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Coursera

AI Image Generation in Python: Stable Diffusion & LoRA

Board Infinity via Coursera

Overview

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This compact, project-driven course teaches learners to build an end-to-end AI image generation application, from running a Stable Diffusion model locally to deploying a production-ready image generation API. The project progresses from basic text-to-image generation to a full pipeline with prompt engineering, fine-tuning, image control, and a deployable web app. AI image generation is transforming creative workflows, and building production apps on top of it is a high-demand skill. By course end, learners will have a working Stable Diffusion pipeline, a custom fine-tuned model, and a deployed generation API plus the knowledge to understand how commercial tools (like Midjourney, DALL·E, Leonardo.ai) work under the hood Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.

Syllabus

  • Building the Stable Diffusion Generation Engine
    • Understand how diffusion models turn noise into images and why they outperform earlier GANs, generate and control images with Stable Diffusion, and master prompt engineering plus image-to-image editing workflows
  • Fine-Tuning, Optimization & Production Deployment
    • Fine-tune Stable Diffusion on custom subjects and styles, optimize the pipeline for speed and cost, and deploy the image generator as a usable web app and API.
  • Deployment, Evaluation & Production Integration
    • Wrapping the pipeline in a Gradio UI (prompt box, parameter sliders, gallery output), exposing negative prompts, seed, and ControlNet controls to users, session handling and queueing for multiple requests, quick prototype-to-shareable-demo workflow.

Taught by

Board Infinity

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