I work at NSFW Coders, and I’m currently involved in developing the Candy AI Clone API, which integrates conversational AI with image generation capabilities. The main idea is to allow the chatbot to understand user prompts and create relevant visual responses in real time.
We’ve been experimenting with different fine-tuning approaches and context-retention models to make responses more coherent and emotionally adaptive. However, combining image generation with NLP-based conversation logic introduces a few challenges, such as:
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Maintaining consistent context across multiple message turns
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Managing token and image data in a unified pipeline
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Optimizing GPU usage to reduce generation time
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Balancing response quality with real-time performance
I’m curious if anyone here has worked on similar multi-modal AI integrations — where both text and visuals are generated within a single API flow. How do you typically structure your model layers or manage latency in such setups?