Tailored Generation provides capabilities to generate visuals (photos, illustrations, vectors) that preserve and faithfully reproduce specific IP elements or guidelines, ensuring consistency across all generated outputs.
The Tailored Generation APIs allow you to manage and train tailored models that maintain the integrity of your visual IP. You can train models through our Console or implement training directly via API. Explore the Console here.
Fully automted training mode Bria supports users in training high-quality finetuned models without the guesswork. Based on the selected IP type & dataset, bria automatically selects the right training parameters. This means that the user only needs to spend time curating their dataset.
Advanced Customization and Access:
Bria offers 2 types of advanced training customization: Expert training mode and source-code & weights.
- Expert training mode is for LoRa Finetune experts and provide the abilities to finetune the training parameters and upload larger training datasets.
- Source-code & Weights is for developers seeking deeper customization and can access Bria’s source-available GenAI models via Hugging Face.
All methods allows full control over fine-tuning, pipeline creation, and integration into proprietary workflows—empowering AI teams to develop and optimize their own generative AI solutions.
The Tailored Generation Training API provides a set of endpoints to manage the entire lifecycle of a tailored generation project:
- Project Management: Create and manage projects that define IP characteristics:
- Create and Retrieve Projects: Use the
/projects
endpoints to create a new project or retrieve existing projects that belong to your organization. - Define IP Type: Specify the IP type (e.g., multi_object_set, defined_character, stylized_scene) and medium.
- Manage Project Details: Use the
/projects/{id}
endpoints to update or delete specific projects.
- Dataset Management: Organize and refine datasets within your projects:
- Create and Retrieve Datasets: Use the
/datasets
endpoints to create new datasets or retrieve existing ones. - Generate an Advanced Caption Prefix (For
stylized_scene
, 'defined_character' and 'object_variants' IP types)- If the IP type is
stylized_scene
, 'defined_character' or 'object_variants', it is recommended to generate an advanced prefix before uploading images. - Use
/tailored-gen/generate_prefix
to generate a structured caption prefix using 1-6 sample images from the input images provided for training (preferably 6 if available). - Update the dataset with the generated prefix using
/datasets/{dataset_id}
before proceeding with image uploads.
- Upload and Manage Images: For Basic upload use the
/datasets/{dataset_id}/images
endpoints to upload up to 200 images and manage their captions. For advanced upload use the /datasets/{dataset_id}/images/bulk
endpoint to upload zip files with >200 high quality images. - Clone Datasets: Create variations of existing datasets using the clone functionality.
- Model Management: Train and optimize tailored models based on your datasets:
- Create and Retrieve Models: Use the
/models
endpoints to create new models or list existing ones. - Choose training mode: Select between Fully automated mode (automatic training based on Bria's reciepes) and Expert mode (for training parameters tweaking)
- Choose Training version & parameters: Select between "light"/bria-2.3 (for fast generation) or "max"/3.2 (for superior prompt alignment and enhanced learning capabilities).
- Monitor and Control: Manage the model lifecycle, including training start/stop and status monitoring and version control over the training parameters.