BRIA-4B-Adapt: Fine-tune oriented Text-to-Image Model for Commercial Licensing

BRIA-4B-Adapt is our new groundbreaking 4 billion parameters text-to-image model, explicitly designed to provide exceptional fine-tuning capabilities for commercial use. The model excels in aligning to the tuned style while preserving an remarkably high prompt alignment. 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.

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Interested in BRIA-4B-Adapt? Purchase is required to license and access BRIA-4B-Adapt, ensuring royalty management with our data partners and full liability coverage for commercial use.

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Key Features

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-4B-Adapt delivers high-quality fine-tuning capabilities for generating 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.
Fully-Automated: Provides access to fully no-code automatic fine-tuning capabilities on Bria's platform: https://platform.bria.ai/console/tailored-generation.

Model Description

Developed by: BRIA AI
Model type: Latent Flow-Matching Text-to-Image Model
License: Commercial licensing terms & conditions.
Purchase is required to license and access the model.
Model Description: BRIA-4B-Adapt is a text-to-image model trained exclusively on a professional-grade, licensed dataset. It is designed for commercial use and includes full legal liability coverage.
Resources for more information: BRIA AI

Script Example using Diffusers: Image Generation using a Fine-Tuned LoRA

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-4B-Adapt", filename='pipeline_bria.py', local_dir=local_dir)
hf_hub_download(repo_id="briaai/BRIA-4B-Adapt", filename='transformer_bria.py', local_dir=local_dir)
hf_hub_download(repo_id="briaai/BRIA-4B-Adapt", filename='bria_utils.py', local_dir=local_dir)

import torch
from pipeline_bria import BriaPipeline

# 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-4B-Adapt", torch_dtype=torch.bfloat16,trust_remote_code=True)
pipe.load_lora_weights("briaai/BRIA-4B-Adapt", subfolder="example_finetuned_model", weight_name = "pytorch_lora_weights.safetensors")
pipe.to(device="cuda")

prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background"
negative_prompt = "Logo,Watermark,Text,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.
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

Script Example for Fine-Tuning a new LoRA

python train_lora.py \ 
    --pretrained_model_name_or_path briaai/BRIA-4B-Adapt \
    --dataset_name example_finetune_data/  \
    --output_dir example_output_lora/ \
    --max_train_steps 1500 \
    --rank 128 \
    --train_batch_size 1 \
    --gradient_accumulation_steps 4 \

briaai
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BRIA-4B-Adapt