This update brings significant enhancements to FANA LLM image generation capabilities, including 54 new NLP triggers, modularization of key functionalities, and improved image analysis and synthesis.
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Enhanced Data Repository: We onboarded to the Aimagine Creator Tool database, expanding their data corpus and enhancing accuracy.
Knowledge Graph Expansion: We have successfully ingested the AImagine FAQ dataset into the Creator Tool database, augmenting the knowledge graph with a wealth of new information and enhancing informative responses.
Image generation NLP has been augmented with an additional set of 54 triggers, bringing the total to 176. This update leverages the power of natural language processing (NLP) to enable users to generate images that are more diverse, creative, and contextually relevant.
Some of the newly added NLP triggers include:
Entities and characters: "human", "buttock", "humanoid", "body", "detective", "superhero", "zombie", "pirate"
Locations and landscapes: "paris", "boulevard", "beach", "ocean", "desert", "jungle"
Objects and vehicles: "croissant", "skateboarding", "drone", "exoskeleton", "sports car", "sailing ship"
Art and style: "comic style", "anime", "manga", "expressionism", "impressionism", "cubism", "pop art"
Concepts and themes: "cyberpunk", "steampunk", "post-apocalyptic", "medieval", "renaissance", "industrial age", "Victorian", "Nordic", "Mayan"
The generate_image module has been updated to leverage the analyze_image module's computer vision capabilities to craft a prompt that explains how to generate an image similar to the requested one, utilizing natural language processing (NLP) techniques to ensure semantic coherence.
The generate_image module has been modified to send the generated image to the analyze_image module, which performs feature extraction using convolutional neural networks (CNNs), and then returns both the image HTML (rendered using a templating engine) and the analysis result (in JSON format), providing a more comprehensive and informative experience for the user.
We have modularized key functionalities of , including llm_client.py, generate.py, embeddings.py and analyze.py, using a service-oriented architecture (SOA) to promote scalability, maintainability, and reusability.
Integrated metadata to improve tracking and debugging.
