Image Generation

Description The /text-to-image/base Route empowers users to create stunning images directly from textual prompts. This pipeline 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.

Examples:

prompt: A professional headshot of a CEO

Guidance Methods

This API supports various guidance methods to provide greater control over text-to-image generation. These methods condition the model on additional inputs derived from user-provided images, allowing for more precise and controlled image generation.

Importantly, users can provide any image they want as input. Our pipeline automatically transforms the input image according to the selected guidance method. For example, if you choose the depth guidance method, our pipeline will extract a depth map from your provided image and use it during the generation inference.

Currently, we support the following guidance methods:

1. controlnet_canny: Extracts edge information from the input image to guide generation based on structural outlines.
2. controlnet_depth: Derives depth information from the input image to influence spatial arrangement in generated images.
3. controlnet_recoloring: Uses grayscale version of the input image to guide recoloring while preserving geometry.
4. controlnet_color_grid: Extracts a 16x16 color grid from the input image to guide the overall color scheme of generated images.

Using Guidance Methods

You can specify up to four guidance methods in a single request. Each method requires an accompanying image and a scale parameter to determine its impact on the generation inference. The table below provides detailed information about each guidance method, with an example os use:

Guidance Method	Prompt	Scale	Input Image	Guidance Image	Output Image
ControlNet Canny	An exotic colorful shell on the beach	1.0
ControlNet Depth	A dog, exploring an alien planet	0.8
ControlNet Recoloring	A vibrant photo of a woman	1.00
ControlNet Color Grid	A dynamic fantasy illustration of an erupting volcano	0.7

Note: When using multiple guidance methods, all input images must have the same aspect ratio, which will determine the aspect ratio of the generated results. To use a guidance method, include the following parameters in your request:

• guidance_method_X: Specify the guidance method (where X is 1, 2). If the paramter guidance_method_2 is used, so does guidance_method_1 has to be used, and so on. If you would like to use only one method, use the paratmer guidance_method_1
• guidance_method_X_scale: Set the impact of the guidance (0.0 to 1.0)
• guidance_method_X_image_file: Provide the base64-encoded input image

By leveraging these guidance methods, you can achieve more precise control over the generated images, tailoring them to your specific needs and creative vision.

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:

Guidance Methods

This API supports various guidance methods to provide greater control over text-to-image generation. These methods condition the model on additional inputs derived from user-provided images, allowing for more precise and controlled image generation.

Importantly, users can provide any image they want as input. Our pipeline automatically transforms the input image according to the selected guidance method. For example, if you choose the depth guidance method, our pipeline will extract a depth map from your provided image and use it during the generation inference.

Currently, we support the following guidance methods:

1. controlnet_canny: Extracts edge information from the input image to guide generation based on structural outlines.
2. controlnet_depth: Derives depth information from the input image to influence spatial arrangement in generated images.
3. controlnet_recoloring: Uses grayscale version of the input image to guide recoloring while preserving geometry.
4. controlnet_color_grid: Extracts a 16x16 color grid from the input image to guide the overall color scheme of generated images.

Using Guidance Methods

You can specify up to four guidance methods in a single request. Each method requires an accompanying image and a scale parameter to determine its impact on the generation inference. The table below provides detailed information about each guidance method, with an example os use:

Guidance Method	Prompt	Scale	Input Image	Guidance Image	Output Image
ControlNet Canny	An exotic colorful shell on the beach	1.0
ControlNet Depth	A dog, exploring an alien planet	0.8
ControlNet Recoloring	A vibrant photo of a woman	1.00
ControlNet Color Grid	A dynamic fantasy illustration of an erupting volcano	0.7

Note: When using multiple guidance methods, all input images must have the same aspect ratio, which will determine the aspect ratio of the generated results. To use a guidance method, include the following parameters in your request:

• guidance_method_X: Specify the guidance method (where X is 1, 2). If the paramter guidance_method_2 is used, so does guidance_method_1 has to be used, and so on. If you would like to use only one method, use the paratmer guidance_method_1
• guidance_method_X_scale: Set the impact of the guidance (0.0 to 1.0)
• guidance_method_X_image_file: Provide the base64-encoded input image

By leveraging these guidance methods, you can achieve more precise control over the generated images, tailoring them to your specific needs and creative vision.

Description

The /text-to-image/hd Route is dedicated 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 /reimagine endpoint in Bria’s API allows guiding image generation not just with prompts but also by using an input image. This feature retains the original structure and depth of the input while incorporating new materials, colors, and textures to create fresh visuals.

Key Benefits

• Simplified Structure Guidance: Use a reference image to replicate its outline and depth, reducing the need for complex prompts and minimizing trial and error.

• Versatile Input/Output Pairings:

  • Convert illustrations, sketches, or photos into new illustrative outputs.
  • Transform photos into variations that maintain the original layout.

• Adjustable Structure Influence: Control how much the input image's structure impacts the output on a scale from 0 to 1, allowing for diverse creative results.

• Aspect Ratio Preservation: Ensures the output maintains the reference image's aspect ratio for layout consistency. The output resolution is approximately 1 megapixel.

• Seamless Integration with Tailored Generation: Combine structural references with tailored endpoints to include unique IP characteristics in the generated outputs.

  Potential Use Cases

Enhanced Creative Control for Platforms & Editing Tools

Empower creative platforms and editing tools with advanced levels of control and flexibility for generating visual content.

