Bria Dataset API

openapi: 3.2.0
info:
  title: Tailored Generation API Reference Dataset API
  version: ''
  description: '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. **Fully Automated 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 provides the ability to finetune training parameters and upload larger training datasets. - **Source-code & Weights** is for developers seeking deeper customization and access to Bria''s source-available GenAI models via Hugging Face. All methods allow 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:


    1. **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. 2. **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 a Visual Schema**

    - Use `/tailored-gen/generate_visual_schema` to create a structured visual schema using 5-10 sample images. - **Refine Structured Data**

    - Use `/tailored-gen/refine_structured_prompt` to iterate on your **Visual Schema** or **Image Captions** using natural language instructions. - *Example:* You can send your generated schema with the instruction "Character''s name is Lucy" to improve the training metadata programmatically. - **Upload and Manage Images**:

    - **Basic upload**: Use `/datasets/{dataset_id}/images` to upload up to 200 images individually. - **Bulk upload**: Use `/datasets/{dataset_id}/images/bulk` to upload zip files with >200 high-quality images (Advanced). - **Clone Datasets**: Create variations of existing datasets using the clone functionality. 3. **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 recipes) and Expert mode (for training parameter tweaking). - **Monitor and Control**: Manage the model lifecycle, including training start/stop,

    status monitoring, and version control over the training parameters. 4. **Generation Capabilities**:

    - **Image Generation**: Use `/image/generate/tailored` for text-to-image generation. - **Structured Prompting**: Use `/structured_prompt/generate/tailored` to create structured prompts via VLM before generation. - **Video Generation**: Use `/video/generate/tailored/image-to-video` to animate tailored images. ### **Training Process**


    To train a tailored model:


    1. **Create a Project**: Use the `/projects` endpoint to define your IP type and medium. 2.'
servers:
- url: https://engine.prod.bria-api.com/v2/
tags:


# --- truncated at 32 KB (50 KB total) ---
# Full source: https://artifacts.apis.io/bria/openapi/bria-dataset-api-openapi.yml