---
license: other
license_name: bria-t2i
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_description: Model weights from BRIA AI can be obtained with the purchase of a commercial license. Fill in the form below and we reach out to you.
extra_gated_heading: "Fill in this form to request a commercial license for the model"
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 T5: Text-to-Image Model for Commercial Licensing

Bria AI 2.3 T5 is an alternative text-to-image model for specific commercial applications. It exclusively uses T5 for text embedding, enabling support for text prompts of unlimited length. This model matches 2.3's photorealism, with a 4% decrease in prompt alignment and 7% decrease in aesthetics. Like all Bria models, 2.3 T5 upholds ethical standards, excludes copyrighted and harmful content, and comes with full legal liability coverage. While 2.3 T5 is preferable when T5 embedding or support for longer prompts is specifically required, our recommended model for most use cases remains [Bria 2.3](https://huggingface.co/briaai/BRIA-2.3), especially given its compatibility with Bria's ControlNets and Adapters.

For more information, please visit our [website](https://bria.ai/).




# What's New

Bria AI 2.3 T5 supports text prompts of unlimited length. Additionally, unlike BRIA 2.3, it exclusively uses T5 for text embedding, eliminating biases associated with CLIP.


[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-2.3-T5-BETA)




### Get Access
Interested in BRIA 2.3 T5? Purchase is required to license and access BRIA 2.3 T5, 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](https://pages.bria.ai/the-visual-generative-ai-platform-for-builders-startups-plan?_gl=1*cqrl81*_ga*MTIxMDI2NzI5OC4xNjk5NTQ3MDAz*_ga_WRN60H46X4*MTcwOTM5OTMzNC4yNzguMC4xNzA5Mzk5MzM0LjYwLjAuMA..) to request access. This program are designed to support emerging businesses and academic pursuits with our cutting-edge technology.

Contact us today to unlock the potential of BRIA 2.3 T5! By submitting the form above, you agree to BRIA’s [Privacy policy](https://bria.ai/privacy-policy/) and [Terms & conditions](https://bria.ai/terms-and-conditions/).




![](2_4.png)


# 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 T5 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
- **License:** [Commercial licensing terms & conditions.](https://bria.ai/customer-general-terms-and-conditions)
- Purchase is required to license and access the model.

- **Model Description:** BRIA 2.3 T5 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](https://bria.ai/)






### Code example using Diffusers 


```
pip install diffusers, torch, huggingface_hub
```


```py
from diffusers import DiffusionPipeline
from huggingface_hub import snapshot_download
import torch
import os, sys

pipeline_path = snapshot_download(repo_id='briaai/BRIA-2.3 T5')    
sys.path.append(pipeline_path)
from ella_xl_pipeline import EllaXLPipeline
    
pipe = DiffusionPipeline.from_pretrained("briaai/BRIA-2.3", torch_dtype=torch.float16, use_safetensors=True)
pipe.load_lora_weights(f'{pipeline_path}/pytorch_lora_weights.safetensors')
pipe.fuse_lora()
pipe.unload_lora_weights()
pipe.to("cuda")
pipe = EllaXLPipeline(pipe,f'{pipeline_path}/pytorch_model.bin')

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: 


1. You must set `pipe.force_zeros_for_empty_prompt = False`
2. Using negative prompt is recommended.
3. 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)`
4. If you need speed - try the BRIA 2.3 Fast which achieve 75% reduced inference time 
5. For 2.3 T5, use 30-50 steps (higher is better)
6. For 2.3 T5, use `guidance_scale` of 5.0 or 7.5