# Image Generation in Sesi

Sesi provides a native, language-level primitive for generating images using AI models. This primitive is designed to interoperate seamlessly with Sesi's file system builtins, allowing you to generate and persist images with minimal boilerplate.

## The `image` Primitive

To generate an image, use the `image` keyword followed by the model name, an optional configuration block, and a prompt block.

### Syntax

The syntax parallels standard `model` calls:

```
image("model-name") {"configKey": "configValue"} {"Prompt text"}
```

### Basic Example

Here is a simple example demonstrating how to generate a single image and save it to disk directly:

```sesi
// 1. Define the generation prompt natively
prompt request {"A simple minimalist company logo for a bakery"}

// 2. Call the image generation primitive
let imageData = image("gemini-3.1-flash-image-preview") {"ratio": "1:1", "size": "1K"} {request}

// 3. Write the payload to disk
try 
{let success = write_image("bakery_logo.png", imageData)
if success {print "Saved bakery_logo.png successfully."}} 
catch (e) {print "Failed to generate image:"
print e}
```
<img src="bakery_logo.png" alt="Bakery Logo" style="width:480px;">

### Advanced Example: Batch Asset Generation Workflow

Here is a practical script demonstrating how to iterate over a data set, generate assets, and save them to a specific directory. This is useful for building automated pipelines like generating UI placeholders or product catalog images.

```sesi
// Set up output directory
let outputDir = "assets/products/"
make_dir(outputDir)
let products = ["coffee_mug", "desk_lamp", "notebook"]

// Iterate through the list and generate a file for each
for product in products 
{print "Generating asset for:" product

// Construct the instruction for the model
prompt request {"A clean studio presentation photograph of a " product " on a solid white background."}
prompt filename { outputDir product ".png" }
try 
{let imageData = image("gemini-3.1-flash-image-preview") {"ratio": '1:1', "size": "1K"} {request}

// Attempt local file write
let success = write_image(filename, imageData)
if success {print "Saved:" filename}} 
catch (e) {print "Failed processing" product ":"
print e}}
print "Asset generation complete."
```
<img src="coffee_mug.png" alt="Coffee Mug" style="width:340px;">
<img src="desk_lamp.png" alt="Desk Lamp" style="width:340px;">
<img src="notebook.png" alt="Notebook" style="width:340px;">

## Configuration Options

When configuring the `image` call (specifically for models like `gemini-3.1-flash-image-preview`), the configuration block maps directly to backend SDK capabilities:

- `"ratio"`: The aspect ratio of the image (e.g., `"1:1"`, `"16:9"`, `"9:16"`).
- `"size"`: Dimensional sizing constraints (Must be `"512"`, `"1K"`, `"2K"`, or `"4K"`).
- `"temperature"`: *Will be deprecated in Gemini 3.x+, use thinkingLevel instead.* — controls variance.

## File I/O Integration: `write_image`

The `image()` call evaluates to a `string` (specifically, base64-encoded image data). To convert this into a standard image file on disk, you must use the `write_image(path, base64_content)` builtin. 

**Important:** Do *not* use `write_file` for image payloads—`write_image` is explicitly implemented in the Sesi engine (`src/builtins.ts`) to handle `Buffer.from(content, 'base64')` decoding for writing safe binary formats.

---

## Passing Images as Input (v1.2+)

Both `model()` and `image()` accept local image files as visual input via the `images` config key. The runtime reads each file from disk, base64-encodes it, and attaches it as an inline data part alongside the prompt. This enables vision tasks such as image description, comparison, OCR, style transfer, and reference-guided generation.

### Supported Formats

`.jpg` / `.jpeg`, `.png`, `.gif`, `.webp`, `.bmp`

### Syntax

```
model("model-name") {images: pathExpr} {prompt}
image("model-name") {images: pathExpr} {prompt}
```

`pathExpr` is any Sesi expression that evaluates to a `string` (single file) or an `array` of strings (multiple files).

### Single Image — Describe a File

```sesi
let caption = model("gemini-3-flash-preview") {images: "shots/frame_01.jpg"} {"Describe what is happening in this photograph in one paragraph."}
print caption
```

### Multiple Images — Side-by-Side Comparison

```sesi
let frames = ["take_a.jpg", "take_b.jpg"]
let notes = model("gemini-3-flash-preview") {images: frames} {"These are two alternate takes of the same scene. List the differences in composition and lighting."}
print notes
```

### Dynamic Path from a Variable

```sesi
let src = "scans/print_neg_04.png"
let reading = model("gemini-3-flash-preview") {images: src} {"Read and transcribe all visible text in this image."}
write_file("output/transcription.txt", reading)
print "Transcription saved."
```

### Reference-Guided Image Generation

Pass a reference image to an image-generation model to ground its output.

```sesi
let ref = "references/grille_detail.jpg"
let render = image("gemini-3.1-flash-image-preview") {images: ref, ratio: "16:9"} {"Render a full front elevation in the same visual style as the reference."}
write_image("output/elevation.png", render)
print "Render saved."
```

### Pipeline: Batch Description from a Directory

```sesi
let dir = "frames/"
let files = list_dir(dir)

for f in files {
  prompt p {dir f}
  let desc = model("gemini-3.1-flash-lite") {images: p} {"Describe this frame in one sentence."}
  print f desc
}
```

### Config Reference

When used inside `model()` or `image()` config blocks, `images` is resolved at runtime — it is **not** a static string key passed to the Gemini SDK. All other config keys (`thinkingLevel`, `max_tokens`, `ratio`, `size`, etc.) work alongside it normally.

| Key | Accepted Value | Notes |
|-----|---------------|-------|
| `images` | `string` or `array<string>` | One or more local file paths. Resolved relative to `process.cwd()`. |

> **Note:** Multimodal input requires a vision-capable model (e.g. `gemini-3-flash-preview`, `gemini-3.1-flash-lite`, `gemini-3.1-pro-preview`). Image-generation models that accept reference images are listed in their respective model documentation.