FIBO
Collection
The FIBO model family (Generation & Edit)
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Updated
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Accelerate FIBO inference with a two-stage distillation approach.
This model combines CFG distillation with Shortcutting Flow Matching (SCFM) for ultra-efficient few-step generation.
🚀 Overview
This is a two-stage distilled model for the FIBO text-to-image model, combining:
- CFG Distillation: First, we distill classifier-free guidance into the model, enabling inference with Guidance Scale = 1.0 (skipping the negative prompt pass).
- SCFM (Shortcutting Flow Matching): On top of the CFG-distilled merged model, we apply velocity field self-distillation to enable efficient few-step sampling.
This two-stage approach allows FIBO Lite to generate high-quality images in significantly fewer inference steps while maintaining the benefits of CFG distillation.
🔑 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=1means 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.
📊 Comparison & Examples
Left: Regular FIBO (Base Model, CFG=5) | Right: FIBO Lite (Distilled LoRA, CFG=1)
🖼️ More Example Outputs from FIBO Distilled LoRA:
 |
 |
 |
 |
 |
 |
| Feature | Base FIBO | FIBO Lite |
|---|---|---|
| Guidance Scale | 5.0 (Typical) | 1.0 (Distilled) |
| Compute per Step | 2x (Cond + Uncond) | 1x (Cond Only) |
| Speed | Baseline | ~10x Faster |
| Quality | Full Quality | Slight Degradation |
🛠️ Usage
Requirements
pip install git+https://github.com/huggingface/diffusers torch torchvision boltons ujson sentencepiece accelerate transformers
for using gemini api, you need to install google-genai as well
Quick Start
import os
import json
import torch
from diffusers import BriaFiboPipeline
from diffusers.modular_pipelines import ModularPipeline
# -------------------------------
# Load the VLM pipeline
# -------------------------------
torch.set_grad_enabled(False)
# Using local VLM
vlm_pipe = ModularPipeline.from_pretrained("briaai/FIBO-VLM-prompt-to-JSON", trust_remote_code=True)
# Using Gemini API, requires GOOGLE_API_KEY environment variable
# assert os.getenv("GOOGLE_API_KEY") is not None, "GOOGLE_API_KEY environment variable is not set"
# vlm_pipe = ModularPipeline.from_pretrained("briaai/FIBO-gemini-prompt-to-JSON", trust_remote_code=True)
# Load the FIBO pipeline
pipe = BriaFiboPipeline.from_pretrained(
"briaai/Fibo-lite",
torch_dtype=torch.bfloat16,
)
pipe.to("cuda")
# Generate with guidance_scale=1.0 (2x faster!)
output = vlm_pipe(
prompt="A hyper-detailed, ultra-fluffy owl sitting in the trees at night, looking directly at the camera with wide, adorable, expressive eyes. Its feathers are soft and voluminous, catching the cool moonlight with subtle silver highlights. The owl's gaze is curious and full of charm, giving it a whimsical, storybook-like personality."
)
json_prompt_generate = output.values["json_prompt"]
results_generate = pipe(
prompt=json_prompt_generate, num_inference_steps=8, guidance_scale=1
)
results_generate.images[0].save("image_generate.png")
with open("image_generate_json_prompt.json", "w") as f:
f.write(json_prompt_generate)
Key Parameters
guidance_scale=1.0: This is the magic setting! With the LoRA loaded, you get the quality of CFG=5 at the speed of CFG=1.num_inference_steps=8: Standard for FIBO Lite. Adjust based on your quality/speed tradeoff.- No negative prompt needed: The distillation handles this internally.
🧠 Training Details
This model was created using a two-stage distillation process:
Stage 1: CFG Distillation
- Method: Guidance Distillation (Distilling the CFG effect into the model weights via LoRA)
- Base Model: briaai/FIBO
- Trainable Parameters: 471,472,128
- Precision: bfloat16
- Teacher Configuration: CFG=5.0 (standard FIBO setting)
- Student Configuration: CFG=1.0 (target deployment)
- Training Objective: Minimize KL divergence between teacher and student outputs
The CFG distillation LoRA was then merged into the base model to create a CFG-free foundation.
Stage 2: SCFM (Shortcutting Flow Matching)
Building on the merged CFG-distilled model, we applied Shortcutting Flow Matching (SCFM):
- Method: Velocity Field Self-Distillation
- Base Model: CFG-distilled merged FIBO (from Stage 1)
- Key Innovation: SCFM imparts a shortcut mechanism to standard flow matching models, enabling flexible trajectory-skipping without requiring step-size embedding
- Training Approach: Works on the velocity field rather than sample space, learning rapidly from self-guided distillation in an online manner
- Result: Efficient few-step flows without compromising quality
Why Two Stages?
- Stage 1 (CFG Distillation) eliminates the need for dual forward passes (conditional + unconditional), halving compute per step.
- Stage 2 (SCFM) enables the model to take larger steps through the diffusion trajectory, dramatically reducing the number of required inference steps.
Together, these stages compound to deliver ultra-efficient generation: fewer steps × less compute per step = significantly faster inference.
🤝 Community & Support
- GitHub: FIBO Repository
- Hugging Face: FIBO Model Card
- Commercial Licensing: Contact Bria AI
📚 Citation
If you use this model in your research or project, please cite:
@misc{gutflaish2025generating,
title = {Generating an Image From 1,000 Words: Enhancing Text-to-Image With Structured Captions},
author = {Gutflaish, Eyal and Kachlon, Eliran and Zisman, Hezi and Hacham, Tal and Sarid, Nimrod and Visheratin, Alexander and Huberman, Saar and Davidi, Gal and Bukchin, Guy and Goldberg, Kfir and Mokady, Ron},
year = {2025},
eprint = {2511.06876},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
doi = {10.48550/arXiv.2511.06876},
url = {https://arxiv.org/abs/2511.06876}
}
@misc{cai2025scfm,
title = {Shortcutting Pre-trained Flow Matching Diffusion Models is Almost Free Lunch},
author = {Cai, Xu and Wu, Yang and Chen, Qianli and Wu, Haoran and Xiang, Lichuan and Wen, Hongkai},
year = {2025},
archivePrefix = {arXiv},
primaryClass = {cs.CV}
}
📄 License
Source-Code & Weights
- The model is open source for non-commercial use with this license
- For commercial use Click here.
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