Generate Image

# Capability Patterns

Mode-specific prompting tips. Load the relevant section during prompt crafting (workflow step 2).

---

## Photorealistic Scenes

Think like a photographer: describe lens, light, moment.

- Specify camera (85mm portrait, 24mm wide), aperture (f/1.8 bokeh, f/11 sharp throughout)
- Describe lighting direction and quality (golden hour from camera-left, three-point softbox)
- Include mood and format (serene, vertical portrait)

## Product Photography

- Isolation: Clean white backdrop, soft even lighting, e-commerce ready
- Lifestyle: Product in use context, natural setting, aspirational but authentic
- Hero shots: Cinematic framing, dramatic lighting, space for text overlay

## Logos & Text

- Put text in quotes: `'Morning Brew Coffee Co'`
- Describe typography: "clean bold sans-serif with generous letter-spacing"
- Specify color scheme, shape constraints, design intent
- Iterate with follow-up edits for refinement

## Stylized Illustration

- Name the style: "kawaii-style sticker", "anime-influenced", "vintage travel poster"
- Describe design language: "bold outlines, flat colors, cel-shading"
- Include format constraints: "white background", "die-cut sticker format"

## Text Rendering

Nano Banana has advanced text rendering capabilities. For best results:
- Put all text in single quotes within the prompt
- Describe font characteristics: weight, style, size relative to the image
- Specify text placement: "centered at the top," "bottom-right corner"
- For multiple text elements, describe each separately with position
- Use `--thinking high` for complex multi-line text or precise typography

## Google Search Grounding

Enable with `--grounding` flag when real-time data helps (weather visualizations, current events infographics, real-world data charts).

**Image search grounding** (Nano Banana only): Add `--image-grounding` alongside `--grounding` to enable image search results as additional visual context. Useful when the model needs to reference real-world visuals (product designs, architectural styles, specific locations).

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## Best Practices

### Hyper-Specificity

Vague prompts produce generic results. Every unspecified attribute becomes a random variable.

```
Vague:    "A woman in a park"
Specific: "A 30-year-old woman with shoulder-length auburn hair sits cross-legged
           on a green wool blanket in a sun-dappled oak grove, reading a hardcover
           book. Late afternoon golden hour, shallow depth of field at f/2.0."
```

Quantities, colors, materials, spatial positions, and named objects all reduce variance.

### Context & Intent

State what the image is for. Purpose shapes composition, mood, and framing decisions.

```
Generic:     "A flat white coffee on a marble counter"
With intent: "A hero image for an artisan coffee brand's homepage — a flat white
              in a handmade ceramic cup on a marble counter, steam rising, soft
              morning light from the left, negative space on the right for text overlay"
```

### Step-by-Step Instructions

Complex scenes benefit from sequential directives rather than a single compound sentence.

```
"Start with a wide establishing shot of a misty fjord at dawn.
 In the foreground, place a wooden dock extending from the lower left.
 A small red sailboat is moored at the dock's end.
 Mountains fill the background, their peaks just catching the first golden light.
 The water is perfectly still, creating mirror reflections."
```

### Positive Framing for Exclusions

Naming a concept under negation ("no X", "not X") biases the output toward X — diffusion models condition on tokens regardless of polarity. To exclude something, name a positive alternative that fills the same role, or scope the scene so the unwanted element is physically not there.

```
Bad:   "A professional headshot on a neutral gray backdrop.
        No distracting background elements, no visible logos or text,
        no harsh shadows on the face."

Good:  "A professional he