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Introduction to Fine-tuning Diffusion Models
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Video Overview
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Flux Schnell and Flux Dev Overview
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Picking a GPU for fine-tuning Flux
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Fine-tuning notebooks for diffusion models
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Installation
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Choosing photos for a training dataset
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Running inference before fine-tuning generating images
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Tips for running in Google Colab
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Running fine-tuning of Flux Schnell using LoRA
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Setting up tensorboard logging
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Inspecting the training results
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Generating images with your LoRA adapter
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Explaining how diffusion models like FLUX work
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Basic diffusion models
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Diffusion in “latent space”
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How Variational Autoencoders work
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FLUX model architecture - putting it all together CLIP, T5, transformer, VAE
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Diffusion steps, Model size, Noise Removal Approaches Flow, Guided generation
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Video Resources trelis.com/ADVANCED-vision
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Learn how to fine-tune a diffusion model using your own photos in this comprehensive 52-minute video tutorial. Explore the Flux Schnell model, select appropriate GPUs, and navigate through the fine-tuning process using LoRA. Discover tips for running the model in Google Colab, setting up tensorboard logging, and generating images with your custom LoRA adapter. Gain insights into the inner workings of diffusion models, including basic concepts, latent space diffusion, and Variational Autoencoders. Delve into the FLUX model architecture, understanding components like CLIP, T5, transformers, and VAE. Examine diffusion steps, model size considerations, and noise removal approaches. Access additional resources and support through provided links to enhance your learning experience.

Fine-Tuning a Diffusion Model with Your Photos

Trelis Research
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#Computer Science #Machine Learning #Diffusion Models #Deep Learning #Stable Diffusion #LoRA (Low-Rank Adaptation) #Transformers #Variational Autoencoders #T5 #Fine-Tuning #Latent Space
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Introduction to Fine-tuning Diffusion Models