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1
Intro
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Overview of SDXL 1.0 and SD 1.5 models
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Dataset Overview
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Short explanation about my ComfyUI node setup
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WAS Node Suite text concatenation
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CLIP G and CLIP L
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CLIPTextEncodeSDXL
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Naive local finetuning with Adafactor
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How to fit finetuning settings into 24 gb VRAM consumer GPU
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Local finetune with Adafactor settings
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Min SNR Gamma paper
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Installing local Tensorboard to view event logs
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Runpod overview
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How much Runpod costs
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Runpod finetune settings
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Weights and Biases overview
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Determining the initial learning rate for AdamW finetune
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Adding a sample prompt to training settings to visually gauge training progress
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Checking AdamW finetune sample images
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Efficiency nodes for XY plot
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How to retrieve your models from Runpod
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Evaluating finetune XY plot
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D-Adaptation overview
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D-Adaptation training settings
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Decoupled Weight Decay Regularization paper
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What does weight decay do?
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Betas and Growth Rate
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drhead's choice of hyperparameters
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LoRA network dimensions and alpha
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Tensorboard analysis for D-Adaptation LoRA
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D-Adaptation sample images analysis
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Prodigy repository
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Prodigy training settings
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How to enable cosine annealing
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Prodigy training settings version 2
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Prodigy code deep dive
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Why I didn't use any warmup for Prodigy training settings
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Weights and Biases analysis for Prodigy
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Prodigy sample images analysis
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Prodigy XY plot
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Prodigy AdamW and Higher Weight Decay analysis
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Prodigy final version XY plot
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Closing thoughts
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CivitAI SDXL Competition
Description:
Dive into an in-depth tutorial on training SDXL 1.0 using various techniques including finetuning, LoRA, D-Adaptation, and Prodigy. Explore the differences between SDXL 1.0 and SD 1.5 models, learn about dataset preparation, and master ComfyUI node setup. Discover how to implement local finetuning with Adafactor, utilize Runpod for cloud-based training, and leverage Weights and Biases for progress tracking. Gain insights into advanced concepts like Min SNR Gamma, Decoupled Weight Decay Regularization, and optimal hyperparameter selection. Compare different training approaches, analyze sample images, and create XY plots to evaluate results. Perfect for those looking to enhance their Stable Diffusion skills and participate in competitions like CivitAI SDXL.

Stable Diffusion- Training SDXL 1.0 - Finetune, LoRA, D-Adaptation, Prodigy

kasukanra
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#Computer Science #Deep Learning #Stable Diffusion #Machine Learning #LoRA (Low-Rank Adaptation) #Programming #Cloud Computing #RunPod #Artificial Intelligence #Computer Vision #AI Image Generation #ComfyUI
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Intro