Merge pull request #895 from DyfanJones/main

add bedrock nova example

.github/workflows/packaging.yaml

@@ -57,7 +57,7 @@ jobs:
       pkg_version: ${{ steps.build.outputs.pkg_version }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Install R
         uses: r-lib/actions/setup-r@v2

.github/workflows/pr-commands.yaml

@@ -14,7 +14,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
@@ -52,7 +52,7 @@ jobs:
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
@@ -89,14 +89,14 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
       VERSION: ${{ github.event.comment.body }}
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - uses: r-lib/actions/pr-fetch@v2
         with:
           repo-token: ${{ secrets.GITHUB_TOKEN }}
 
       - uses: r-lib/actions/setup-r@v2
-      
+
       - name: Version
         id: version
         run: |
@@ -122,7 +122,7 @@ jobs:
               writeLines(lines, desc)
             }
           }
-          
+
           paws_update_version("cran")
         shell: Rscript {0}
 

.github/workflows/regen.yml

@@ -18,7 +18,7 @@ jobs:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Install R
         uses: r-lib/actions/setup-r@v2

.github/workflows/unit-tests.yml

@@ -29,7 +29,7 @@ jobs:
       CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
 
       - name: Install R
         uses: r-lib/actions/setup-r@v2
@@ -67,7 +67,7 @@ jobs:
         shell: Rscript {0}
 
       - name: Cache R packages
-        uses: actions/cache@v2
+        uses: actions/cache@v4
         with:
           path: ${{ env.R_LIBS_USER }}
           key: ${{ runner.os }}-${{ matrix.config.R }}-1-${{ hashFiles('.github/depends.Rds') }}

examples/aws_nova_background_generation_guide.md

@@ -0,0 +1,137 @@
+## Background replacement with Amazon Bedrock in R: A Step-by-Step Guide
+
+Developed from [05_background_replacement_using_mask_prompt](https://github.com/aws-samples/amazon-nova-samples/blob/main/multimodal-generation/image-generation/notebook/05_background_replacement_using_mask_prompt.ipynb)
+
+### 1. Load Necessary Libraries
+
+First, we need to load the required libraries for AWS services, JSON handling, base64 encoding/decoding, and image processing.
+
+```r
+library(paws)
+library(yyjsonr)
+library(base64enc)
+library(magick)
+```
+
+### 2. Define the Image Generation Function
+
+Next, we define a helper function `generate_images` that will handle the image generation process using Amazon Bedrock.
+
+```r
+# Function to generate images using Amazon Nova
+generate_images <- function(
+    inference_params,
+    output_directory,
+    model_id = "amazon.nova-canvas-v1:0",
+    client = NULL) {
+  # Create client if not provided
+  client <- client %||% bedrockruntime(region = "us-east-1")
+
+  # Invoke the model with the provided parameters
+  resp <- client$invoke_model(
+    body = write_json_str(inference_params, auto_unbox = TRUE),
+    modelId = model_id,
+    accept = "application/json",
+    contentType = "application/json"
+  )
+
+  # Create the output directory if it doesn't exist
+  dir.create(output_directory, recursive = TRUE, showWarnings = FALSE)
+
+  # Read the response body and decode the images
+  resp_body <- read_json_raw(resp$body)
+  img_raw <- lapply(resp_body$images, base64decode)
+  img <- lapply(img_raw, image_read)
+
+  # Save the request and response to JSON files
+  write_json_file(
+    inference_params,
+    file.path(output_directory, "request.json"),
+    auto_unbox = TRUE, pretty = TRUE
+  )
+  write_json_file(
+    resp_body,
+    file.path(output_directory, "response_body.json"),
+    auto_unbox = TRUE, pretty = TRUE
+  )
+
+  # Save the generated images to the output directory
+  for (i in seq_along(img)) {
+    image_write(
+      img[[i]], file.path(output_directory, sprintf("image_%s.png", i))
+    )
+  }
+}
+```
+
+### 3. Set Up Directories and Source Image
+
+We define the directories and specify the source image that we want to use for the image generation task.
+
+```r
+# Define directories and source image
+DIR <- "amazon_nova_example"
+SOURCE_IMAGE <- file.path(DIR, "images/amazon-coffee-maker-1.png")
+```
+
+### 4. Encode the Source Image
+
+The source image is encoded to base64 format, which is required for the inference parameters.
+
+```r
+# Encode the source image to base64
+SOURCE_IMAGE_BASE64 = base64encode(SOURCE_IMAGE)
+```
+
+### 5. Define Inference Parameters
+
+We set up the parameters for the image generation task, including the task type, description, mask prompt, and configuration.
+
+```r
+# Define inference parameters for the image generation task
+INFERENCE_PARAMS <- list(
+  taskType= "OUTPAINTING",
+  outPaintingParams= list(
+    image= SOURCE_IMAGE_BASE64,
+    text= "a coffee maker in a sparse stylish kitchen, a single plate of pastries next to the coffee maker, a single cup of coffee. highly detailed, highest quality, product imagery",  # Description of the background to generate
+    maskPrompt= "coffee maker",  # The element(s) to keep
+    outPaintingMode= "PRECISE"  # "DEFAULT" softens the mask. "PRECISE" keeps it sharp.
+  ),
+  imageGenerationConfig = list(
+    numberOfImages= 1,  # Number of variations to generate. 1 to 5.
+    quality= "standard",  # Allowed values are "standard" and "premium"
+    cfgScale= 7.0,  # How closely the prompt will be followed
+    seed= 123L # Set Seed
+  )
+)
+```
+
+### 6. Generate a Unique ID for the Output Directory
+
+We create a unique ID for the output directory to ensure that each run of the script saves its results in a separate folder.
+
+```r
+# Generate a unique ID for the output directory
+GENERATION_ID <- strftime(Sys.time(), format = "%Y-%m-%d_%H-%M-%S")
+OUTPUT_DIR <- file.path(DIR, "output", GENERATION_ID)
+```
+
+### 7. Call the Function to Generate Images
+
+Finally, we call the `generate_images` function with the defined parameters and output directory to generate the images.
+
+```r
+# Call the function to generate images
+generate_images(INFERENCE_PARAMS, OUTPUT_DIR)
+```
+
+<div style="display: flex; justify-content: space-around;">
+  <div style="text-align: center;">
+    <img src="amazon_nova_example/amazon-coffee-maker-1.png" alt="Input Image" style="max-width: 100%;">
+    <figcaption>Figure 1: Input Image</figcaption>
+  </div>
+  <div style="text-align: center;">
+    <img src="amazon_nova_example/image_1.png" alt="Output Image" style="max-width: 100%;">
+    <figcaption>Figure 2: Output Image</figcaption>
+  </div>
+</div>