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Tailored Generation
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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 (FIBO Models)
    • Required for fibo training versions
    • Use /tailored-gen/generate_visual_schema to create a structured visual schema using 5-10 sample images.
  • Generate Caption Prefix (Legacy Models)
    • Use /tailored-gen/generate_prefix to create a text-based prefix for legacy training versions.
  • 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).
  • Choose Training version: Select "Fibo" for best results.
  • 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 v2/image/generate/tailored (FIBO) or v1/text-to-image/tailored (Legacy).
  • Structured Prompting: Use v2/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 and version.
  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
https://engine.prod.bria-api.com/v1

Project

Manage your projects

Operations

Dataset

Manage training datasets

Operations

Create Dataset

Request

Create a new dataset.

Training Version: The training_version parameter determines the dataset's compatibility and structure:

  • Legacy (max, light, 3.2, 2.3): Uses text captions and caption_prefix. Compatible with each other.
  • FIBO (fibo): Uses JSON structured data (visual_schema). caption_prefix is null.

Defaults & constraints:

  • training_version defaults to max (will change to fibo in future updates).
  • training_version is immutable after creation.

Project Compatibility & Automatic Assignment: When creating a dataset, the system validates compatibility with the parent Project:

  1. Automatic Assignment: If the Project's training_version is null, it will automatically inherit the training_version of this new dataset. The Project will then be locked to this version family.

  2. Validation: If the Project already has a training_version set:

    • FIBO Projects: Can only contain fibo datasets.
    • Legacy Projects: Can contain any legacy dataset (max, light, 3.2, 2.3).
    • Mixing Forbidden: You cannot create a fibo dataset in a legacy project, or vice versa.

Completion Requirements:

  • Legacy: Minimum 1 image required to mark as completed.
  • FIBO: Minimum 5 images required to mark as complete

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)

training_versionstring

Specifies the foundation model version this dataset is intended for.

  • fibo: Uses JSON structured data (visual_schema). Requires generating a schema before uploading images.
  • max (default), light, 3.2, 2.3: Legacy versions using text captions and caption_prefix.
Default "max"
Enum"max""light""3.2""2.3""fibo"
upload_typestring

The method used to upload images to the dataset. 'basic' is the default

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": "advanced"
  }'

Responses

Dataset successfully created

Bodyapplication/json
idinteger

Unique identifier for the dataset

project_idinteger

Associated project ID

namestring

Dataset name

training_versionstring

The foundation model version this dataset targets (e.g., 'fibo', 'max').

Enum"max""light""3.2""2.3""fibo"
caption_prefixstring or null

Text prepended to captions.

  • For training_version = max/light/3.2/2.3: Required string.
  • For training_version = fibo: Null.
visual_schemastring or null

A string containing the JSON structure representing the visual backbone.

  • For training_version = fibo: Required string (must be generated via /generate_visual_schema).
  • For training_version = max/light/3.2/2.3: Null.
statusstring

Status of the dataset

Value"draft"
captions_update_statusstring

Status of captions update process

Enum"empty""in_progress""completed"
upload_typestring

Upload type

Enum"basic""advanced"
captions_sourcestring

The source of the captions. For 'basic' datasets, this is a null. For 'advanced' datasets, this indicates if captions were generated 'automatic' or provided 'manual'. this value is determined during bulk-upload

Enum"automatic""manual"
created_atstring(date-time)

Timestamp when the dataset was created

updated_atstring(date-time)

Timestamp when the dataset was last updated

Response
application/json
{
  "id": 456,
  "project_id": 123,
  "name": "dataset v1",
  "caption_prefix": "An illustration of a character named Lora, a female character with purple hair,",
  "status": "draft",
  "captions_update_status": "empty",
  "training_version": "3.2",
  "upload_type": "advanced",
  "visual_schema": null,
  "created_at": "2024-05-26T12:00:00Z",
  "updated_at": "2024-05-26T12:00:00Z",
  "captions_source": null
}

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

Unique identifier for the dataset

project_idinteger

Associated project ID

namestring

Dataset name

training_versionstring

The foundation model version this dataset targets (e.g., 'fibo', 'max').

Enum"max""light""3.2""2.3""fibo"
caption_prefixstring or null

Text prepended to captions.

