Bria VGL Prompt Writing

Generate structured JSON prompts for Bria's FIBO models using Visual Generation Language (VGL).

Related Skill: Use bria-ai to execute these VGL prompts via the Bria API. VGL defines the structured prompt format; bria-ai handles generation, editing, and background removal.

Core Concept

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.

Operation Modes

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

JSON Schema

Output a single valid JSON object with these required keys:

1. short_description (String)

Concise summary of image content, max 200 words. Include key subjects, actions, setting, and mood.

2. objects (Array, max 5 items)

Each object requires:

{
  "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:

{
  "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:

{
  "number_of_objects": 3
}

Size guidance: If a person is the main subject, use "medium-to-large" or "large within frame".

3. background_setting (String)

Overall environment, setting, and background elements not in objects.

4. lighting (Object)

{
  "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"
}

5. aesthetics (Object)

{
  "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".

6. photographic_characteristics (Object)

{
  "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.

7. style_medium (String)

"photograph" | "oil painting" | "watercolor" | "3D render" | "digital illustration" | "pencil sketch"

Default to "photograph" unless explicitly requested otherwise.

8. artistic_style (String)

If not photograph, describe characteristics in max 3 words: "impressionistic, vibrant, textured"

For photographs, use "realistic" or similar.

9. context (String)

Describe the image type/purpose:

• "High-fashion editorial photograph for magazine spread"
• "Concept art for fantasy video game"
• "Commercial product photography for e-commerce"

10. text_render (Array)

Default: empty array []

Only populate if user explicitly provides exact text content:

{
  "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).

11. edit_instruction (String)

Single imperative command describing the edit/generation.

Edit Instruction Formats

For Standard Edits (no mask)

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.

For Masked Region Edits

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.

Fidelity Rules

Standard Edit Mode

Preserve ALL visual properties unless explicitly changed by instruction:

• Subject identity, pose, appearance
• Object existence, location, size, orientation
• Composition, camera angle, lens characteristics
• Style/medium

Only change what the edit strictly requires.

Masked Edit Mode

• Preserve all visible (non-masked) portions exactly
• Fill grey masked regions to blend seamlessly with unmasked areas
• Match existing style, lighting, and subject matter
• Never describe grey masks—describe content that fills them

Example Output

{
  "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."
}

Common Pitfalls

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

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Using VGL with Bria API

Generate Image with Structured Prompt

Pass VGL JSON to the structured_prompt parameter:

from bria_client import BriaClient

client = BriaClient()

vgl_prompt = {
    "short_description": "Professional businesswoman in modern office...",
    "objects": [...],
    # ... full VGL JSON
}

# Use structured_prompt for deterministic generation
result = client.refine(
    structured_prompt=json.dumps(vgl_prompt),
    instruction="Generate this scene",
    aspect_ratio="16:9"
)
print(result['result']['image_url'])

Refine Existing Generation

After generation, Bria returns a structured_prompt you can modify and regenerate:

# Initial generation
result = client.generate("A cozy coffee shop interior")
structured = result['result']['structured_prompt']

# Modify and regenerate
result = client.refine(
    structured_prompt=structured,
    instruction="Change the lighting to golden hour"
)

curl Example

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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References

• Schema Reference - Complete JSON schema with all parameter values
• bria-ai - API client and endpoint documentation for executing VGL prompts