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Tailored Generation
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Generate Visual Schema

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

Get Bulk Upload Status

Request

Retrieve the status and progress of a bulk image upload job.

Path
dataset_idintegerrequired

The ID of the dataset.

Headers
api_tokenstringrequired
curl -i -X GET \
  'https://engine.prod.bria-api.com/v1/tailored-gen/datasets/{dataset_id}/images/bulk-upload/status' \
  -H 'api_token: string'

Responses

Successfully retrieved job status.

Bodyapplication/json
statusstring

The current status of the upload job.

Enum"pending""processing""completed""failed"
progress_percentageinteger

The percentage of images processed.

total_processedinteger

The total number of images found in the upload source.

doneinteger

The number of images successfully uploaded.

failedinteger

The number of images that failed to upload.

failed_imagesArray of objects

A list of images that failed to upload, with details on the reason.

failed_images[].​filenamestring

The original filename of the failed image.

failed_images[].​error_codeinteger

The error code for the failure.

failed_images[].​error_messagestring

A descriptive message for the failure.

Response
application/json
{
  "status": "processing",
  "progress_percentage": 50,
  "total_processed": 100,
  "done": 45,
  "failed": 5,
  "failed_images": [
    {
      "filename": "photo1.bmp",
      "error_code": 415,
      "error_message": "Unsupported file format for image: photo1.bmp"
    },
    {
      "filename": "photo2.jpg",
      "error_code": 412,
      "error_message": "Image resolution must be at least 1024x1024: photo2.jpg"
    }
  ]
}

Generate Visual Schema

Request

Generates a structured JSON visual schema (backbone) based on the provided sample images.

This endpoint is required for datasets intended for the FIBO training version. The visual schema represents mutual characteristics (style, IP, colors, etc.) across training images and is used for:

  1. Caption generation during image upload.
  2. Prompt translation (user text → structured prompt) during generation.

Usage:

  • Provide 5-10 representative images of your style/IP.
  • The returned visual_schema string must be added to your dataset using the PUT /tailored-gen/datasets/{dataset_id} endpoint.

This API endpoint supports content moderation via an optional parameter.

Headers
api_tokenstringrequired

Authentication token.

Bodyapplication/jsonrequired
imagesArray of strings[ 5 .. 10 ] itemsrequired

An array of 5-10 images. Supports base64-encoded strings or public URLs. Supported formats: png, jpg, jpeg, webp.

content_moderationboolean

When enabled, applies content moderation to input visuals.

  • Processing stops at the first image that fails moderation.
  • Returns a 422 error with details about which image failed.
Default false
curl -i -X POST \
  https://engine.prod.bria-api.com/v1/tailored-gen/generate_visual_schema \
  -H 'Content-Type: application/json' \
  -H 'api_token: string' \
  -d '{
    "images": [
      "/9j/4AAQSkZJRgABAQAAAQABAAD...",
      "/9j/4AAQSkZJRgABAQAAAQABAAD...",
      "/9j/4AAQSkZJRgABAQAAAQABAAD...",
      "/9j/4AAQSkZJRgABAQAAAQABAAD...",
      "/9j/4AAQSkZJRgABAQAAAQABAAD..."
    ],
    "content_moderation": true
  }'

Responses

Successfully generated visual schema.

Bodyapplication/json
visual_schemastring

A string containing the structured JSON visual schema.

Response
application/json
{
  "visual_schema": "{\"style\": \"...\", \"colors\": \"...\"}"
}

Refine Structured Prompt

Request

Refines a Structured Prompt object (such as a Visual Schema or an Image Caption) based on user instructions.

Access Control & Validation:

  • Dataset Ownership: Requires a valid dataset_id to verify that the API token belongs to the organization owning the dataset.
  • Draft Status: The referenced dataset must be in draft mode. Refinement is disabled for completed datasets.

Use Cases:

  1. Refine Visual Schema: Input the initial schema generated by Bria and instructions like "Make the style description more detailed".
  2. Refine Image Caption: Input a specific image's caption and instructions like "Fix the description of the hair color".

The endpoint uses a VLM/LLM to process the input JSON and instructions, returning a valid, modified JSON structure that preserves the required format.

Headers
api_tokenstringrequired
Bodyapplication/jsonrequired
dataset_idintegerrequired

The ID of the dataset associated with this refinement. Used to validate ownership and draft status.

structured_promptstringrequired

The current structured prompt string (Visual Schema or Caption) to be refined.

instructionstringrequired

Natural language instruction describing how to modify the JSON (e.g., "Add a field for mood", "Correct the object color").

context_imagesArray of strings

Optional. List of image URLs or Base64 strings to provide visual context for the refinement (useful when refining specific image captions).

curl -i -X POST \
  https://engine.prod.bria-api.com/v1/tailored-gen/refine_structured_prompt \
  -H 'Content-Type: application/json' \
  -H 'api_token: string' \
  -d '{
    "dataset_id": 456,
    "structured_prompt": "{\"style\": \"cartoon\", \"colors\": [\"red\", \"blue\"]}",
    "instruction": "Change the style to '\''realistic 3D render'\'' and remove blue from colors."
  }'

Responses

Successfully refined JSON.

Bodyapplication/json
structured_promptstring

The refined Structured Prompt string.

Response
application/json
{
  "structured_prompt": "string"
}

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