Bria | Product Shot
Place any product into any scene with a prompt or reference image, keeping product details intact. Built on licensed data for safe, risk-free commercial use and optimized for eCommerce.
Avg Run Time: 20.000s
Model Slug: bria-product-shot
Category: Image to Image
Input
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(Max 50MB)
Enter an URL or choose a file from your computer.
Click to upload or drag and drop
(Max 50MB)
Output
Example Result
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Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
Get Prediction Result
Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.
Overview
Bria-product-shot is an advanced image generation model designed specifically for eCommerce and product photography applications. Developed by Bria AI, the model enables users to place any product into any scene using either a text prompt or a reference image, while maintaining the integrity and details of the product itself. This capability is particularly valuable for businesses seeking to create high-quality, contextually relevant product images at scale without the need for manual photography or extensive post-processing.
The model is built on licensed data, ensuring that all outputs are safe for commercial use and free from copyright risks. Bria-product-shot leverages generative AI techniques optimized for photorealistic product placement, background replacement, and scene composition. Its unique value lies in its ability to preserve product details and consistency across different scenes, making it ideal for catalog generation, marketing materials, and dynamic online storefronts.
Bria-product-shot stands out due to its focus on ethical AI practices, robust data licensing, and specialized optimization for eCommerce workflows. The model supports seamless integration into automated pipelines and offers high adaptability for various product categories and visual styles.
Technical Specifications
- Architecture: Custom generative AI model, likely based on diffusion or transformer-based architectures tailored for image synthesis and editing
- Parameters: Not publicly specified; optimized for commercial-grade image generation
- Resolution: Supports high-resolution outputs suitable for eCommerce (commonly up to 4K for product shots)
- Input/Output formats: Accepts image files (PNG, JPEG, Base64), text prompts, and reference images; outputs in standard image formats (PNG, JPEG)
- Performance metrics: Prioritizes photorealism, product detail preservation, and background integration; asynchronous processing supported for scalability
Key Considerations
- Ensure input images are high-resolution and well-lit for optimal product detail retention
- Use clear, descriptive prompts or high-quality reference images to guide scene composition
- Test multiple product types and backgrounds to understand model strengths and edge cases
- Balance quality and speed: higher resolution and complex scenes may increase processing time
- Avoid overly abstract or ambiguous prompts, which can reduce output accuracy
- For best results, iterate on prompt wording and reference selection to refine outputs
Tips & Tricks
- Start with simple, direct prompts describing the desired scene and product placement
- Use reference images that closely match the intended context for more consistent results
- Adjust prompt specificity to control background complexity and style (e.g., "modern kitchen" vs. "bright kitchen with marble countertops")
- For catalog consistency, reuse reference backgrounds and product angles across batches
- Apply iterative refinement: generate initial outputs, review, and adjust prompts or references as needed
- Leverage built-in content moderation for images from unknown sources to ensure safe outputs
- Experiment with different lighting and contrast in input images to maximize product visibility
Capabilities
- Places products into diverse scenes while preserving product details and realism
- Supports both prompt-driven and reference-based image generation
- Excels at background replacement and contextual product placement for eCommerce
- Maintains consistency in product appearance across multiple images
- Handles a wide range of product categories and visual styles
- Enables automated, scalable image generation for catalogs and marketing assets
- Integrates with existing image editing workflows via API endpoints
What Can I Use It For?
- Automated product catalog generation for online stores
- Dynamic marketing asset creation for ads and promotional materials
- Personalized product imagery for targeted campaigns
- Background replacement and enhancement for professional product photography
- Creative scene composition for social media and branding
- Batch processing of product images for large-scale eCommerce operations
- Industry-specific applications such as fashion, electronics, home goods, and cosmetics
Things to Be Aware Of
- Some experimental features may behave unpredictably in highly complex or abstract scenes
- Users report best results with clear product images and well-defined backgrounds
- Processing time may increase with higher resolution outputs or intricate scene compositions
- Resource requirements scale with batch size and image complexity; asynchronous processing recommended for large workloads
- Consistency in product appearance is a noted strength, especially for catalog applications
- Positive feedback centers on photorealism, detail preservation, and ease of integration
- Occasional concerns include limitations in handling unusual product shapes or ambiguous prompts
- Community discussions highlight the importance of prompt engineering and reference selection for optimal results
Limitations
- May struggle with highly abstract prompts or scenes lacking clear context
- Not optimal for non-product-centric image generation or artistic styles outside eCommerce
- Limited public information on model architecture and parameter count restricts deep technical analysis
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