Description

The /text-to-image/base Route empowers users to create stunning images directly from textual prompts. This model allows for generating high-quality, photorealistic and artistic, images with a resolution of up to 1024x1024 pixels, supporting a variety of aspect ratios natively to accommodate diverse creative needs.

Advanced Customization and Access:

Beyond the API, developers interested in deeper customization can access BRIA's models directly through Hugging Face. This alternative provides access to the underlying model source code, offering additional features such as Bria 2.3 Fast LoRA and ControlNets: Canny , Depth, and ReColoring. This option is ideal for developers seeking advanced control over the image generation process and those who wish to integrate cutting-edge AI directly into their workflows.

Examples:

prompt: A professional headshot of a CEO

BRIA BASE model 2.3:

prompt: A vaporwave detailed illustration of a street in London in pastel hues, under a Union Jack sky

BRIA BASE model 2.3:

prompt: A portrait of a Beautiful and playful ethereal singer, art deco, fantasy, intricate art deco golden designs, elegant, highly detailed, sharp focus, blurry background, teal and orange shades

BRIA BASE model 2.3:

Description

The /text-to-image/fast Route is optimized for speed, enabling rapid image creation without compromising quality. This model allows for generating high-quality, photorealistic and artistic, images with a resolution of up to 1024x1024 pixels, supporting a variety of aspect ratios natively to accommodate diverse creative needs. Ideal for applications requiring quick turnaround without sacrificing image fidelity.

Advanced Customization and Access:

Beyond the API, developers interested in deeper customization can access BRIA's models directly through Hugging Face. This alternative provides access to the underlying model source code, offering additional features such as ControlNets: Canny , Depth, and ReColoring. This option is ideal for developers seeking advanced control over the image generation process and those who wish to integrate cutting-edge AI directly into their workflows.

An example:

prompt: A portrait of a Beautiful and playful ethereal singer, art deco, fantasy, intricate art deco golden designs, elegant, highly detailed, sharp focus, blurry background, teal and orange shades

BRIA FAST model 2.3:

Description

The /text-to-image/hd Route is tailored for projects demanding the utmost in image detail and clarity. This model allows for generating high-quality, photorealistic and artistic, images with unparalleled resolution of 1920x1080 (1:1 1536x1536) pixel, supporting a variety of aspect ratios natively to accommodate diverse creative needs.

Advanced Customization and Access:

Beyond the API, developers interested in deeper customization can access BRIA's models directly through Hugging Face. This alternative provides access to the underlying model source code, offering additional features such as ControlNets: Canny , Depth, and ReColoring. This option is ideal for developers seeking advanced control over the image generation process and those who wish to integrate cutting-edge AI directly into their workflows.

Examples:

prompt: A photo of detailed short female blond hair viewed from behind, with rich texture and clearly visible individual strands that give depth and realism, and featuring subtle waves reflect light

BRIA HD model 2.2:

prompt: A portrait of a Beautiful and playful ethereal singer, art deco, fantasy, intricate art deco golden designs, elegant, highly detailed, sharp focus, blurry background, teal and orange shades

BRIA HD model 2.2:

Description

The campaign_generator Route enables users to create visual campaigns for various ad channels using a visual_id. This route facilitates the generation of templates featuring diverse layouts that incorporate elements like text, logos, and calls to action (CTA), tailored to various resolutions (placements) and target audiences.

Ad backgrounds are expanded to fit the necessary layout specifications.

Examples

original image (Generated by Bria):

Results:

Description

The Image Expension Route can be used to expand an image and its background, by utilizing generative AI.

You can decide on the image size of the final result as well as the position and size of the original image compared to the final result.

In this way, you can create unique variations of your original image instead of cropping it into different aspect ratios and losing important details.

Optimal input range

Input Image Area: Ensure that the input image area is more than 15% of the canvas area to achieve optimal results.

Canvas Size: The canvas size should be up to an area of 5000x5000 pixels. Here are some examples:

original image (Generated by Bria):

results:

original image (Stock Image):

results:

You can use this route on a modified image by providing the sid from the response of the previously used route.

This route returns both the URL and the sid associated with the updated image.

Description

The Isolated Increase Resolution Route is used to upsacle the resolution of any image.

The main difference between the regular and this isolated Increase Resolution endpoint is that you can use this endpoint without uploading or registering the image to Bria.

Constraints

The Bria API currently supports only JPEG and PNG files in RGB, RGBA, or CMYK color modes. When the file is of a different type or color mode, the status code 415 will be returned.

It's possible to increase the resolution of an image up to a total area of 8192x8192 pixels.

Increase the Resolution of an image using the image file

In our documentation request example, we demonstrate how to Increase the Resolutio of an image using the image URL.

If you want to use an image from a file, follow the example below:

  import requests

  url = "https://engine.prod.bria-api.com/v1/image/increase_resolution?desired_increase=2"

  payload = {}
  files=[
    ('file',('image_name.jpeg',open('/path_to_local_file/image_name.jpeg','rb'),'image/jpeg'))
  ]
  headers = {
    'api_token': 'xxxx'
  }

  response = requests.request("POST", url, headers=headers, data=payload, files=files)

  print(response.text)
            

Description

The Increase Resolution Route is used to upsacle the resolution of any image.

