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#stablediffusion #aiart #python #generativeai #diffusion #pytorch I am Dr. Balyogi Mohan Dash, and I welcome you to Intelligent Machines! In This video, we will see how image-to-image ai diffusion works using the Hugging Face Diffusers library. You will learn how to generate realistic image variations by controlling the denoising strength, understand how noise levels affect image coherence, and see practical code examples for adding and removing noise step by step. This tutorial is ideal for anyone exploring diffusion models, AI-based image editing, or generative modeling with Diffusers. How AI Image Generators Work. All code used in this video is available here: https://github.com/mohan696matlab/Diffusion_Gen_AI_Course Subscribe to the channel to follow this complete course and master diffusion models from theory to practical implementation. 🔗Links🔗 LinkedIn: https://www.linkedin.com/in/balyogi-mohan-dash/ GitHub: https://github.com/mohan696matlab Google Scholar: https://scholar.google.com/citations?user=jzcIElIAAAAJ&hl=en WhatsApp inquiries are currently closed. Please reach out via email for any questions: [email protected] 00:00 Introduction – What is Image-to-Image Diffusion? 00:32 Concept – How AI Generates Image Variations 01:15 - Example – Neon Green Hair and Noise Addition 02:20 - Key Idea – Understanding Denoising Strength 03:20 - Implementation – Coding Image-to-Image Diffusion 04:40 - Results – Comparing Variations at Different Noise Levels 05:50 - Next Steps – Latent Diffusion and Conclusion
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