Write structured VGL (Visual Generation Language) JSON prompts for Bria's FIBO image generation models. Use this skill when creating detailed image descriptions in JSON format for text-to-image generation, image editing, inpainting, outpainting, background generation, or captioning. Triggers include requests to write structured prompts, create VGL JSON, describe images for AI generation, or work with Bria/FIBO's structured_prompt format. Also use when converting natural language image requests into the deterministic JSON schema required by FIBO models.
Generate structured JSON prompts for Bria's FIBO models using Visual Generation Language (VGL).
> **Related Skill**: Use **[bria-ai](../bria-ai/SKILL.md)** to execute these VGL prompts via the Bria API. VGL defines the structured prompt format; bria-ai handles generation, editing, and background removal.
VGL replaces ambiguous natural language prompts with deterministic JSON that explicitly declares every visual attribute: objects, lighting, camera settings, composition, and style. This ensures reproducible, controllable image generation.
| Mode | Input | Output | Use Case |
|------|-------|--------|----------|
| **Generate** | Text prompt | VGL JSON | Create new image from description |
| **Edit** | Image + instruction | VGL JSON | Modify reference image |
| **Edit_with_Mask** | Masked image + instruction | VGL JSON | Fill grey masked regions |
| **Caption** | Image only | VGL JSON | Describe existing image |
| **Refine** | Existing JSON + edit | Updated VGL JSON | Modify existing prompt |
Output a single valid JSON object with these required keys:
Concise summary of image content, max 200 words. Include key subjects, actions, setting, and mood.
Each object requires:
```json
{
"description": "Detailed description, max 100 words",
"location": "center | top-left | bottom-right foreground | etc.",
"relative_size": "small | medium | large within frame",
"shape_and_color": "Basic shape and dominant color",
"texture": "smooth | rough | metallic | furry | fabric | etc.",
"appearance_details": "Notable visual details",
"relationship": "Relationship to other objects",
"orientation": "upright | tilted 45 degrees | facing left | horizontal | etc."
}
```
**Human subjects** add:
```json
{
"pose": "Body position description",
"expression": "winking | joyful | serious | surprised | calm",
"clothing": "Attire description",
"action": "What the person is doing",
"gender": "Gender description",
"skin_tone_and_texture": "Skin appearance"
}
```
**Object clusters** add:
```json
{
"number_of_objects": 3
}
```
**Size guidance**: If a person is the main subject, use `"medium-to-large"` or `"large within frame"`.
Overall environment, setting, and background elements not in `objects`.
```json
{
"conditions": "bright daylight | dim indoor | studio lighting | golden hour | blue hour | overcast",
"direction": "front-lit | backlit | side-lit from left | top-down",
"shadows": "long, soft shadows | sharp, defined shadows | minimal shadows"
}
```
```json
{
"composition": "rule of thirds | symmetrical | centered | leading lines | medium shot | close-up",
"color_scheme": "monochromatic blue | warm complementary | high contrast | pastel",
"mood_atmosphere": "serene | energetic | mysterious | joyful | dramatic | peaceful"
}
```
For people as main subject, specify shot type in composition: `"medium shot"`, `"close-up"`, `"portrait composition"`.
```json
{
"depth_of_field": "shallow | deep | bokeh background",
"focus": "sharp focus on subject | soft focus | motion blur",
"camera_angle": "eye-level | low angle | high angle | dutch angle | bird's-eye",
"lens_focal_length": "wide-angle | 50mm standard | 85mm portrait | telephoto | macro"
}
```
**For people**: Prefer `"standard lens (35mm-50mm)"` or `"portrait lens (50mm-85mm)"`. Avoid wide-angle unless specified.
`"photograph"` | `"oil painting"` | `"watercolor"` | `"3D render"` | `"digital illustration"` | `"pencil sketch"`
Default to `"photograph"` unless explicitly requested otherwise.
If not photograph, describe characteristics in max 3 words: `"impressionistic, vibrant, textured"`
For photographs, use `"realistic"` or similar.
Describe the image type/purpose:
**Default: empty array `[]`**
Only populate if user explicitly provides exact text content:
```json
{
"text": "Exact text from user (never placeholder)",
"location": "center | top-left | bottom",
"size": "small | medium | large",
"color": "white | red | blue",
"font": "serif typeface | sans-serif | handwritten | bold impact",
"appearance_details": "Metallic finish | 3D effect | etc."
}
```
Exception: Universal text integral to objects (e.g., "STOP" on stop sign).
Single imperative command describing the edit/generation.
