Image and Video Generation

Configure image resources

Image models may be deployed on the same Azure OpenAI resource as your text models, or on a separate resource. Use the image-specific helpers only when the resource or key differs. This setup chunk is illustrative and is not run:

foundry_set_image_endpoint(Sys.getenv("AZURE_FOUNDRY_IMAGE_ENDPOINT"))
foundry_set_image_key("your-image-api-key")

Sys.setenv(AZURE_FOUNDRY_IMAGE_MODEL = "my-image-deployment")

Generate an image

foundry_image() returns one row per generated image with the prompt, any model-revised prompt, the output format, and the image bytes (as a URL or base64, depending on the model). Here we ask for a small, compressed JPEG so the recorded fixture stays light.

image <- foundry_image(
  "A friendly red panda reading a book, flat vector illustration",
  model = "gpt-image-2",
  size = "1024x1024",
  quality = "low",
  output_format = "jpeg",
  output_compression = 40
)

image[, c("prompt", "revised_prompt", "output_format", "created")]
#> # A tibble: 1 × 4
#>   prompt                        revised_prompt output_format created            
#>   <chr>                         <chr>          <chr>         <dttm>             
#> 1 A friendly red panda reading… <NA>           jpeg          2026-07-03 21:31:36

Decode the returned bytes to a file with foundry_save_image() and display the result:

img_path <- tempfile(fileext = ".jpeg")
foundry_save_image(image, img_path)
#> ✔ Image saved to '/tmp/RtmpTfwV4A/file1812b639901.jpeg' (from base64)
embed_image(
  img_path,
  alt = "AI-generated flat vector illustration of a friendly red panda reading a book"
)

Image URLs are temporary. Save images that belong in reports, stimuli, or audited records to a location you choose. This vignette uses temporary files and removes them after use. Inline image display requires the suggested base64enc package.

Edit an image

foundry_image_edit() takes an existing image and a prompt. The call below is illustrative (it needs an image file on disk) and is not run here:

edited_path <- tempfile(fileext = ".jpeg")
edited <- foundry_image_edit(
  image = img_path,
  prompt = "Use a blue, Microsoft-inspired color palette.",
  model = "gpt-image-2",
  output_format = "jpeg"
)

foundry_save_image(edited, edited_path)
unlink(edited_path)

Generate video (preview)

Video generation is a preview, long-running workflow: create a job, poll it, and download the content once a generation succeeds. Because the job is asynchronous these calls are shown for reference and are not run here:

job <- foundry_video_job_create(
  "A short animation of dots clustering into groups",
  model = "my-video-model",
  width = 1280,
  height = 720,
  n_seconds = 5
)

job <- foundry_video_job_get(job$job_id)

video_path <- tempfile(fileext = ".mp4")
foundry_video_download(
  generation_id = job$generation_id,
  path = video_path
)
unlink(video_path)

When to use these APIs

• Generate research stimuli or report illustrations with foundry_image().
• Iterate on existing figures or image stimuli with foundry_image_edit().
• Treat video as experimental: keep prompts, job IDs, and output files together so generated assets remain reproducible and auditable.
• Track costs separately from text calls. Image and video generation are usually more expensive than text and embedding calls.