You can also use this route on a modified image by providing the sid from the response of the previously used route.

Description

The Image to PSD Route is used to create a layered PSD file from any image.

The image is divided into different layers (depending on the image): a background layer with all identified objects removed, a foreground layer without the background, and a layer for each object.

You can also use this route on a modified image by providing the sid from the response of the previously used route.

Description

The Crop Route is used to crop an image without a background tightly around the remaining region of interest.

You can also use this route on a modified image by providing the sid from the response of the previously used route. You can utilize Bria's /background/remove endpoint and then use the result of that route in the /crop route.

This route returns both the URL and the sid associated with the updated image.

On images without transparent backgrounds, the response will be the provided image at the selected resolution.

Description

This route allows you to generate images using a tailored model. Tailored models are trained using your own datasets to create unique visual styles, character illustrations, or icons. You can train a tailored model through the API or using the Bria platform.

Overview of Tailored Model API

The Tailored Model API provides a set of endpoints to manage the entire lifecycle of a tailored model:

1. Dataset Management: Create and manage datasets which will be used to train your tailored models. You can:

   • Create and Retrieve Datasets: Use the /datasets endpoint to create a new dataset or retrieve existing datasets that belong to the client organization.
   • Upload and Retrieve Images: Use the /datasets/{dataset_id}/images endpoint to upload a new image to a dataset or retrieve existing images that belong to the dataset.
   • Manage Individual Images: Use the /datasets/{dataset_id}/images/{image_id} endpoint to get or delete a specific image from a dataset.

2. Model Management: Create and manage models based on your datasets. You can:

   • Create and Retrieve Models: Use the /models endpoint to create a new model or retrieve all models that belong to the client organization.
   • Get and Delete Specific Models: Use the /models/{id} endpoint to retrieve a specific model by its ID or delete a specific model.

3. Training Management: Start and stop training jobs for your models. You can:

   • Start Training: Use the /models/{id}/start_training endpoint to initiate the training process. Note that the dataset must include the minimum required number of images and have a status of 'completed' before training can start.
   • Stop Training: Use the /models/{id}/stop_training endpoint to stop a training job for a model.

Training a Tailored Model

To train a tailored model via the API:

1. Create a Dataset: Use the /datasets endpoint to create a dataset.
2. Upload Images: Upload your images to this dataset using the /datasets/{dataset_id}/images endpoint.
3. Create a Model: Use the /models endpoint to create a model associated with your dataset.
4. Complete the Dataset: Ensure the dataset meets the minimum image requirements and change its status to 'completed'.
5. Start Training: Initiate the training process using the /models/{id}/start_training endpoint. Training of a tailored model takes between 1-3 hours.
6. Monitor Training: Optionally, you can check the training status to monitor progress.
7. Generate Images: Once the model is trained, use this route to generate images.

Alternatively, you can use the Bria platform to manage and train your tailored models through a user-friendly interface. Access the Bria platform here.

Description This endpoint allows the creation of a new dataset that will belong to the organization associated with the used API token. The dataset will be created with a default status of "draft".

Description Retrieve a list of datasets that belong to the organization associated with the used API token. If no datasets are found, an empty array is returned.

Description Retrieve a specific dataset by its ID. The response includes all the details of the dataset and an array of associated images.

Description Update the details of a specific dataset by its ID. If the status of the dataset is completed, the style_description and use_case cannot be updated. Additionally, if the status is completed, it cannot be returned to draft if a model has already been trained based on it.

Description Delete a specific dataset. Changes status to deleted.

Description Upload a new image to a specific dataset. One of the following parameters, file or image_url, must be provided.

Upload a new image using the image file

In our documentation request example, we demonstrate how to upload a new image using the image URL.

If you want to use an image from a file, follow the example below:

  import requests

  url = "/tailored-gen/datasets/{dataset_id}/images"

  payload = {}
  files=[
    ('file',('image_name.jpeg',open('/path_to_local_file/image_name.jpeg','rb'),'image/jpeg'))
  ]
  headers = {
    'api_token': 'xxxx'
  }

  response = requests.request("POST", url, headers=headers, data=payload, files=files)

  print(response.text)

Description Retrieve a list of images in a specific dataset.

Description Retrieve a specific image by its ID.

Description Delete a specific image.

Description Create a new model.

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

Description Retrieve a specific model by its ID.

Description Delete a specific model.

Description Start a new training job for a specific model. Training of a tailored model takes between 1-3 hours.

Description Stop a specific training job for a model.

This capability allows you to create a precise cutout of a product from any given image. This feature is especially valuable for e-commerce platforms and applications, serving as a fundamental building block for crafting a user-friendly interface.

The following interactive Colab notebook demonstrates how to use this API effectively. Explore the example code and try it out for yourself to fully appreciate the potential of this suite.

The Product Pack Shot feature is designed to create professional standard pack shots. The output is a 2000x2000 px image, with the product size and location according to best practices. This feature can allow users to take any photo of a product and transform it into a professional pack shot, placing the product on a clean, seamless background, typically white but customizable to any color.

The following interactive Colab notebook demonstrates how to use this API effectively. Explore the example code and try it out for yourself to fully appreciate the potential of this suite.