This is a simplified guide to an AI model called fibo-edit/edit/structured_instruction maintained by bria. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.
Model overview
fibo-edit/edit/structured_instruction is Bria's structured instructions generation endpoint designed for the Fibo Edit model. It works by converting natural language editing requests into structured formats that guide precise image modifications. This approach differs from fibo-edit/edit, which combines JSON, masks, and images directly, and complements fibo-edit/rewrite_text for text-specific modifications within images.
Capabilities
The model transforms user intent into structured editing instructions that the Fibo Edit system can interpret and execute. This enables consistent, reproducible image edits where the instructions themselves become part of the editing pipeline. The structured format creates transparency in what changes will occur, reducing ambiguity between user expectations and actual results.
What can I use it for?
This model serves applications requiring programmatic control over image editing workflows. Content teams can use it to generate standardized editing instructions across batches of images. Marketing platforms can build instruction templates for common edits, while design tools can provide users with generated editing guidance. The structured output format integrates into pipelines where consistency and auditability matter, from e-commerce product photo standardization to automated content moderation workflows.
The endpoint works within Bria's editing ecosystem. For generation tasks, fibo/generate/structured_prompt and fibo-lite/generate/structured_prompt provide complementary structured outputs in different model sizes. Learn more about Bria's work through their creator profile.
Things to try
Test the model by generating instructions for both simple and complex edits—compare the structured output for straightforward changes like color adjustment against intricate modifications like object repositioning. Experiment with editing the same image multiple times using different instruction sets to understand how the structured format handles competing or sequential modifications. This reveals how the model prioritizes and sequences instructions when handling layered editing requests.
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