Skip to content
/Dataset

Overview

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 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.
  1. 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.
  1. 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.
  1. 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.
Register and get API Access

Training Process

To train a tailored model:

  1. Create a Project: Use the /projects endpoint to define your IP type and medium.
  2. Create a Dataset: Use the /datasets endpoint to create a dataset within your project.
  3. Define Visual Identity:
    • Step A (Generate): Call /tailored-gen/generate_visual_schema, sampling 5-10 images from your input set.
    • Step B (Refine - Optional): Call /tailored-gen/refine_structured_prompt with the generated schema and instructions to tweak the definitions (e.g., "Remove references to blue background").
    • Step C (Apply): Update the dataset with the final schema using /datasets/{dataset_id}.
  4. Upload Images: Upload images using the /datasets/{dataset_id}/images or /datasets/{dataset_id}/images/bulk endpoints (minimum resolution: 1024x1024px).
  5. Prepare Dataset: Review auto-generated captions (you can also use refine_structured_prompt to fix specific image captions) and update the dataset status to 'completed'.
  6. Create Model: Use the /models endpoint to create a model, which requires a training mode.
  7. Start Training: Initiate training via the /models/{id}/start_training endpoint. Training typically takes 4-6 hours.
  8. Monitor Progress: Check the training status using the /models/{id} endpoint until training is 'Completed'.
  9. Generate Images:
  • Use v2/image/generate/tailored for text-to-image generation.

Alternatively, manage and train tailored models through Bria's user-friendly Console.
Get started here.

Download OpenAPI description
tailored-generation.json
tailored-generation.yaml
Languages
Servers
https://engine.prod.bria-api.com/v2

Project

Manage your projects

Operations

Dataset

Manage training datasets

Operations

Create Dataset

Request

Create a new dataset.

Datasets use JSON structured data (visual_schema) for training. You must generate a visual schema via /generate_visual_schema before completing the dataset.

Completion Requirements: Minimum 5 images required to mark as completed.

Upload types:

  • Basic upload type: Supports up to 200 images, uploading image files
  • Advanced upload type: Supports up to 5000 images, uploading a zip file
Headers
api_tokenstringrequired
Bodyapplication/jsonrequired
project_idinteger

Associated project ID (required)

namestring

Dataset name (required)

upload_typestring

The method used to upload images to the dataset.

Default "basic"
Enum"basic""advanced"
curl -i -X POST \
  https://engine.prod.bria-api.com/v1/tailored-gen/datasets \
  -H 'Content-Type: application/json' \
  -H 'api_token: string' \
  -d '{
    "project_id": 123,
    "name": "dataset v1",
    "upload_type": "basic"
  }'

Responses

Dataset successfully created

Bodyapplication/json
idinteger
project_idinteger
namestring
visual_schemastring or null

A string containing the JSON structure representing the visual backbone. Must be generated via /generate_visual_schema.

statusstring
Value"draft"
captions_update_statusstring
Enum"empty""in_progress""completed"
upload_typestring
Enum"basic""advanced"
created_atstring(date-time)
updated_atstring(date-time)
Response
application/json
{
  "id": 456,
  "project_id": 123,
  "name": "dataset v1",
  "visual_schema": null,
  "status": "draft",
  "captions_update_status": "empty",
  "upload_type": "basic",
  "created_at": "2024-05-26T12:00:00Z",
  "updated_at": "2024-05-26T12:00:00Z"
}

Get Datasets

Request

Retrieve a list of all datasets. If there are no datasets, returns an empty array.

Headers
api_tokenstringrequired
curl -i -X GET \
  https://engine.prod.bria-api.com/v1/tailored-gen/datasets \
  -H 'api_token: string'

Responses

Successfully retrieved datasets

Bodyapplication/jsonArray [
idinteger
project_idinteger
namestring
visual_schemastring or null
statusstring
Enum"draft""completed"
captions_update_statusstring
Enum"empty""in_progress""completed"
upload_typestring
Enum"basic""advanced"
images_countinteger
created_atstring(date-time)
updated_atstring(date-time)
]
Response
application/json
[
  {
    "id": 0,
    "project_id": 0,
    "name": "string",
    "visual_schema": "string",
    "status": "draft",
    "captions_update_status": "empty",
    "upload_type": "basic",
    "images_count": 0,
    "created_at": "2019-08-24T14:15:22Z",
    "updated_at": "2019-08-24T14:15:22Z"
  }
]

Get Datasets by Project

Request

Retrieve all datasets for a specific project.

Path
project_idstringrequired
Query
include_modelsboolean

If true, include model objects using the dataset.

Default false
include_models_idsboolean

If true, include model IDs using the dataset.

Default false
Headers
api_tokenstringrequired
curl -i -X GET \
  'https://engine.prod.bria-api.com/v1/tailored-gen/projects/{project_id}/datasets?include_models=false&include_models_ids=false' \
  -H 'api_token: string'

Responses

Successfully retrieved datasets

Bodyapplication/jsonArray [
idinteger
project_idinteger
namestring
visual_schemastring or null
upload_typestring
Enum"basic""advanced"
images_countinteger
statusstring
Enum"draft""completed"
captions_update_statusstring
Enum"empty""in_progress""completed"
modelsArray of objects

Only included when include_models=true

model_idsArray of strings

Only included when include_models_ids=true

created_atstring(date-time)
updated_atstring(date-time)
]
Response
application/json
[
  {
    "id": 0,
    "project_id": 0,
    "name": "string",
    "visual_schema": "string",
    "upload_type": "basic",
    "images_count": 0,
    "status": "draft",
    "captions_update_status": "empty",
    "models": [
      {}
    ],
    "model_ids": [
      "string"
    ],
    "created_at": "2019-08-24T14:15:22Z",
    "updated_at": "2019-08-24T14:15:22Z"
  }
]

Model

Manage and train models

Operations

Image Generation

Generate images using tailored models

Operations

Video Generation

Image-to-Video capabilities

Operations