• Maintain Spatial Consistency

  Structure reference image

  

  Generated Visual (combined into a gif)

  

• Convert Sketches to Illustrations

  Structure reference image

  

  prompt: A watercolor painting of a lively urban street featuring a red vintage car parked in front of multi-story buildings, where soft, fluid brushstrokes capture the subtle gradients in the building facades, with warm earth tones blending into cool blues and grays for the shadows, giving the scene a nostalgic and dreamy atmosphere.

  structure_ref_influence: 0.75

  Generated Visual

  

• Generate Diverse Variations

  Structure reference image

  

  prompt: A ginger kitten sits on a textured beige surface, surrounded by soft balls of yarn.

  structure_ref_influence: 0.75

  Generated Visual

  

• Stylize Typography and Logos

  Structure reference image

  

  prompt: curled orange peel.

  structure_ref_influence: 0.1

  Generated Visual

  

Interoperability with Tailored Generation

• Reskin Gaming Assets: Maintain the structure and detail of assets while updating textures and colors for fresh looks without altering the original shape or layout.

• Iterate on Gaming Asset Designs: Simplifies design iteration for gaming assets, enabling rapid exploration and refinement.

• Customize Marketing Assets: Transform marketing visuals while preserving their composition, adding new styles and elements with structural guidance.

• Adapt User-Generated Content: Repurpose user-generated content for marketing campaigns, making it fit seamlessly with brand aesthetics.

For practical tips, view our guide here.

Description

The /prompt_enhancer route is designed to boost users' creativity by transforming simple prompts into more detailed and vivid descriptions. This helps generate richer, more diverse images. (It is also available as a built-in flag in all of our /text-to-image routes, excluding tailored generation.)

We recommend using this feature by offering users a range of prompts to choose from before generating an image, enabling them to explore creative ideas.

*Works best with short to medium prompts of up to approximately 50 words.

Examples:

original prompt: A cat

enhanced prompt: A black and white photograph of a sophisticated Siamese cat, sitting in a chair next to a large window, with the urban cityscape visible in the background

Description

The /text-to-vector/base route offers the ability to generate high-quality, editable vector graphic assets from textual prompts at scale. This endpoint allows for generating quality vector graphics such as icons, logos, and other illustrations.

Examples:

prompt: A sticker of a cute kitten

prompt: A beautiful butterfly

On the left, a generated vector illustration. On the right, the same illustration after being re-colored in a vector editor

Guidance Methods

This API supports various guidance methods to provide greater control over text-to-image generation. These methods condition the model on additional inputs derived from user-provided images, allowing for more precise and controlled image generation.

Importantly, users can provide any image they want as input. Our pipeline automatically transforms the input image according to the selected guidance method. For example, if you choose the depth guidance method, our pipeline will extract a depth map from your provided image and use it during the generation inference.

Currently, we support the following guidance methods:

1. controlnet_canny: Extracts edge information from the input image to guide generation based on structural outlines.
2. controlnet_depth: Derives depth information from the input image to influence spatial arrangement in generated images.
3. controlnet_recoloring: Uses grayscale version of the input image to guide recoloring while preserving geometry.
4. controlnet_color_grid: Extracts a 16x16 color grid from the input image to guide the overall color scheme of generated images.

Using Guidance Methods You can specify up to four guidance methods in a single request. Each method requires an accompanying image and a scale parameter to determine its impact on the generation inference. Note: When using multiple guidance methods, all input images must have the same aspect ratio, which will determine the aspect ratio of the generated results. To use a guidance method, include the following parameters in your request:

• guidance_method_X: Specify the guidance method (where X is 1, 2). If the paramter guidance_method_2 is used, so does guidance_method_1 has to be used, and so on. If you would like to use only one method, use the paratmer guidance_method_1
• guidance_method_X_scale: Set the impact of the guidance (0.0 to 1.0)
• guidance_method_X_image_file: Provide the base64-encoded input image

By leveraging these guidance methods, you can achieve more precise control over the generated images, tailoring them to your specific needs and creative vision.

Description

The /text-to-vector/fast route is optimized for speed, enabling rapid vector graphics creation without compromising quality. This endpoint allows for generating high-quality vector graphics such as icons, logos, and other illustrations.

Examples:

prompt: An icon of a bird with a blue head and yellow beak against a solid background

Guidance Methods

This API supports various guidance methods to provide greater control over text-to-image generation. These methods condition the model on additional inputs derived from user-provided images, allowing for more precise and controlled image generation.

Importantly, users can provide any image they want as input. Our pipeline automatically transforms the input image according to the selected guidance method. For example, if you choose the depth guidance method, our pipeline will extract a depth map from your provided image and use it during the generation inference.

Currently, we support the following guidance methods:

1. controlnet_canny: Extracts edge information from the input image to guide generation based on structural outlines.
2. controlnet_depth: Derives depth information from the input image to influence spatial arrangement in generated images.
3. controlnet_recoloring: Uses grayscale version of the input image to guide recoloring while preserving geometry.
4. controlnet_color_grid: Extracts a 16x16 color grid from the input image to guide the overall color scheme of generated images.

Using Guidance Methods

You can specify up to four guidance methods in a single request. Each method requires an accompanying image and a scale parameter to determine its impact on the generation inference. Note: When using multiple guidance methods, all input images must have the same aspect ratio, which will determine the aspect ratio of the generated results. To use a guidance method, include the following parameters in your request:

• guidance_method_X: Specify the guidance method (where X is 1, 2). If the paramter guidance_method_2 is used, so does guidance_method_1 has to be used, and so on. If you would like to use only one method, use the paratmer guidance_method_1
• guidance_method_X_scale: Set the impact of the guidance (0.0 to 1.0)
• guidance_method_X_image_file: Provide the base64-encoded input image

By leveraging these guidance methods, you can achieve more precise control over the generated images, tailoring them to your specific needs and creative vision.