "8f3ab601",
    "metadata": {},
    "source": [
-    "### 3. Retrieve JumpStart Artifacts & Deploy an Endpoint\n",
+    "### 2. Retrieve JumpStart Artifacts & Deploy an Endpoint\n",
     "\n",
     "***\n",
     "\n",
@@ -191,14 +120,18 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "a8a79ec9",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "from sagemaker import image_uris, model_uris, script_uris, hyperparameters\n",
     "from sagemaker.model import Model\n",
     "from sagemaker.predictor import Predictor\n",
     "from sagemaker.utils import name_from_base\n",
     "\n",
+    "# model_version=\"*\" fetches the latest version of the model\n",
+    "model_id, model_version = \"model-txt2img-stabilityai-stable-diffusion-v1-4\", \"*\"\n",
     "\n",
     "endpoint_name = name_from_base(f\"jumpstart-example-infer-{model_id}\")\n",
     "\n",
@@ -252,7 +185,7 @@
    "id": "b2e0fd36",
    "metadata": {},
    "source": [
-    "### 4. Query endpoint and parse response\n",
+    "### 3. Query endpoint and parse response\n",
     "\n",
     "---\n",
     "Input to the endpoint is any string of text dumped in json and encoded in `utf-8` format. Output of the endpoint is a `json` with generated text.\n",
@@ -264,7 +197,9 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "84fb30d0",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
@@ -320,7 +255,8 @@
    "metadata": {
     "pycharm": {
      "is_executing": true
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -339,7 +275,7 @@
     }
    },
    "source": [
-    "### 5. Advanced features\n",
+    "### 4. Advanced features\n",
     "\n",
     "***\n",
     "This model also supports many advanced parameters while performing inference. They include:\n",
@@ -362,7 +298,8 @@
    "metadata": {
     "pycharm": {
      "is_executing": true
-    }
+    },
+    "tags": []
    },
    "outputs": [],
    "source": [
@@ -412,7 +349,7 @@
    "id": "870d1173",
    "metadata": {},
    "source": [
-    "### 6. Clean up the endpoint"
+    "### 5. Clean up the endpoint"
    ]
   },
   {

introduction_to_amazon_algorithms/jumpstart_text_to_image/README.md

@@ -1,2 +1,2 @@
 ### SageMaker JumpStart Text to Image
-This notebook `Amazon_JumpStart_Text_To_Image.ipynb` demos how to use JumpStart to generate image conditioned on text that appears indistinguishable from the real images.
+This notebook `Amazon_JumpStart_Text_To_Image.ipynb` demos how to use JumpStart to generate image conditioned on text that appears indistinguishable from a real images.