{"id":1711,"date":"2026-07-29T09:41:27","date_gmt":"2026-07-29T14:41:27","guid":{"rendered":"https:\/\/portfolio.juandiegourdaneta.com\/blog\/pipeline-contenido-ia\/"},"modified":"2026-10-20T20:57:52","modified_gmt":"2026-10-21T01:57:52","slug":"pipeline-contenido-ia","status":"publish","type":"post","link":"https:\/\/juandiegourdaneta.com\/en\/proyecto\/pipeline-contenido-ia\/","title":{"rendered":"AI Content Generation Pipeline"},"content":{"rendered":"

An image and video lab using ComfyUI, Windows with an NVIDIA GPU and Mac with Apple Silicon.<\/p>\n

I configured the local environments and workflows for generation, editing, background removal and upscaling. I use models such as SDXL and Juggernaut XL, alongside cloud services as needed.<\/p>\n

I keep prompts, references, parameters and files from each production to review results and resume work. Visual references help maintain character continuity across images.<\/p>\n

The lab is used to prepare material for campaigns, courses and product demonstrations.<\/p>\n

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ComfyUI, image models and Python organization<\/h2>\n

ComfyUI organizes generation through node-based workflows. SDXL and Juggernaut XL are among the models used, with local execution in compatible environments and external services depending on the production. Python complements work preparation and organization; not every model runs the same way on Mac and Windows.<\/p>\n

What this development contributes<\/h2>\n

Saving references, prompts and parameters makes it possible to revisit an image and understand how it was produced. Final selection requires visual review and suitability for use: campaign artwork has different requirements from interface assets. This is generative AI integration in production, not training a proprietary foundation model.<\/p>\n

Related projects<\/h2>\n