Bria Fibo Lite API

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Quick Start

import fal_client

def on_queue_update(update):
    if isinstance(update, fal_client.InProgress):
        for log in update.logs:
           print(log["message"])

result = fal_client.subscribe(
    "bria/fibo-lite/generate",
    arguments={},
    with_logs=True,
    on_queue_update=on_queue_update,
)
print(result)

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("bria/fibo-lite/generate", {
  input: {},
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

curl --request POST \
  --url https://fal.run/bria/fibo-lite/generate \
  --header "Authorization: Key $FAL_KEY" \
  --header "Content-Type: application/json" \
  --data '{}'

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Examples

several coffee beans floating independently in mid air, the subject is red, in a red photo, red and black duotone image, red wash, red light, red lens, extreme close up, a dark black empty background, closeup, dramatic lighting with a strong red hue that gives the image a moody and intense atmospher…

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Input Schema

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Output Schema

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Input Example

{
  "prompt": "Oil painting of a fluffy, wide-eyed cat sitting upright, holding a small wooden sign reading \"Feed Me.\" Rich textures, dramatic brushstrokes, warm tones, and vintage charm.",
  "seed": 7,
  "aspect_ratio": "1:1",
  "negative_prompt": "",
  "steps_num": 8,
  "sync_mode": false
}

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Output Example

{
  "image": {
    "url": "",
    "content_type": "image/png",
    "file_name": "z9RV14K95DvU.png",
    "file_size": 4404019,
    "width": 1024,
    "height": 1024
  },
  "structured_prompt": {
    "short_description": "",
    "objects": [
      {
        "description": "",
        "location": "",
        "relationship": ""
      }
    ],
    "background_setting": "",
    "lighting": {
      "conditions": "",
      "direction": ""
    },
    "aesthetics": {
      "composition": "",
      "color_scheme": "",
      "mood_atmosphere": "",
      "aesthetic_score": "",
      "preference_score": ""
    },
    "text_render": [
      {
        "text": "",
        "location": "",
        "size": "",
        "color": "",
        "font": ""
      }
    ],
    "context": "",
    "artistic_style": ""
  }
}

1. CFG Distillation: First, we distill classifier-free guidance into the model, enabling inference with Guidance Scale = 1.0 (skipping the negative prompt pass).
2. SCFM (Shortcutting Flow Matching): On top of the CFG-distilled merged model, we apply velocity field self-distillation to enable efficient few-step sampling.

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🔑 Key Benefits

• Two-Stage Distillation: Combines CFG distillation with SCFM for maximum efficiency—the SCFM was trained on top of the already CFG-distilled merged model.
• Few-Step Generation: SCFM enables efficient sampling in significantly fewer inference steps.
• No CFG Overhead: Running at guidance_scale=1 means calculating the noise prediction only once per step instead of twice.
• Quality Tradeoff: As a distillation approach, there is a slight quality degradation compared to the full model at CFG=5, but the speed gains make it ideal for rapid iteration and production workflows.
• Drop-in Replacement: Works seamlessly with existing FIBO workflows—just set guidance_scale=1.0.
• Memory Efficient: Minimal additional GPU memory overhead.

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Limitations

• steps_num range: 4 to 30