TL;DR

Bria 3.2 is the next-generation commercial-ready text-to-image model. With just 4 billion parameters, it provides exceptional aesthetics and text rendering, evaluated to provide on par results to leading open-source models, and outperforming other licensed models.

In addition to being built entirely on licensed data, 3.2 provides several advantages for enterprise and commercial use:

Efficient Compute - the model is X3 smaller than the equivalent models in the market (4B parameters vs 12B parameters other open source models)
Architecture Consistency: Same architecture as 3.1—ideal for users looking to upgrade without disruption.
Fine-tuning Speedup: 2x faster fine-tuning on L40S and A100.

CLICK HERE FOR A DEMO

BRIA 3.2: Training data and Commercial Licensing

BRIA 3.2 is our latest text-to-image model explicitly designed for commercial applications. This model combines technological innovation with ethical responsibility and legal security, setting a new standard in the AI industry. Bria AI licenses the foundation model with full legal liability coverage. Our dataset does not contain copyrighted materials, such as fictional characters, logos, trademarks, public figures, harmful content, or privacy-infringing content.

For more information, please visit our website.

Join our Discord community for more information, tutorials, tools, and to connect with other users!

For Commercial License : click Here.

What's New vs pervious models:

Improved Aesthetics:
- 65% user preference for BRIA 3.2 over BRIA 3.1.
- 76% user preference for BRIA 3.2 over BRIA 2.3.
Superior Text Rendering: The model is optimized to generate short text consists of 1-6 words. OCR Score improvement from 5% (3.1) to 70% (3.2).
Consistent Prompt Alignment: Maintains high-quality textual description adherence.

Get Access

Bria 3.2 is available everywhere you build, either as source-code and weights, ComfyUI nodes or API endpoints.

API Endpoint: Bria.ai , Fal.ai, Replicate
ComfyUI: Use it in workflows
Interested in BRIA 3.2 source code and weights for commercial use? Purchase is required to license BRIA 3.2 got commercial use, ensuring royalty management with our data partners and full liability coverage.
Are you a startup or a student? We encourage you to apply for our Startup Program to request access. This program are designed to support emerging businesses and academic pursuits with our cutting-edge technology.
By submitting the form above, you agree to BRIA’s Privacy policy and Terms & conditions.

Key Features

Architecture: 4B parameter, rectified flow transformer based model with T5 text encoder.
Legally Compliant: Offers full legal liability coverage for copyright and privacy infringements. Thanks to training on 100% licensed data from leading data partners, we ensure the ethical use of content.
Patented Attribution Engine: Our attribution engine is our way to compensate our data partners, powered by our proprietary and patented algorithms.
Enterprise-Ready: Specifically designed for business applications, Bria AI 3.0 delivers high-quality, compliant imagery for a variety of commercial needs.
Customizable Technology: Provides access to source code and weights for extensive customization, catering to specific business requirements.

Model Description

Developed by: BRIA AI
Model type: Latent diffusion text-to-image model
Resources for more information: BRIA AI

Code example using Diffusers

pip install diffusers, hf_hub_download

from huggingface_hub import hf_hub_download
import os

try:
    local_dir = os.path.dirname(__file__)
except:
    local_dir = '.'
    
hf_hub_download(repo_id="briaai/BRIA-3.2", filename='pipeline_bria.py', local_dir=local_dir)
hf_hub_download(repo_id="briaai/BRIA-3.2", filename='transformer_bria.py', local_dir=local_dir)
hf_hub_download(repo_id="briaai/BRIA-3.2", filename='bria_utils.py', local_dir=local_dir)

import torch
from pipeline_bria import BriaPipeline, BriaTransformer2DModel

# trust_remote_code = True - allows loading a transformer which is not present at the transformers library(from transformer/bria_transformer.py)
pipe = BriaPipeline.from_pretrained("briaai/BRIA-3.2", torch_dtype=torch.bfloat16,trust_remote_code=True)
pipe.to(device="cuda")

prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background"
negative_prompt = "Logo,Watermark,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"

images = pipe(prompt=prompt, negative_prompt=negative_prompt, height=1024, width=1024).images[0]

Some tips for using our text-to-image model at inference:

Using negative prompt is recommended.
For Fine-tuning, use zeros instead of null text embedding.
We support multiple aspect ratios, yet resolution should overall consists approximately 1024*1024=1M pixels, for example: ((1024,1024), (1280, 768), (1344, 768), (832, 1216), (1152, 832), (1216, 832), (960,1088)
Use 30-50 steps (higher is better)
Use guidance_scale of 5.0

briaai
/

BRIA-3.2

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