BRIA’s Technology
We're your holistic solution for all visual generation and modification requirements, whether it's access to foundation models, APIs, SDKs or Web integration. With a commitment to safe commercial use, we empower your organization and address your business challenges at scale while offering solutions with full liability coverage.
Quality
Exclusively trained from scratch on the largest licensed dataset built in collaboration with our data partners we guarantee the best quality outputs.
Legal & Safe
Trained on fully licensed data, we guarantee quality outputs with full liability, copyright protection, privacy and non-harmful content ensuring safe commercial use.
Use It Your Way Our technology is licensed and can be used for commercial use.
Licensed Source Models
Full suite of licensed models (source and weights) ready for use or fine-tuning, along with a comprehensive toolkit. Enhancing the research process by gaining flexibility, velocity and scale.
API’s Pipeline
Enhance your product with Generative AI capabilities by utilizing BRIA’s API and SDK for the quickest time to market. Allow your users to generate and customize real and synthetic visuals.
We built a machine that creates the largest licensed high quality visual collection in the world.
Our proprietary attribution engine: Our patented attribution engine rewards data contributors and creators based on their visual impact on every visual generation, thus nurturing a sustainable economy.
Algorithm Family Models
BRIA 2.X
High resolution images and quality outcomes
BRIA 1.4
Best in class seamless integration of text-to-image, inpainting and outpainting models
BRIA 1.2
Full compatible replacement for Stability v1.5
Brand Protector
Set of API's to monitor brand & style compliance
Generative Search
Search capabilities for any visual repository without the need for tagging or metadata
Predictable and Controllable API Pipelines
By delayering the visual to 5 elements, we allow customization of each layer in our APIs
Safe and Compliant Technology
Being trained on safe, fully-licensed images, the model is your fast track to generation business impact, responsibly. Using them will save you time, workload and garnet you with velocity and scale.
Bria’s licensed source model
An existing open-source model
Logos & Trademarks Safe
Not trained on trademarks or logos (e.g., logos of Apple, Coca-Cola or Nike), the model does not generate visuals that violate trademarks or logos.
Bria’s licensed source model
Apple on a wooden desk
An existing open-source model
Apple on a wooden desk
Copyrights Safe
Not trained on copyright-protected or fictional-characters visuals (e.g., Yoda, Superman, or Super Mario), the model does not generate images that violate copyrights.
Bria’s licensed source model
Chewbacca
An existing open-source model
Chewbacca
Misinformation and NSFW Protected
Our model mitigates harmful and fake visuals as well as misinformation protection. Additionally, our images are marked as AI-generated
Bria’s licensed source model
Prince singer
An existing open-source model
Prince singer
Seamless Swap-In for Higher Quality
Version Bria 1.2 was built to replace stability 1.5.
BRIA V1.2 Model
Existing text-to-image models may expose users to considerable legal risks or exposure to NSFW content.
BRIA’s safe solution is seamlessly integrated in a “plug and play” manner unlocking all available extensions and pipelines.
To achieve this, we have adopted the popular and widely-used architecture known as latent diffusion model. Bria 1.2 is a vanilla application of such architecture (equivalent to Stable Diffusion v1.5), and we offer an improved algorithm with Bria v1.4. We recommend plug-and-play Bria 1.2 for immediate full legal compliance and generation safety. For better results and full legal compliance and generation safety, we recommend adopting Bria 1.4.
We utilise a latent diffusion model architecture consisting of several components: a text encoder called CLIP ViT-L/14, a perceptual encoder-decoder known as VAE, and a UNet backbone. In this model, images are transformed into latent representations through the VAE, whereas textual captions are encoded using the text encoder. These encoded captions are then integrated into the UNet backbone using cross-attention. The optimization objective is similarity between the noise added to the latent representation and the UNet prediction
Coming soon: Version Bria 2.1 was built to replace stability XL.
Bria 1.2
Stability 1.5
Prompt: Mountain
Prompt: Burger
