BRIA-2.3-FAST-LORA / README.md
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metadata
license: other
license_name: bria-2.3-fast-lora
license_link: https://bria.ai/customer-general-terms-and-conditions
library_name: diffusers
inference: false
tags:
  - text-to-image
  - legal liability
  - commercial use
extra_gated_prompt: >-
  Model weights from BRIA AI can be obtained after purchasing a commercial
  license. Fill in the form below and we reach out to you.
extra_gated_fields:
  Name: text
  Company/Org name: text
  Org Type (Early/Growth Startup, Enterprise, Academy): text
  Role: text
  Country: text
  Email: text
  By submitting this form, I agree to BRIA’s Privacy policy and Terms & conditions, see links below: checkbox

BRIA 2.3 FAST-LORA: Text-to-Image Model for Commercial Licensing

Introducing Bria AI 2.3 FAST-LORA, a groundbreaking text-to-image model explicitly designed for commercial applications in the enterprise. 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.

What's New

BRIA 2.3 FAST-LORA is a speedy version of BRIA 2.3, that provides an optimal balance between speed and accuracy. Engineered for efficiency, it takes only 1.64 seconds to generate images on a standard NVIDIA A10 GPU, achieving excellent image quality with an 80% reduction in inference time.

The model was distilled using the LCM technique and supports multiple aspect ratios, with the default resolution being 1024x1024. Similar to Bria AI 2.3, it presents improved realism and aesthetics.

Our evaluations show that our model achieves image quality comparable to its teacher, BRIA 2.3, and outperforms the SDXL LCM. While SDXL Turbo is faster, our model produces significantly better human faces as it supports higher resolution. These assessments were conducted by measuring human preferences.

CLICK HERE FOR A DEMO

Get Access

Interested in BRIA 2.3 FAST-LORA? Purchase is required to access BRIA 2.3 FAST-LORA, ensuring royalty management with our data partners and full liability coverage for commercial use.

Are you a startup or a student? We encourage you to apply for our Startup Program to request access. This program is designed to support emerging businesses and academic pursuits with our cutting-edge technology.

Contact us today to unlock the potential of BRIA 2.3 FAST-LORA! By submitting the form above, you agree to BRIA’s Privacy policy and Terms & conditions.

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 AI 2.3 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: Text-to-Image model
  • License: BRIA 2.3 FAST-LORA Licensing terms & conditions.
  • Purchase is required to license and access the model.
  • Model Description: BRIA 2.3 Fast is an efficient 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

Code example using Diffusers

pip install diffusers
from diffusers import DiffusionPipeline, LCMScheduler
import torch

pipe = DiffusionPipeline.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16)
pipeline.load_lora_weights("briaai/BRIA-2.3-FAST-LORA")
pipeline.fuse_lora()

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.to("cuda")

prompt = "A portrait of a Beautiful and playful ethereal singer, golden designs, highly detailed, blurry background"

image = pipe(prompt, num_inference_steps=8, guidance_scale=0.0).images[0]