Start with action verb, describe changes, never reference "original image":
| Category | Rewritten Instruction |
|----------|----------------------|
| Style change | `Turn the image into the cartoon style.` |
| Object attribute | `Change the dog's color to black and white.` |
| Add element | `Add a wide-brimmed felt hat to the subject.` |
| Remove object | `Remove the book from the subject's hands.` |
| Replace object | `Change the rose to a bright yellow sunflower.` |
| Lighting | `Change the lighting from dark and moody to bright and vibrant.` |
| Composition | `Change the perspective to a wider shot.` |
| Text change | `Change the text "Happy Anniversary" to "Hello".` |
| Quality | `Refine the image to obtain increased clarity and sharpness.` |
Reference "masked regions" or "masked area" as target:
| Intent | Rewritten Instruction |
|--------|----------------------|
| Object generation | `Generate a white rose with a blue center in the masked region.` |
| Extension | `Extend the image into the masked region to create a scene featuring...` |
| Background fill | `Create the following background in the masked region: A vast ocean extending to horizon.` |
| Atmospheric fill | `Fill the background masked area with a clear, bright blue sky with wispy clouds.` |
| Subject restoration | `Restore the area in the mask with a young woman.` |
| Environment infill | `Create inside the masked area: a greenhouse with rows of plants under glass ceiling.` |
Preserve ALL visual properties unless explicitly changed by instruction:
Only change what the edit strictly requires.
```json
{
"short_description": "A professional businesswoman in a navy blazer stands confidently in a modern glass office, holding a tablet. Natural daylight streams through floor-to-ceiling windows, creating a warm, productive atmosphere.",
"objects": [
{
"description": "A confident businesswoman in her 30s with shoulder-length dark hair, wearing a tailored navy blazer over a white blouse. She holds a tablet in her left hand while gesturing naturally with her right.",
"location": "center-right",
"relative_size": "large within frame",
"shape_and_color": "Human figure, navy and white clothing",
"texture": "smooth fabric, professional attire",
"appearance_details": "Minimal jewelry, well-groomed professional appearance",
"relationship": "Main subject, interacting with tablet",
"orientation": "facing slightly left, three-quarter view",
"pose": "Standing upright, relaxed professional stance",
"expression": "confident, approachable smile",
"clothing": "Tailored navy blazer, white silk blouse, dark trousers",
"action": "Presenting or reviewing information on tablet",
"gender": "female",
"skin_tone_and_texture": "Medium warm skin tone, healthy smooth complexion"
},
{
"description": "A modern tablet device with a bright display showing charts and graphs",
"location": "center, held by subject",
"relative_size": "small",
"shape_and_color": "Rectangular, silver frame with illuminated screen",
"texture": "smooth glass and metal",
"appearance_details": "Thin profile, business application visible on screen",
"relationship": "Held by businesswoman, focus of her attention",
"orientation": "vertical, screen facing viewer at slight angle",
"pose": null,
"expression": null,
"clothing": null,
"action": null,
"gender": null,
"skin_tone_and_texture": null,
"number_of_objects": null
}
],
"background_setting": "Modern corporate office interior with floor-to-ceiling windows overlooking a city skyline. Minimalist furniture in neutral tones, potted plants adding touches of green.",
"lighting": {
"conditions": "bright natural daylight",
"direction": "side-lit from left through windows",
"shadows": "soft, natural shadows"
},
"aesthetics": {
"composition": "rule of thirds, medium shot",
"color_scheme": "professional blues and neutral whites with warm accents",
"mood_atmosphere": "confident, professional, welcoming"
},
"photographic_characteristics": {
"depth_of_field": "shallow, background slightly soft",
"focus": "sharp focus on subject's face and upper body",
"camera_angle": "eye-level",
"lens_focal_length": "portrait lens (85mm)"
},
"style_medium": "photograph",
"artistic_style": "realistic",
"context": "Corporate portrait photography for company website or LinkedIn professional profile.",
"text_render": [],
"edit_instruction": "Generate a professional businesswoman in a modern office environment holding a tablet."
}
```
1. **Don't invent text** - Keep `text_render` empty unless user provides exact text
2. **Don't over-describe** - Max 5 objects, prioritize most important
3. **Match the mode** - Use correct `edit_instruction` format for masked vs standard edits
4. **Preserve fidelity** - Only change what's explicitly requested
5. **Be specific** - Use concrete values ("85mm portrait lens") not vague terms ("nice camera")
6. **Null for irrelevant** - Human-specific fields should be `null` for non-human objects
---
Pass VGL JSON to the `structured_prompt` parameter:
```python
from bria_client import BriaClient
client = BriaClient()
vgl_prompt = {
"short_description": "Professional businesswoman in modern office...",
"objects": [...],
# ... full VGL JSON
}
result = client.refine(
structured_prompt=json.dumps(vgl_prompt),
instruction="Generate this scene",
aspect_ratio="16:9"
)
print(result['result']['image_url'])
```
After generation, Bria returns a `structured_prompt` you can modify and regenerate:
```python
result = client.generate("A cozy coffee shop interior")
structured = result['result']['structured_prompt']
result = client.refine(
structured_prompt=structured,
instruction="Change the lighting to golden hour"
)
```
```bash
curl -X POST "https://engine.prod.bria-api.com/v2/image/generate" \
-H "api_token: $BRIA_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"structured_prompt": "{\"short_description\": \"...\", ...}",
"prompt": "Generate this scene",
"aspect_ratio": "16:9"
}'
```
---
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