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Tailored Generation
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Create Model

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

Model

Manage and train models

Operations

Create Model

Request

Create a new model.

Headers
api_tokenstringrequired
Bodyapplication/jsonrequired
namestring

Name of the model (required)

dataset_idinteger

ID of the dataset to use (required)

training_modestring

Defines the training configuration strategy.

  • fully_automated: Automatically configures the training recipe based on dataset size and IP medium/type.
  • expert: For experienced users needing control over training parameters.
Default "fully_automated"
Enum"fully_automated""expert"
descriptionstring

Description of the model (optional)

curl -i -X POST \
  https://engine.prod.bria-api.com/v1/tailored-gen/models \
  -H 'Content-Type: application/json' \
  -H 'api_token: string' \
  -d '{
    "name": "Lora Character Model",
    "dataset_id": 456,
    "training_mode": "fully_automated",
    "description": "A model trained on Lora character illustrations"
  }'

Responses

Model successfully created

Bodyapplication/json
idinteger
namestring
descriptionstring
statusstring
Value"Created"
training_modestring
Enum"fully_automated""expert"
project_idinteger
dataset_idinteger
created_atstring(date-time)
updated_atstring(date-time)
active_model_versionstring
training_parametersobject
checkpoint_stepsArray of strings
Response
application/json
{
  "id": 12233,
  "name": "Test Model",
  "description": "Test description",
  "status": "Created",
  "dataset_id": 13748,
  "training_mode": "fully_automated",
  "project_id": 12429,
  "created_at": "2025-08-20T06:06:05.849402",
  "updated_at": "2025-08-20T06:06:05.849404",
  "active_model_version": "default",
  "training_parameters": {},
  "checkpoint_steps": []
}

Get Models

Request

Retrieve a list of models. If there are no models, an empty array is returned.

Query
include_training_parametersboolean

If true, includes detailed training parameters and checkpoint steps.

Default false
Headers
api_tokenstringrequired
curl -i -X GET \
  'https://engine.prod.bria-api.com/v1/tailored-gen/models?include_training_parameters=false' \
  -H 'api_token: string'

Responses

Successfully retrieved models

Bodyapplication/jsonArray [
idinteger
namestring
descriptionstring
statusstring
Enum"Created""InProgress""Completed""Failed""Stopping""Stopped"
training_modestring
Enum"fully_automated""expert"
active_model_versionstring
training_parametersobject
training_parameters.​learning_ratenumber(float)
training_parameters.​lr_schedulerstring
training_parameters.​rankinteger
training_parameters.​total_training_stepsinteger
training_parameters.​checkpoint_intervalinteger
training_parameters.​lr_warmup_stepsinteger or null
checkpoint_stepsArray of strings
project_idinteger
dataset_idinteger
created_atstring(date-time)
]
Response
application/json
[
  {
    "id": 0,
    "name": "string",
    "description": "string",
    "status": "Created",
    "training_mode": "fully_automated",
    "active_model_version": "string",
    "training_parameters": {
      "learning_rate": 0.1,
      "lr_scheduler": "string",
      "rank": 0,
      "total_training_steps": 0,
      "checkpoint_interval": 0,
      "lr_warmup_steps": 0
    },
    "checkpoint_steps": [
      "string"
    ],
    "project_id": 0,
    "dataset_id": 0,
    "created_at": "2019-08-24T14:15:22Z"
  }
]

Get Models by Project

Request

Retrieve all models for a project.

Path
project_idintegerrequired
Query
include_training_parametersboolean
Default false
Headers
api_tokenstringrequired
curl -i -X GET \
  'https://engine.prod.bria-api.com/v1/tailored-gen/projects/{project_id}/models?include_training_parameters=false' \
  -H 'api_token: string'

Responses

Successfully retrieved models

Bodyapplication/jsonArray [
idinteger
namestring
descriptionstring
statusstring
Enum"Created""InProgress""Completed""Failed""Stopping""Stopped"
training_modestring
Enum"fully_automated""expert"
project_idinteger
dataset_idinteger
active_model_versionstring
training_parametersobject
checkpoint_stepsArray of strings
created_atstring(date-time)
updated_atstring(date-time)
]
Response
application/json
[
  {
    "id": 0,
    "name": "string",
    "description": "string",
    "status": "Created",
    "training_mode": "fully_automated",
    "project_id": 0,
    "dataset_id": 0,
    "active_model_version": "string",
    "training_parameters": {},
    "checkpoint_steps": [
      "string"
    ],
    "created_at": "2019-08-24T14:15:22Z",
    "updated_at": "2019-08-24T14:15:22Z"
  }
]

Image Generation

Generate images using tailored models

Operations

Video Generation

Image-to-Video capabilities

Operations