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
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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.
Most importantly, BRIA 2.3 FAST-LORA is compatible with additional plugins, such as ControlNets. This enables the building of complex pipelines while still maintaining fast inference.
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)
pipe.load_lora_weights("briaai/BRIA-2.3-FAST-LORA")
pipe.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]
Using both LCM LORA and ControlNet
condition_image_path = "A_dog.png"
prompt = "A white dog"
seed = 222
w, h = 1024, 1024
controlnet = ControlNetModel.from_pretrained(
"briaai/BRIA-2.3-ControlNet-Canny",
torch_dtype=torch.float16
)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained("briaai/BRIA-2.3", controlnet=controlnet, torch_dtype=torch.float16)
pipe.load_lora_weights("briaai/BRIA-2.3-FAST-LORA")
pipe.fuse_lora()
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.force_zeros_for_empty_prompt = False
pipe.to("cuda")
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"
generator = torch.Generator("cuda").manual_seed(seed)
# Calculate Canny image
low_threshold, high_threshold = 100, 200
input_image = cv2.imread(condition_image_path)
input_image = cv2.Canny(input_image, low_threshold, high_threshold)
input_image = input_image[:, :, None]
input_image = np.concatenate([input_image, input_image, input_image], axis=2)
condition_image = Image.fromarray(input_image)
#Generate
image = pipe(prompt, image=condition_image, controlnet_conditioning_scale=1.0, num_inference_steps=8, width=w,height=h, guidance_scale=0.0, negative_prompt=negative_prompt, generator=generator,).images[0]