  • For training_version = max/light/3.2/2.3: Required string.
  • For training_version = fibo: Null.
statusstring

Status of the dataset

Enum"draft""completed"
captions_update_statusstring

Status of captions update process, relevant for automatic captions generation.

Enum"empty""in_progress""completed"
upload_typestring

The upload type

Enum"basic""advanced"
captions_sourcestring

The source of the captions. For 'basic' datasets, this is a null. For 'advanced' datasets, this indicates if captions were generated 'automatic' or provided 'manual'.

Enum"automatic""manual"
images_countinteger
created_atstring(date-time)

Timestamp when the dataset was created

updated_atstring(date-time)

Timestamp when the dataset was last updated

]
Response
application/json
[
  {
    "id": 456,
    "project_id": 123,
    "name": "dataset v1",
    "caption_prefix": "An illustration of a character named Lora, a female character with purple hair,",
    "status": "completed",
    "captions_update_status": "empty",
    "upload_type": "advanced",
    "training_vesion": "3.2",
    "visual_schema": null,
    "captions_source": "manual",
    "images_count": 2800,
    "created_at": "2024-05-26T12:00:00Z",
    "updated_at": "2024-05-26T14:30:00Z"
  },
  {
    "id": 457,
    "project_id": 124,
    "name": "dataset v2",
    "caption_prefix": "An illustration of a character named Max, a male character with spiky black hair,",
    "status": "draft",
    "captions_update_status": "empty",
    "upload_type": "basic",
    "training_vesion": "3.2",
    "visual_schema": null,
    "captions_source": null,
    "images_count": 34,
    "created_at": "2024-05-27T09:00:00Z",
    "updated_at": "2024-05-27T09:00:00Z"
  }
]

Get Datasets by Project

Request

Retrieve all datasets for a specific project

Path
project_idstringrequired

Project ID

Query
include_modelsboolean

If true, a list of model objects using the dataset should be included in the response under the parameter 'models'

Default false
include_models_idsboolean

If true, a list of model ids using the dataset should be included in the response under the parameter 'model_ids'

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

Unique identifier for the dataset

project_idinteger

Associated project ID

namestring

Dataset name

training_versionstring

The foundation model version this dataset targets (e.g., 'fibo', 'max').

Enum"max""light""3.2""2.3""fibo"
caption_prefixstring or null

Text prepended to captions.

  • For training_version = max/light/3.2/2.3: Required string.
  • For training_version = fibo: Null.
upload_typestring

The upload type

Enum"basic""advanced"
captions_sourcestring

The source of the captions. For 'basic' datasets, this is a null. For 'advanced' datasets, this indicates if captions were generated 'automatic' or provided 'manual'.

Enum"automatic""manual"
images_countinteger

Number of images in the dataset

statusstring

Status of the dataset

Enum"draft""completed"
captions_update_statusstring

Status of captions update process

Enum"empty""in_progress""completed"
modelsArray of objects

List of model objects using this dataset. Only included when include_models=true

model_idsArray of strings

List of model IDs using this dataset. Only included when include_models_ids=true

created_atstring(date-time)

Timestamp when the dataset was created

updated_atstring(date-time)

Timestamp when the dataset was last updated

]
Response
application/json
[
  {
    "id": 456,
    "project_id": 123,
    "name": "dataset v1",
    "caption_prefix": "An illustration of a character named Lora, a female character with purple hair,",
    "upload_type": "advanced",
    "training_vesion": "3.2",
    "visual_schema": null,
    "captions_source": "automatic",
    "images_count": 1500,
    "status": "completed",
    "captions_update_status": "empty",
    "created_at": "2024-05-26T12:00:00Z",
    "updated_at": "2024-05-26T14:30:00Z"
  },
  {
    "id": 457,
    "project_id": 123,
    "name": "dataset v2",
    "caption_prefix": "An illustration of a character named Max, a male character with spiky black hair,",
    "upload_type": "basic",
    "training_vesion": "3.2",
    "visual_schema": null,
    "captions_source": null,
    "images_count": 8,
    "status": "draft",
    "captions_update_status": "empty",
    "created_at": "2024-05-27T09:00:00Z",
    "updated_at": "2024-05-27T09:00:00Z"
  }
]

Model

Manage and train models

Operations

Image Generation V2

Generation using FIBO models

Operations

Image Generation (Legacy)

Generation using Legacy models

Operations

Video Generation

Image-to-Video capabilities

Operations